RNNs.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FCPfiLyUo0VT"
      },
      "source": [
        "# RNN implementation for dietary classification\n",
        "In the following we'll try some RNN's implementations, starting from the BiLSTM architecture and following using Attention-based BiLSTM."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mb0ZCPZvo0VX"
      },
      "source": [
        "## Colab setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "3bddb248-efaa-4115-9733-65feac4f2bfc",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k42K6731hJVi",
        "outputId": "d11d18c8-d167-41a5-d95e-0e6a7ee85981"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/datasets/distilbert_results\n"
          ]
        }
      ],
      "source": [
        "%cd /content/drive/MyDrive/datasets/distilbert_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting keras-tuner\n",
            "  Downloading keras_tuner-1.4.7-py3-none-any.whl (129 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.1/129.1 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (from keras-tuner) (2.15.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from keras-tuner) (24.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from keras-tuner) (2.31.0)\n",
            "Collecting kt-legacy (from keras-tuner)\n",
            "  Downloading kt_legacy-1.0.5-py3-none-any.whl (9.6 kB)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->keras-tuner) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->keras-tuner) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->keras-tuner) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->keras-tuner) (2024.2.2)\n",
            "Installing collected packages: kt-legacy, keras-tuner\n",
            "Successfully installed keras-tuner-1.4.7 kt-legacy-1.0.5\n"
          ]
        }
      ],
      "source": [
        "!pip install keras-tuner --upgrade"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tofUKATBo0VZ"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-05-27T10:05:59.940475Z",
          "iopub.status.busy": "2024-05-27T10:05:59.940102Z",
          "iopub.status.idle": "2024-05-27T10:06:05.458739Z",
          "shell.execute_reply": "2024-05-27T10:06:05.457727Z",
          "shell.execute_reply.started": "2024-05-27T10:05:59.940433Z"
        },
        "id": "Ta3L9TekiG2r",
        "outputId": "8abd1bde-fe90-4811-f3f2-f765c47f9595",
        "trusted": true
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import keras_tuner as kt\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.layers import Layer\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense, Embedding, Bidirectional, BatchNormalization, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OsOMdjTFnvj2"
      },
      "source": [
        "## Dataset Loading"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-27T10:06:15.052397Z",
          "iopub.status.busy": "2024-05-27T10:06:15.051725Z",
          "iopub.status.idle": "2024-05-27T10:06:16.323794Z",
          "shell.execute_reply": "2024-05-27T10:06:16.322795Z",
          "shell.execute_reply.started": "2024-05-27T10:06:15.052358Z"
        },
        "id": "hZGhDAj5ZUgH",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "#data = pd.read_csv('./dataset_balanced_10k.csv')\n",
        "data = pd.read_csv('./dataset/dataset_balanced.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "execution": {
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        "outputId": "ed99f49d-dd25-427e-f674-c21336b54373",
        "trusted": true
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      "outputs": [
        {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cooking_method</th>\n",
              "      <th>ingredients</th>\n",
              "      <th>tags</th>\n",
              "      <th>Vegetarian</th>\n",
              "      <th>Dairy Free</th>\n",
              "      <th>Gluten Free</th>\n",
              "      <th>Low Carb</th>\n",
              "      <th>Low Fat</th>\n",
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              "      <th>Dessert</th>\n",
              "      <th>Meat</th>\n",
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              "      <th>Meat&amp;Fish</th>\n",
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              "      <td>['Toast the Tuscan bread on a hot BBQ grill.',...</td>\n",
              "      <td>['4 large slices of Tuscan-style or dense coun...</td>\n",
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              "      <td>['2 cans pear halves, drained', '3 tablespoons...</td>\n",
              "      <td>American,Fruit,Pear Recipes,Pureeing Recipes,G...</td>\n",
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              "      <td>['4 cups all purpose flour', '1 tablespoon sal...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>['To soak beans: In a large bowl, cover beans ...</td>\n",
              "      <td>['1 pound dried small white beans, rinsed and ...</td>\n",
              "      <td>Easy Main Dish,Easy,Main Dish,Bean Soup,Beans ...</td>\n",
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              "                                      cooking_method  \\\n",
              "0  ['Toast the Tuscan bread on a hot BBQ grill.',...   \n",
              "1  ['Preheat the oven to 350 degrees F. Line the ...   \n",
              "2  ['Simmer pear halves and brandy in a saucepan ...   \n",
              "3  ['In a large bowl, mix together the flour, sal...   \n",
              "4  ['To soak beans: In a large bowl, cover beans ...   \n",
              "\n",
              "                                         ingredients  \\\n",
              "0  ['4 large slices of Tuscan-style or dense coun...   \n",
              "1  ['1/2 cup regular olive oil, not extra-virgin,...   \n",
              "2  ['2 cans pear halves, drained', '3 tablespoons...   \n",
              "3  ['4 cups all purpose flour', '1 tablespoon sal...   \n",
              "4  ['1 pound dried small white beans, rinsed and ...   \n",
              "\n",
              "                                                tags  Vegetarian  Dairy Free  \\\n",
              "0                     Italian,Bread,Low Sodium,Vegan           1           0   \n",
              "1  Easy Dessert Recipes,Dessert,Easy,Carrot Cake,...           0           0   \n",
              "2  American,Fruit,Pear Recipes,Pureeing Recipes,G...           1           0   \n",
              "3  Easy Main Dish,Easy,Main Dish,Easy Baking,Roas...           1           0   \n",
              "4  Easy Main Dish,Easy,Main Dish,Bean Soup,Beans ...           0           0   \n",
              "\n",
              "   Gluten Free  Low Carb  Low Fat  Low Sodium  Dessert  Meat  Fish  Dairy  \\\n",
              "0            0         0        0           1        0     0     0      0   \n",
              "1            1         0        0           1        1     0     0      0   \n",
              "2            1         0        1           1        0     0     0      0   \n",
              "3            0         0        0           0        0     0     0      0   \n",
              "4            1         0        0           0        0     1     0      0   \n",
              "\n",
              "   Vegetarian&Dessert  Meat&Fish  \n",
              "0                   1          0  \n",
              "1                   1          0  \n",
              "2                   1          0  \n",
              "3                   1          0  \n",
              "4                   0          1  "
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          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head()"
      ]
    },
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      "execution_count": 9,
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        "trusted": true
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      "outputs": [],
      "source": [
        "recipes = data['cooking_method']\n",
        "labels_binary = data['Vegetarian&Dessert']\n",
        "labels_multi = data[['Vegetarian&Dessert', 'Low Fat', 'Low Sodium', 'Low Carb', 'Gluten Free', 'Dairy Free']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TensorFlow version: 2.16.1\n",
            "Num GPUs Available:  0\n"
          ]
        }
      ],
      "source": [
        "# Check TensorFlow version and GPU availability\n",
        "print(\"TensorFlow version:\", tf.__version__)\n",
        "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))\n",
        "\n",
        "# Get GPU details if available\n",
        "if tf.config.list_physical_devices('GPU'):\n",
        "    gpu_info = tf.config.list_physical_devices('GPU')[0]\n",
        "    print(\"GPU Name:\", gpu_info.name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ts0TIN7xooKo"
      },
      "source": [
        "## Pre processing\n",
        "We first need to tokenize all the recipes in order to feed the RNNs.\n",
        "\n",
        "We'll do all of ours experiments using the entire recipe procedures, rather than just its ingredients"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "execution": {
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        },
        "id": "y-EDn0bisJTV",
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      },
      "outputs": [],
      "source": [
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(recipes)  # Fit the tokenizer on the texts\n",
        "\n",
        "sequences = tokenizer.texts_to_sequences(recipes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "execution": {
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        },
        "id": "KPvd7azQte1V",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "vocab_size = len(tokenizer.word_index) + 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-27T10:07:02.329967Z",
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      "outputs": [],
      "source": [
        "max_length = max(len(x) for x in sequences)\n",
        "recipes_sequences = pad_sequences(sequences, maxlen=max_length, padding='post', dtype='float32', value=0.0)  # Post-padding with zeros"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "(50902, 535)"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "recipes_sequences.shape"
      ]
    },
    {
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      "execution_count": 21,
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        },
        "id": "M2HN9okNpy3w",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(recipes_sequences, labels_binary, test_size=0.3, random_state=42, stratify=labels_binary)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PL_tL0aIn_y5"
      },
      "source": [
        "## Training the rnn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-27T10:07:03.436445Z",
          "iopub.status.busy": "2024-05-27T10:07:03.435807Z",
          "iopub.status.idle": "2024-05-27T10:07:03.440813Z",
          "shell.execute_reply": "2024-05-27T10:07:03.439876Z",
          "shell.execute_reply.started": "2024-05-27T10:07:03.436405Z"
        },
        "id": "wKfQtqBXo0Vk",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "input_length = recipes_sequences.shape[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-05-27T10:07:51.234078Z",
          "iopub.status.busy": "2024-05-27T10:07:51.233752Z",
          "iopub.status.idle": "2024-05-27T10:12:25.726378Z",
          "shell.execute_reply": "2024-05-27T10:12:25.725386Z",
          "shell.execute_reply.started": "2024-05-27T10:07:51.234053Z"
        },
        "id": "iY00vdHs9ydY",
        "outputId": "9cd85bec-cfe7-4605-bae4-fd20fdb69cee",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m891/891\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 37ms/step - accuracy: 0.7521 - loss: 0.5091 - val_accuracy: 0.9388 - val_loss: 0.2211\n",
            "Epoch 2/10\n",
            "\u001b[1m891/891\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 37ms/step - accuracy: 0.9626 - loss: 0.1651 - val_accuracy: 0.9571 - val_loss: 0.1540\n",
            "Epoch 3/10\n",
            "\u001b[1m891/891\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 37ms/step - accuracy: 0.9816 - loss: 0.0966 - val_accuracy: 0.9580 - val_loss: 0.1376\n",
            "Epoch 4/10\n",
            "\u001b[1m891/891\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 37ms/step - accuracy: 0.9852 - loss: 0.0699 - val_accuracy: 0.9554 - val_loss: 0.1413\n",
            "Epoch 5/10\n",
            "\u001b[1m891/891\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 37ms/step - accuracy: 0.9878 - loss: 0.0560 - val_accuracy: 0.9620 - val_loss: 0.1303\n",
            "Epoch 6/10\n",
            "\u001b[1m891/891\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 37ms/step - accuracy: 0.9899 - loss: 0.0444 - val_accuracy: 0.9575 - val_loss: 0.1423\n",
            "Epoch 7/10\n",
            "\u001b[1m891/891\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 37ms/step - accuracy: 0.9910 - loss: 0.0385 - val_accuracy: 0.9559 - val_loss: 0.1494\n",
            "Epoch 8/10\n",
            "\u001b[1m891/891\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 37ms/step - accuracy: 0.9918 - loss: 0.0353 - val_accuracy: 0.9586 - val_loss: 0.1469\n",
            "Epoch 8: early stopping\n",
            "Restoring model weights from the end of the best epoch: 5.\n"
          ]
        }
      ],
      "source": [
        "early_stopping = EarlyStopping(monitor='val_loss',  min_delta=0.001, patience=3, verbose=1, mode='min', restore_best_weights=True)\n",
        "model = Sequential([\n",
        "    Embedding(input_dim=vocab_size, output_dim=512, input_length=input_length),\n",
        "    Bidirectional(LSTM(1)),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "history = model.fit(X_train, y_train, batch_size=32, epochs=10, validation_split=0.2, callbacks=[early_stopping])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-27T10:12:25.729906Z",
          "iopub.status.busy": "2024-05-27T10:12:25.729168Z",
          "iopub.status.idle": "2024-05-27T10:12:26.332380Z",
          "shell.execute_reply": "2024-05-27T10:12:26.331490Z",
          "shell.execute_reply.started": "2024-05-27T10:12:25.729879Z"
        },
        "id": "WTBiDZGEo0Vk",
        "outputId": "c14b994d-a04f-4a4d-c7b5-726044910282",
        "trusted": true
      },
      "outputs": [
        {
          "data": {
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mL730Uo3rERERc6gHqIH5+1jxs1kpcTgptJcRFtg4b3bZsWNHPvjgA4YOHYrFYuHBBx88aU9OfZkwYQLTp0+nQ4cOdOnShWeffZaDBw9WK/ytX7+e0NBQ93OLxUJKSgpXXHEFt9xyCy+++CKhoaHcf//9tGzZkiuuuAKAiRMnMnjwYDp16sTBgwdZsmQJXbt2BWDq1Kn06dOH7t27Y7fb+fjjj92viYhI06EA1MAsFguhgb7kFtrJLy5ttAHon//8JzfeeCNnn3020dHR3HfffeTn5zd4Hffddx+ZmZmMGTMGm83GrbfeSnp6OjbbqYf/zjvvPI/nNpuNsrIy5syZw913380f/vAHSkpKOO+88/j000/dw3EOh4Px48eze/duwsLCGDRoEP/6178A11pGU6ZMYfv27QQGBnLuuecyb968uj9xERGpVxbD7AkXjVB+fj7h4eHk5eURFhbm8VpxcTHbtm2jbdu2BAQE1Oz4xaVszynC12alS3yobn55GpxOJ127duWaa67hkUceMbucelEXP2MiIt7oZH+/j6ceIBOE+PlgtVgodTgpLnUS6Fd3k5mbmx07dvD5559z/vnnY7fbmTVrFtu2beO6664zuzQREWnCNAnaBFarhRB/V/YsKC41uZrGzWq1MnfuXPr168fAgQNZv349X3zxhebdiIhIragHyCShAT7kF5eSX1xG7Ml76bxaUlISy5YtM7sMERFpZtQDZJLQANeE2yMlZZQ5Gv7qKhEREW+mAGQSPx8rAb42DKDAXmZ2OSIiIl5FAchEYQHl84COKACJiIg0JAUgE1UMgxXYS02//YOIiIg3UQAyUZCfDR+rBYfToKjEYXY5IiIiXkMByEQWi+VoL5AuhxcREWkwCkAmC62YB1Tc+OcBXXDBBUycONH9PDk5mZkzZ570PRaLhQULFtT6s+vqOCIiIqAAZLoQfx8sWCgudVBSVj/DYEOHDmXQoEFVvvbNN99gsVj46aefTvu4K1eu5NZbb61teR4eeughevXqVWn/vn37GDx4cJ1+1vHmzp1LixYt6vUzRESkcVAAMpmPzUqQv+tWGPn11At00003sXjxYnbv3l3ptTlz5tC3b1969ux52seNiYkhKCioLko8pfj4ePz9/Rvks0REpPlTAGoE6nsY7A9/+AMxMTHMnTvXY39hYSHvvvsuN910E7m5uYwaNYqWLVsSFBREjx49eOutt0563OOHwH777TfOO+88AgIC6NatG4sXL670nvvuu49OnToRFBREu3btePDBByktdc1/mjt3Lg8//DDr1q3DYrFgsVjcNR8/BLZ+/XouuugiAgMDiYqK4tZbb6WwsND9+g033MCwYcN46qmnSEhIICoqivHjx7s/qyZ27tzJFVdcQUhICGFhYVxzzTVkZWW5X1+3bh0XXnghoaGhhIWF0adPH1atWgW47mk2dOhQIiIiCA4Opnv37nz66ac1rkVERGpHt8KoC4YBpYdr/PYwq4Os0sMUlVlwhBjYrNW8O7xvEFTjTvI+Pj6MGTOGuXPn8sADD7jvPv/uu+/icDgYNWoUhYWF9OnTh/vuu4+wsDA++eQTrr/+etq3b0///v1P+RlOp5OrrrqKuLg4fvjhB/Ly8jzmC1UIDQ1l7ty5JCYmsn79em655RZCQ0O59957GTlyJBs2bGDhwoV88cUXAISHh1c6RlFREenp6QwYMICVK1eSnZ3NzTffzJ133ukR8pYsWUJCQgJLlixhy5YtjBw5kl69enHLLbec8nyqOr+K8PPVV19RVlbG+PHjGTlyJEuXLgVg9OjR9O7dm9mzZ2Oz2Vi7di2+vq5J7uPHj6ekpISvv/6a4OBgfvnlF0JCQk67DhERqRuNIgA999xzPPnkk2RmZpKSksKzzz57wj+6H3zwAY8//jhbtmyhtLSUjh07cs8993D99de729xwww28+uqrHu9LT09n4cKF9XMCpYfh8cQavz0A6FGTN/51L/gFV6vpjTfeyJNPPslXX33FBRdcALiGv4YPH054eDjh4eFMnjzZ3X7ChAksWrSId955p1oB6IsvvmDTpk0sWrSIxETX9+Lxxx+vNG/nb3/7m/txcnIykydPZt68edx7770EBgYSEhKCj48P8fHxJ/ysN998k+LiYl577TWCg13nP2vWLIYOHcoTTzxBXFwcABEREcyaNQubzUaXLl0YMmQIGRkZNQpAGRkZrF+/nm3btpGUlATAa6+9Rvfu3Vm5ciX9+vVj586d/OUvf6FLly4AdOzY0f3+nTt3Mnz4cHr0cP2Xbteu3WnXICIidcf0IbC3336bSZMmMW3aNNasWUNKSgrp6elkZ2dX2T4yMpIHHniA5cuX89NPPzFu3DjGjRvHokWLPNoNGjSIffv2ubdTDec0d126dOHss8/mlVdeAWDLli1888033HTTTQA4HA4eeeQRevToQWRkJCEhISxatIidO3dW6/gbN24kKSnJHX4ABgwYUKnd22+/zcCBA4mPjyckJIS//e1v1f6MYz8rJSXFHX4ABg4ciNPpZPPmze593bt3x2azuZ8nJCSc8OeqOp+ZlJTkDj8A3bp1o0WLFmzcuBGASZMmcfPNN5OWlsaMGTPYunWru+1dd93Fo48+ysCBA5k2bVqNJp2LiEjdMb0H6J///Ce33HIL48aNA+CFF17gk08+4ZVXXuH++++v1L6i96LC3Xffzauvvsq3335Lenq6e7+/v/9JexHqlG+QqzemFvKLS9mRexhfm5XOcSHuYapTfu5puOmmm5gwYQLPPfccc+bMoX379px//vkAPPnkk/z73/9m5syZ9OjRg+DgYCZOnEhJSUlNTqdKy5cvZ/To0Tz88MOkp6cTHh7OvHnzePrpp+vsM45VMfxUwWKx4HTW341nH3roIa677jo++eQTPvvsM6ZNm8a8efO48sorufnmm0lPT+eTTz7h888/Z/r06Tz99NNMmDCh3uoREZETM7UHqKSkhNWrV5OWlubeZ7VaSUtLY/ny5ad8v2EYZGRksHnzZs477zyP15YuXUpsbCydO3fm9ttvJzc394THsdvt5Ofne2ynxWJxDUXVYgsJCcfiF0yJNYBiS2D13ledkHSMa665BqvVyptvvslrr73GjTfe6A5ay5Yt44orruCPf/wjKSkptGvXjl9//bXax+7atSu7du1i37597n3ff/+9R5vvvvuONm3a8MADD9C3b186duzIjh07PNr4+fnhcJx8OYCuXbuybt06ioqK3PuWLVuG1Wqlc+fO1a75dFSc365du9z7fvnlFw4dOkS3bt3c+zp16sSf//xnPv/8c6666irmzJnjfi0pKYnbbruNDz74gHvuuYeXX365XmoVEZFTMzUA5eTk4HA43HM2KsTFxZGZmXnC9+Xl5RESEoKfnx9Dhgzh2Wef5ZJLLnG/PmjQIF577TUyMjJ44okn+Oqrrxg8ePAJ/7BOnz7dPQ8mPDzcY5ijoVitFkL8K64Gq59VoUNCQhg5ciRTpkxh37593HDDDe7XOnbsyOLFi/nuu+/YuHEjf/rTnzyucDqVtLQ0OnXqxNixY1m3bh3ffPMNDzzwgEebjh07snPnTubNm8fWrVt55plnmD9/vkeb5ORktm3bxtq1a8nJycFut1f6rNGjRxMQEMDYsWPZsGEDS5YsYcKECVx//fWVfpZOl8PhYO3atR7bxo0bSUtLo0ePHowePZo1a9awYsUKxowZw/nnn0/fvn05cuQId955J0uXLmXHjh0sW7aMlStX0rVrVwAmTpzIokWL2LZtG2vWrGHJkiXu10REpOGZPgeoJkJDQ1m7di0rV67kscceY9KkSe4rcQCuvfZaLr/8cnr06MGwYcP4+OOPWblypUebY02ZMoW8vDz3duy/8htSxeXw9bUeELiGwQ4ePEh6errHfJ2//e1vnHnmmaSnp3PBBRcQHx/PsGHDqn1cq9XK/PnzOXLkCP379+fmm2/mscce82hz+eWX8+c//5k777yTXr168d133/Hggw96tBk+fDiDBg3iwgsvJCYmpsq5W0FBQSxatIgDBw7Qr18/RowYwcUXX8ysWbNO75tRhcLCQnr37u2xDR06FIvFwocffkhERATnnXceaWlptGvXjrfffhsAm81Gbm4uY8aMoVOnTlxzzTUMHjyYhx9+GHAFq/Hjx9O1a1cGDRpEp06deP7552tdr4iI1IzFMPE25CUlJQQFBfHee+95/LEdO3Yshw4d4sMPP6zWcW6++WZ27dpVaSL0sWJiYnj00Uf505/+dMrj5efnEx4eTl5eHmFhYR6vFRcXs23bNtq2bUtAQEC16quukjInmzJdw2/dEsLwsTXJfCq1VJ8/YyIizdnJ/n4fz9S/sH5+fvTp04eMjAz3PqfTSUZGRpVXEJ2I0+mscqikwu7du8nNzSUhIaFW9dY3Px8rAb6uq5YK7I3/3mAiIiJNlelXgU2aNImxY8fSt29f+vfvz8yZMykqKnJfFTZmzBhatmzJ9OnTAdd8nb59+9K+fXvsdjuffvopr7/+OrNnzwZcQxgPP/www4cPJz4+nq1bt3LvvffSoUMHj6vEGquwAB+KSx0UHCklIsjP7HJERESaJdMD0MiRI9m/fz9Tp04lMzOTXr16sXDhQvdk1p07d2K1Hu2oKioq4o477mD37t0EBgbSpUsX/ve//zFy5EjANRfjp59+4tVXX+XQoUMkJiZy6aWX8sgjjzSJe0mFBviSXWCnwF6GYRjVuxxeREREToupc4AaK7PmAIHr0v6N+wooczppFxPivjJMvIfmAImI1EyTmQPUlNVXbrRYLMfcHLV+LoeXxk3/JhERqX8KQKepYnXhw4drfvPTU3EHoCOaCO2NKlbfPvY2HiIiUrc0vnKabDYbLVq0cN9TKigoqM7n6fjghLJSjpSVkF9oxc9Hfwi9hdPpZP/+/QQFBeHjo/89RUTqi37D1kDFPcZqemPN6jhUYMde5qTkkK/mAXkZq9VK69atNQFeRKQe6S9rDVgsFhISEoiNjaW0tH7m6axYsZOXvvmdfsmRzBjes14+QxonPz8/jysfRUSk7ikA1YLNZqu3eRpnd05g2qe/sX9jLo9bfQjy038qERGRuqJ/ZjZSHWJDaBURSEmZk2VbTnwnexERETl9CkCNlMVi4aIusQB8uan+5hqJiIh4IwWgRqwiAC3dnK21YUREROqQAlAjdla7KAJ9bezLK2bjvgKzyxEREWk2FIAasQBfGwM7RAHw5aYsk6sRERFpPhSAGrmLurhuCqt5QCIiInVHAaiRu7BLDAA/7jrEgaISk6sRERFpHhSAGrmE8EC6JoRhGK7J0CIiIlJ7CkBNwMW6HF5ERKROKQA1AReWB6Cvf91PqcNpcjUiIiJNnwJQE9ArqQWRwX7kF5exesdBs8sRERFp8hSAmgCb1cIFnVyToZdoGExERKTWFICaiAs1D0hERKTOKAA1Eed1isFmtfBbdiG7Dhw2uxwREZEmTQGoiQgP9KVvmwhAvUAiIiK1pQDUhOju8CIiInVDAagJqQhAy3/P5XBJmcnViIiINF0KQE1Ih9gQkiIDKSlzsmxLrtnliIiINFkKQE2IxWLhos4aBhMREaktBaAmpuJy+CWbsjEMw+RqREREmiYFoCbmrHZRBPrayMwv5pd9+WaXIyIi0iQpADUxAb42BnaIBrQqtIiISE0pADVBFVeDZSgAiYiI1IgCUBNUEYDW7jpEbqHd5GpERESaHgWgJig+PIBuCWEYBnz1636zyxEREWlyFICaKA2DiYiI1JwCUBN1UVdXAPr61/2UOpwmVyMiItK0KAA1USmtWhAZ7EdBcRmrdxw0uxwREZEmRQGoibJZLVzQKQbQqtAiIiKnSwGoCbtQd4cXERGpEQWgJuy8TjHYrBa2ZBeyM/ew2eWIiIg0GQpATVh4oC9920QA8OWmLJOrERERaToaRQB67rnnSE5OJiAggNTUVFasWHHCth988AF9+/alRYsWBAcH06tXL15//XWPNoZhMHXqVBISEggMDCQtLY3ffvutvk/DFBWXw3+5WesBiYiIVJfpAejtt99m0qRJTJs2jTVr1pCSkkJ6ejrZ2VXPa4mMjOSBBx5g+fLl/PTTT4wbN45x48axaNEid5t//OMfPPPMM7zwwgv88MMPBAcHk56eTnFxcUOdVoO5uPxy+O+35lJkLzO5GhERkabBYhiGYWYBqamp9OvXj1mzZgHgdDpJSkpiwoQJ3H///dU6xplnnsmQIUN45JFHMAyDxMRE7rnnHiZPngxAXl4ecXFxzJ07l2uvvfaUx8vPzyc8PJy8vDzCwsJqfnINwDAMzntyCbsOHOGl6/twafd4s0sSERExxen8/Ta1B6ikpITVq1eTlpbm3me1WklLS2P58uWnfL9hGGRkZLB582bOO+88ALZt20ZmZqbHMcPDw0lNTT3hMe12O/n5+R5bU2GxWLios6sXaMlmXQ0mIiJSHaYGoJycHBwOB3FxcR774+LiyMzMPOH78vLyCAkJwc/PjyFDhvDss89yySWXALjfdzrHnD59OuHh4e4tKSmpNqfV4C7q6jrXLzdlY3KHnoiISJNg+hygmggNDWXt2rWsXLmSxx57jEmTJrF06dIaH2/KlCnk5eW5t127dtVdsQ0gtW0kgb42svLt/Ly36fReiYiImMXHzA+Pjo7GZrORleV5CXdWVhbx8Seey2K1WunQoQMAvXr1YuPGjUyfPp0LLrjA/b6srCwSEhI8jtmrV68qj+fv74+/v38tz8Y8Ab42BnaI5ouNWSzZlM0ZLcPNLklERKRRM7UHyM/Pjz59+pCRkeHe53Q6ycjIYMCAAdU+jtPpxG63A9C2bVvi4+M9jpmfn88PP/xwWsdsaiquBvtS84BEREROydQeIIBJkyYxduxY+vbtS//+/Zk5cyZFRUWMGzcOgDFjxtCyZUumT58OuObr9O3bl/bt22O32/n00095/fXXmT17NuCaFDxx4kQeffRROnbsSNu2bXnwwQdJTExk2LBhZp1mvbuwfCL02l2HyC20ExXSdHu0RERE6pvpAWjkyJHs37+fqVOnkpmZSa9evVi4cKF7EvPOnTuxWo92VBUVFXHHHXewe/duAgMD6dKlC//73/8YOXKku829995LUVERt956K4cOHeKcc85h4cKFBAQENPj5NZT48AC6JYTxy758lm7ez/A+rcwuSUREpNEyfR2gxqgprQN0rKc/38yzX25hSM8EnrvuTLPLERERaVBNZh0gqVsVd4f/evN+Sh1Ok6sRERFpvBSAmpGUVi2IDPajwF7Gqu0HzS5HRESk0VIAakZsVgsXdI4BtCq0iIjIySgANTMVd4fP2Jh1ipYiIiLeSwGomTm3Yww2q4Wt+4vYmXvY7HJEREQaJQWgZiY80Jd+yREAfLlJvUAiIiJVUQBqhiqGwb7cvN/kSkRERBonBaBmqCIAfb81lyJ7mcnViIiIND4KQM1Q+5gQWkcGUeJwsmxLjtnliIiINDoKQM2QxWJx9wLpcngREZHKFICaqYpVob/clI3udiIiIuJJAaiZSm0bSZCfjax8Oz/vzTe7HBERkUZFAaiZCvC1MbBDNABLNmkYTERE5FgKQM2Ye1VoBSAREREPCkDN2IWdXQFo3e5D5BbaTa5GRESk8VAAasbiwwPonhiGYcBSLYooIiLipgDUzF10zNVgIiIi4qIA1MxVXA7/9a/7KXU4Ta5GRESkcVAAauZSWrUgKtiPAnsZq7YfNLscERGRRkEBqJmzWS2c3zkG0N3hRUREKigAeQHNAxIREfGkAOQFzu0Yg4/Vwtb9RezILTK7HBEREdMpAHmB8EBf+iZHAOoFEhERAQUgr6FhMBERkaMUgLzERV3iAPjh9wMU2ctMrkZERMRcCkBeon1MMK0jgyhxOPl2S47Z5YiIiJhKAchLWCwW9zCY7g4vIiLeTgHIixw7D8gwDJOrERERMY8CkBdJbRdJkJ+N7AI7P+/NN7scERER0ygAeRF/HxsDO0QDuhpMRES8mwKQl7lYl8OLiIgoAHmbirvDr9t9iJxCu8nViIiImEMByMvEhQXQPTEMw4Clm/ebXY6IiIgpFIC80MW6HF5ERLycApAXqhgG+/rX/ZQ6nCZXIyIi0vAUgLxQSqsWRAX7UWAvY+X2A2aXIyIi0uAUgLyQ1Wrhgs4aBhMREe+lAOSlKlaFzlAAEhERL9QoAtBzzz1HcnIyAQEBpKamsmLFihO2ffnllzn33HOJiIggIiKCtLS0Su1vuOEGLBaLxzZo0KD6Po0m5dxO0fhYLfy+v4gduUVmlyMiItKgTA9Ab7/9NpMmTWLatGmsWbOGlJQU0tPTyc6uumdi6dKljBo1iiVLlrB8+XKSkpK49NJL2bNnj0e7QYMGsW/fPvf21ltvNcTpNBlhAb70TY4AtCiiiIh4H9MD0D//+U9uueUWxo0bR7du3XjhhRcICgrilVdeqbL9G2+8wR133EGvXr3o0qUL//nPf3A6nWRkZHi08/f3Jz4+3r1FREQ0xOk0KRd3iQMUgERExPuYGoBKSkpYvXo1aWlp7n1Wq5W0tDSWL19erWMcPnyY0tJSIiMjPfYvXbqU2NhYOnfuzO23305ubu4Jj2G328nPz/fYvEHF5fA//H6AInuZydWIiIg0HFMDUE5ODg6Hg7i4OI/9cXFxZGZmVusY9913H4mJiR4hatCgQbz22mtkZGTwxBNP8NVXXzF48GAcDkeVx5g+fTrh4eHuLSkpqeYn1YS0jwmmdWQQJQ4n327JMbscERGRBmP6EFhtzJgxg3nz5jF//nwCAgLc+6+99louv/xyevTowbBhw/j4449ZuXIlS5curfI4U6ZMIS8vz73t2rWrgc7AXBaLxX012JcbNQwmIiLew9QAFB0djc1mIysry2N/VlYW8fHxJ33vU089xYwZM/j888/p2bPnSdu2a9eO6OhotmzZUuXr/v7+hIWFeWzeoiIALdmcjWEYJlcjIiLSMEwNQH5+fvTp08djAnPFhOYBAwac8H3/+Mc/eOSRR1i4cCF9+/Y95efs3r2b3NxcEhIS6qTu5iS1XSRBfjayC+z8vNc75j6JiIiYPgQ2adIkXn75ZV599VU2btzI7bffTlFREePGjQNgzJgxTJkyxd3+iSee4MEHH+SVV14hOTmZzMxMMjMzKSwsBKCwsJC//OUvfP/992zfvp2MjAyuuOIKOnToQHp6uinn2Jj5+9g4p0M0ABkaBhMRES9hegAaOXIkTz31FFOnTqVXr16sXbuWhQsXuidG79y5k3379rnbz549m5KSEkaMGEFCQoJ7e+qppwCw2Wz89NNPXH755XTq1ImbbrqJPn368M033+Dv72/KOTZ27nlAmxWARETEO1gMTfyoJD8/n/DwcPLy8rxiPlBWfjGpj2dgscCKv6YRE6qgKCIiTc/p/P02vQdIzBcXFsAZLcMwDFiqXiAREfECCkACwEWdj14NJiIi0twpAAlwdFXob37NodThNLkaERGR+qUAJACktGpBVLAfBfYyVm4/YHY5IiIi9UoBSACwWi1c0FmrQouIiHdQABI3XQ4vIiLeQgFI3M7tFI2P1cLv+4vYnlNkdjkiIiL1RgFI3MICfOmXHAnAl5vUCyQiIs2XApB4OPbmqCIiIs2VApB4uKirKwD98PsBCu1lJlcjIiJSPxSAxEO76GDaRAVR4nDy7W85ZpcjIiJSLxSAxIPFYuHCilWhNQ9IRESaKQUgqeTirkfnATmduleuiIg0PwpAUkn/tpEE+dnILrDz8958s8sRERGpcwpAUom/j41zOkQDuhxeRESaJwUgqVLFMJhWhRYRkeZIAUiqVDERet2uQ+wvsJtcjYiISN1SAJIqxYYFcEbLMACWqhdIRESaGQUgOaGLOmtVaBERaZ4UgBra4QNmV1BtF3WNA+DrX3MoKXOaXI2IiEjdUQBqSNuXwb9TYP17ZldSLT1bhhMd4kehvYxV25tOcBMRETkVBaCGtPEjsOfDB7fAhg/MruaUrFYL53cqvxpMl8OLiEgzogDUkNKnQ68/guGE92+GXz40u6JTcl8OrwAkIiLNiAJQQ7Ja4fJnIGUUGA5470bY9InZVZ3UOR2j8bFa+D2niO05RWaXIyIiUicUgBqa1QZXPAc9rgZnGbwzFjYvNLuqEwoL8KVfciSgXiAREWk+ahSAdu3axe7du93PV6xYwcSJE3nppZfqrLBmzWqDYS9A96vAWQrvXA+/fWF2VSekYTAREWluahSArrvuOpYsWQJAZmYml1xyCStWrOCBBx7g73//e50W2GzZfOCql6Dr5eAogXnXwdYvza6qShd2cQWgH7blUmgvM7kaERGR2qtRANqwYQP9+/cH4J133uGMM87gu+++44033mDu3Ll1WV/zZvOFEa9Alz+Aww5vjYLfvzK7qkraRQfTJiqIUofBt7/lmF2OiIhIrdUoAJWWluLv7w/AF198weWXXw5Aly5d2LdvX91V5w1svjBiDnQaDGXF8OZI2P6t2VV5sFgsXNSlYhgsy+RqREREaq9GAah79+688MILfPPNNyxevJhBgwYBsHfvXqKiouq0QK/g4wfXvAodL4WyI/DGNbDjO7Or8lARgJZs3o/TaZhcjYiISO3UKAA98cQTvPjii1xwwQWMGjWKlJQUAD766CP30JicJh9/uOZ1aH8RlBbBG1fDzh/Mrsqtf9tIgvxs7C+w8/PefLPLERERqRWLYRg1+ue8w+EgPz+fiIgI977t27cTFBREbGxsnRVohvz8fMLDw8nLyyMsLKxhP7z0iGsYbNtX4BcKYxZAq74NW8MJ/On1VSz6OYuJaR2ZmNbJ7HJEREQ8nM7f7xr1AB05cgS73e4OPzt27GDmzJls3ry5yYcf0/kGwqh5kHwulBTA61fBnjVmVwUcMwymy+FFRKSJq1EAuuKKK3jttdcAOHToEKmpqTz99NMMGzaM2bNn12mBXskvyBWCWp8N9jx4fRjsXWt2VVzY2RWA1u3OY3+B3eRqREREaq5GAWjNmjWce+65ALz33nvExcWxY8cOXnvtNZ555pk6LdBr+YfA6HcgKRWKy0NQ5npTS4oNC6BHy3AAlmxWL5CIiDRdNQpAhw8fJjQ0FIDPP/+cq666CqvVyllnncWOHTvqtECv5h8Ko9+Dln3hyEF49XLI+tnUki7UMJiIiDQDNQpAHTp0YMGCBezatYtFixZx6aWXApCdnd3wk4abu4AwuP4DSDwTjhxwhaDsTaaVUzEP6Jvfcigpc5pWh4iISG3UKABNnTqVyZMnk5ycTP/+/RkwYADg6g3q3bv3aR/vueeeIzk5mYCAAFJTU1mxYsUJ27788suce+65REREEBERQVpaWqX2hmEwdepUEhISCAwMJC0tjd9+++2062o0AsJdISghBQ7nwKtDYf+vppTSs2U40SF+FNrLWLn9gCk1iIiI1FaNAtCIESPYuXMnq1atYtGiRe79F198Mf/6179O61hvv/02kyZNYtq0aaxZs4aUlBTS09PJzq56iGXp0qWMGjWKJUuWsHz5cpKSkrj00kvZs2ePu80//vEPnnnmGV544QV++OEHgoODSU9Pp7i4uCan2zgERsD1CyCuBxRlu0JQ7tYGL8NqtXBBZ90cVUREmrYarwNUoeKu8K1atarR+1NTU+nXrx+zZs0CwOl0kpSUxIQJE7j//vtP+X6Hw0FERASzZs1izJgxGIZBYmIi99xzD5MnTwYgLy+PuLg45s6dy7XXXnvKY5q6DtCpFOW6wk/2zxCaCOM+gch2DVrCp+v3cccba2gXHcyXky9o0M8WERE5kXpfB8jpdPL3v/+d8PBw2rRpQ5s2bWjRogWPPPIITmf154WUlJSwevVq0tLSjhZktZKWlsby5curdYzDhw9TWlpKZGQkANu2bSMzM9PjmOHh4aSmplb7mI1acBSM+RBiukDBXpg7FA5ub9ASzu0YjY/Vwu85RWzLKWrQzxYREakLNQpADzzwALNmzWLGjBn8+OOP/Pjjjzz++OM8++yzPPjgg9U+Tk5ODg6Hg7i4OI/9cXFxZGZmVusY9913H4mJie7AU/G+0zmm3W4nPz/fY2vUQmJg7P9BdCfI3+0KQYd2NtjHhwb40r+tK3BqGExERJqiGgWgV199lf/85z/cfvvt9OzZk549e3LHHXfw8ssvM3fu3Dou8cRmzJjBvHnzmD9/PgEBATU+zvTp0wkPD3dvSUlJdVhlPQmJdYWgqA6QtxPm/gHydjfYx2tVaBERacpqFIAOHDhAly5dKu3v0qULBw5U/8qg6OhobDYbWVlZHvuzsrKIj48/6XufeuopZsyYweeff07Pnj3d+yvedzrHnDJlCnl5ee5t165d1T4HU4XGu0JQRFs4tMMVgvL3NshHV6wH9MO2XArtZQ3ymSIiInWlRgEoJSXFPWn5WLNmzfIII6fi5+dHnz59yMjIcO9zOp1kZGS4L62vyj/+8Q8eeeQRFi5cSN++njcKbdu2LfHx8R7HzM/P54cffjjhMf39/QkLC/PYmoywRLjhY2jRBg5uc02QLqje8GFttIsOJjkqiFKHwbe/7a/3zxMREalLPjV50z/+8Q+GDBnCF1984Q4Vy5cvZ9euXXz66aendaxJkyYxduxY+vbtS//+/Zk5cyZFRUWMGzcOgDFjxtCyZUumT58OwBNPPMHUqVN58803SU5Ods/rCQkJISQkBIvFwsSJE3n00Ufp2LEjbdu25cEHHyQxMZFhw4bV5HQbv/BWrhA0ZwjkbnGFoBs+cQ2T1ROLxcKFXWKZs2w7X27KZtAZCfX2WSIiInWtRj1A559/Pr/++itXXnklhw4d4tChQ1x11VX8/PPPvP7666d1rJEjR/LUU08xdepUevXqxdq1a1m4cKF7EvPOnTvZt2+fu/3s2bMpKSlhxIgRJCQkuLennnrK3ebee+9lwoQJ3HrrrfTr14/CwkIWLlxYq3lCjV6L1jD2IwhrBTm/ukJQYf32zFTMA/py036czlqtpiAiItKgar0O0LHWrVvHmWeeicPhqKtDmqJRrwN0KrlbXXOBCvZCbHfXHKHgqHr5KHuZgzP/vpiiEgcf3TmQnq1a1MvniIiIVEe9rwMkjVhUe1foCYl3LZb42hVwuH5uWeHvY+OcjtGALocXEZGmRQGoOYruUN7zEwtZ6+H1Ya67ydeDo8NgCkAiItJ0KAA1VzGdXHOCgqJh3zp4/Soozqvzj7mw/L5gP+3OI7ugCd9rTUREvMppXQV21VVXnfT1Q4cO1aYWqWuxXV0haO4fYO8a+N9w+OMHEFB385piwwLo0TKc9XvyWLp5P9f0bQKLSIqIiNc7rR6gY1dLrmpr06YNY8aMqa9apSbiurtCUGAE7F4Jb4wAe0GdfsSFWhVaRESamDq9Cqy5aNJXgZ3I3rXw2uWuYbDWZ8Mf3wO/4Do59Lpdh7jiuWWE+Puw5sFL8PPRyKqIiDQ8XQUmlSX2gusXgH847PwO3hwJJYfr5NA9WoYTHeJPob2Mldvr54ozERGRuqQA5E1angnXfwB+obD9G3jrWig9UuvDWq0WLugcA+hqMBERaRoUgLxNq77wx/fBLwS2fQXzRkNp7a/euliXw4uISBOiAOSNWqfC6HfBNwi2ZsA710OZvVaHPKdjNL42C9tyitiWU1RHhYqIiNQPBSBv1eZsuO4d8AmE3z6Hd8ZCWUmNDxca4Eu/5EhAvUAiItL4KQB5s7bnwnXzwCcAfv0M3hsHjtIaH+7oqtBZdVWhiIhIvVAA8nbtLoBr3wSbP2z6GN67scYhqCIArdh2gEJ7WR0WKSIiUrcUgAQ6XAzXvgE2P9j4EXxwKzhOP8C0iwkhOSqIUofBt7/tr4dCRURE6oYCkLh0vASueQ2svvDzB7DgdnA6TvswF3WJAyBjo+YBiYhI46UAJEd1HgxXzwWrD6x/Bz4cf9ohqGIYbMnm/TidWmRcREQaJwUg8dT1DzDiFbDYYN1b8H93gdNZ7bf3bxtJsJ+NnEI7G/bW/d3nRURE6oICkFTW7QoY/h+wWOHH/8HHE6sdgvx8rJzb0bUqtIbBRESksVIAkqqdcRVc+ZIrBK15FT6dDNW8b+7RYTAFIBERaZwUgOTEel4Nw2YDFlj1X/jsvmqFoAu6uHqAftqdR3ZB7W+zISIiUtcUgOTkUq6FK2a5Hq94ERY9cMoQFBsaQM9W4QAs3aTL4UVEpPFRAJJT6/1HGPpv1+Pvn4PFU08Zgi7srJujiohI46UAJNXT5wYY8k/X4++egYy/nzQEVcwD+ua3/ZSUVf8qMhERkYagACTV1+8mGPyk6/G3/4Sl00/YtEfLcKJD/CkqcbBi24EGKlBERKR6FIDk9KTeCunlweerJ2DpE1U2s1otXNjZNRlaw2AiItLYKADJ6RtwB1zyiOvx0sfhm6erbKbL4UVEpLFSAJKaGXgXXDzN9Tjj77Ds35WanNMxGl+bhW05Rfy+v7CBCxQRETkxBSCpuXMnwYV/cz1ePBWWP+fxcmiAL/3bRgIaBhMRkcZFAUhq5/y/wPn3ux4v+iv88KLHyxWXw2sYTEREGhMFIKm9C+6Hcye7Hn92L6x42f1SxTygH34/QEFxqRnViYiIVKIAJLVnscBFf4OBE13PP50Mq+YA0C4mhLbRwZQ5Db79Lce8GkVERI6hACR1w2KBtIdgwJ2u5x9PhDWvA1oVWkREGh8FIKk7Fgtc+iik3uZ6/tEEWPuWx+XwTmf17igvIiJSnxSApG5ZLDBoBvS7GTBgwe2cVfgFwX42cgpLWL8nz+wKRUREFICkHlgsrltm9BkHGPh8dDsTEzYAGgYTEZHGQQFI6ofV6rp5au/rwXByU/bjDLb+oAAkIiKNggKQ1B+rFYY+A71GYzUcPOM7i8R9X5CdX2x2ZSIi4uUUgKR+Wa1w+bPQcyS+FgezfJ9hW8Z/oeSw2ZWJiIgXMz0APffccyQnJxMQEEBqaiorVqw4Yduff/6Z4cOHk5ycjMViYebMmZXaPPTQQ1gsFo+tS5cu9XgGckpWG1zxPJuiL8XX4iB13V9hRmv4b7rrPmJbv4SSIrOrFBERL2JqAHr77beZNGkS06ZNY82aNaSkpJCenk52dtXzRA4fPky7du2YMWMG8fHxJzxu9+7d2bdvn3v79ttv6+sUpLpsPpRcPpsXy4awz4gEZyns+t51J/nXr3QFov+kweJp8NsXYC8wu2IREWnGLIZhmLYwS2pqKv369WPWrFkAOJ1OkpKSmDBhAvfff/9J35ucnMzEiROZOHGix/6HHnqIBQsWsHbt2hrXlZ+fT3h4OHl5eYSFhdX4OOLJ6TS47j/f8/3vubT3yeGZs4roXroeti+DvJ2ejS02SEiB5IGQfC60PgsCws0pXEREmoTT+fttWg9QSUkJq1evJi0t7WgxVitpaWksX768Vsf+7bffSExMpF27dowePZqdO3ee+k1S76xWC3Nu6M9FXeLYWhbD0GXJvN3qr/Dn9XD3TzBsNvT6I0Qkg+GAvWvgu2fhzWvgiWR48TxY9ABs+hSOHDT7dEREpAnzMeuDc3JycDgcxMXFeeyPi4tj06ZNNT5uamoqc+fOpXPnzuzbt4+HH36Yc889lw0bNhAaGlrle+x2O3a73f08Pz+/xp8vJxfoZ+PF6/sw5YP1vLd6N/e9v579BXbGX9gBS0Qb6HWdq2HeblfP0I5vXV8PbIV961zb8lmABeLOgORzXL1EbQZCUKSp5yYiAoCjDAozIX/v0a2g4vE+yN8DpUfANxB8g8A3oPxrIPgc89i9Vee1QM/9PgGuNdnkhEwLQPVl8ODB7sc9e/YkNTWVNm3a8M4773DTTTdV+Z7p06fz8MMPN1SJXs/XZuXJET2JDfXn+aVbeerzX9lfYGfq0O7YrOX/w4a3gpSRrg1cvzR2LIPt37q23N8ga71r+2G2q01sN1cgalMeiEJizDlBEWm+So+cINgcsxVlg+E0uVDLSUJTeXDyeO3Yxyd5zee44OUb6LrQpQkyLQBFR0djs9nIysry2J+VlXXSCc6nq0WLFnTq1IktW7acsM2UKVOYNGmS+3l+fj5JSUl1VoNUZrFYuHdQF2JC/fn7x7/w6vId5BSW8M+RKfj7VPE/U1gC9Bjh2gAKslyBqCIU7d8E2b+4thUvudpEdz6mh+gcCI2rfFwREQDDgOJD5T00xwabPZ77qjv8bvWB0AQISyz/2tL1OKz8sV8wlBZD6WFXqCo74vpaerh8f/njsmPanPS18sfO0ooTKn/tMBw5UF/fNRebXxXh6LjgVem1QEg6C9qeW7+1nYRpAcjPz48+ffqQkZHBsGHDANck6IyMDO688846+5zCwkK2bt3K9ddff8I2/v7++Pv719lnSvWNG9iW6BB/Jr2zlk/W7+NAUQkvjulDWIDvyd8YGgdnXOXaAIpyysNQeSDK/hlyNru2Vf91tYnqUN5DVB6KwhLr9+REpHFwOqFovyvMFOw7Ltgcs6+0muuT+QZVEWwSPfcFx7jWQWtojtLyQHVsODpm8whax75WEaiODVqHT/C+Ytdz92eWuDZO816P5/zZOwMQwKRJkxg7dix9+/alf//+zJw5k6KiIsaNGwfAmDFjaNmyJdOnTwdcE6d/+eUX9+M9e/awdu1aQkJC6NChAwCTJ09m6NChtGnThr179zJt2jRsNhujRo0y5yTllIamJBIZ7MefXl/N8t9zufbF75l7Yz9iQwOqf5DgaOh2hWsDOHwAdnznCkM7voXMDZC7xbWtnutqE9G2vIeofNishXr9RJqcshJXgCnY59lbc2ywKdgHzrLqHS8wwhVgKnpvPMJN+deA8MY7v8bm69qo5yuYnU5XYKoyaB3fq1VV0DoMLfvUb42nYOpl8ACzZs3iySefJDMzk169evHMM8+QmpoKwAUXXEBycjJz584FYPv27bRt27bSMc4//3yWLl0KwLXXXsvXX39Nbm4uMTExnHPOOTz22GO0b9++2jXpMnhzbNiTxw1zVpBTWEJSZCCv3ZhK2+jgujn4kYOw8/ujc4gyf6o8Rt+iteuS+zYDXT1ELdo03l9yIt7AXnhcsNlzNNBU7Cuq7v0FLRAaX0Wwaek5VOUXVK+nJPXrdP5+mx6AGiMFIPPsyC1izCsr2JF7mKhgP+aM60fPVi3q/oOK82DnD7D9G9fQ2d61rkvvjxWedDQMtRkIke0UiETqgmG4/lFSKdgce6XUXrBXc0jF5lf1PJtj94XEga3ZXfcjx1EAqiUFIHPtL7Azbu4KNuzJJ8jPxgt/7MN5ner5ii57gSsQVVx2v3dN5S7z0MSjYSj5HNecIgWi02MYUGYHH39977zFkUOQ9XP5tt71NXtj9efb+IdV7rU5fu5NUJR+ngRQAKo1BSDzFdrLuO311Xy7JQcfq4Wnr0nhil4tG66AkiLY9UP5WkTLYPeqY66uKBcSd0wP0TkQ07l5/xIuKwF7fvlWAMXlX+3HfHXvO8l+Zyn4hbi+XzFdXFtsV9fX8FbN+3vYnDkdcOB3yCwPOVkbXF/zdp34PUHRJ55nUxF0AvQ7WKpPAaiWFIAah5IyJ/e8u47/W7cXgL8N6crN57YzqZjDsHvl0SvNdq8Eh92zTVD00TCUPBBiuppzFcjxHGWewaVSeDnF/op9x59vfXAHo64Q2+VoQFIwalyOHHSFm8wNR4NO9kbPK4OOFd4a4rq7tvgzILY7RLRx9QSK1CEFoFpSAGo8nE6DRz75hTnLtgPwp/Pacd+gLlitJv8xLC2GPauOrla9a4XryoZjBUZCm7OPXmUWd8bpBSKn45jelBP1qBwbXvKqaFtQ/aGG6vILAf/Q8i3M9TUg7JjnYUdfr3J/CBRkuv5g7t9U/nWza3HLE12p4xd6tMcotsvRgBTWUsGoPjnKXKuwZ20oDzvlQ1n5u6tu7xvkWpA0rrvr5z3+DNfzwBYNWrZ4LwWgWlIAalwMw+CFr37niYWuW6Rc1bslT4zoia+tEfSuVCizw5415XOIygPR8cEjINwVhBLPdP2htxe4JnlW2ftSACWFdVujT2AVoaT8a0BY5VDjsf+Y1+tr1VdHKeRuhf0bIXuTKxzt3+RauuBUwagiFFUEJAWj03f4wNHenIrAs39T5WBfoUVrV8iJO6O8Z6eH6z5+TXRVYGkeFIBqSQGocXp31S7u/2A9DqfBBZ1jeH70mQT5NdKrOspKYN/ao5fd7/qh5oHG5ndcr0p45VBSVU/L8eHFdorFJRurshJXL8T+TeXBqDwgHdh64mDkH1beY3TscFpX17wSbw9GjjJXqMw6Zvgqc4PrCqyq+AZD3DG9OnFnuJ4HhDds3SLVoABUSwpAjdeXm7K44401FJc6SUlqwZwb+hEZ7Gd2WafmKHPdyHXHt64hH9/AU/fAVAQdzZOoWkUwqhhKqwhI1QpGFROvOzfvYFSU69mrk7XB9T060XyuFm1cPTnusNPdtWBoY5jLJlINCkC1pADUuK3ZeZAb567k0OFS2kUH8+qN/UmK1OJlUq6sxNXD4Q5F5QEpd2vltZ4q+Icf7TGquCIttqvrKqSmEIwcpa5zdk9KLg89Bfuqbu8X4pqbE3/G0bAT201XXEmTpwBUSwpAjd+W7ALGvrKSPYeOEBvqz6s39qdrgv5byUm4g9Hxc4yqEYzcc4w6mx+MinKOm5RcPlfHUVJ1+4i2npOS47pDi2T16kizpABUSwpATUNmXjFjX1nB5qwCQgN8eHlMX85qF2V2WdLUlNmP9hh5zDH6/eTBKLZL5TlGofF1F4wcpZDza/kcnfVHr8AqzKy6vV+I59BVfA9XWPMPrZt6RJoABaBaUgBqOvIOl3LLa6tYsf0Afj5Wnrm2F4POSDC7LGkOKoJRpTlGJwlGAeHHLe7YuXrBqHD/0VWSKyYl799UefFNACwQWdGr0+Po2jrhrdWrI15PAaiWFICaluJSB3e99SOf/5KFxQKPXHEGfzyrjdllSXNVZoec3yrPMTrwe+Ub7FYICPccQgsIh+xfjoadE93Q0z/s6AKCFVdgxXZ1raUkIpUoANWSAlDTU+Zw8uCHP/PWip0A3H1xRyamdcTSFCawSvNwbDA6ttfoZMHIzeK62W78MevqxJ3hWmtHP8Mi1XY6f78b6SIqIqfHx2bl8SvPIDbUn39n/Ma/M34ju8DOo8POwGb2qtHiHXz8XQEm/gzP/aXFrlWu928+GoyK81zDZPHH9Or4BZtTt4iXUgCSZsNisfDnSzoRE+rPgx9u4K0VO8kttPPMqN4E+Gp1WjGJb4BrQnJ8D7MrEZFjaMacNDt/PKsNs0efiZ+Plc9/yWLMf1eQd6SqyaQiIuKtFICkWRp0RgKv3difUH8fVmw/wDUvLCcz7wT3NBIREa+jACTN1lntonjntgHEhvqzOauA4bO/Y0t2Hd9gVEREmiQFIGnWuiaE8f7tZ9MuOpg9h45w9QvfsWbnQbPLEhERkykASbOXFBnEu7cNICWpBQcPl3Ldy9+zZNMJ1l0RERGvoAAkXiEqxJ+3bknl/E4xFJc6ufm1Vby3erfZZYmIiEkUgMRrBPn58J+xfbmqd0scToPJ767jha+2orVARUS8jwKQeBVfm5Wnrk7hT+e1A2DGZ5t45OONOJ0KQSIi3kQBSLyO1WphymVd+duQrgC8smwbE99eS0nZqW5XICIizYUCkHitm89tx8yRvfCxWvho3V5unLuSQnuZ2WWJiEgDUAASrzasd0teuaEfQX42vt2Sw6iXvmd/gd3sskREpJ4pAInXO69TDG/dchZRwX6s35PHiBe+Y0dukdlliYhIPVIAEgFSklrw3u1nkxQZyI7cwwyf/R0b9uSZXZaIiNQTBSCRcm2jg3n/9rPpmhBGTmEJ1770Pd9tyTG7LBERqQcKQCLHiA0N4O0/ncVZ7SIptJcxds4K/m/dXrPLEhGROqYAJHKcsABfXr2xP0N6JFDqMLhr3o/MXbbN7LJERKQOKQCJVMHfx8Yzo3ozZkAbDAMe+r9f+MfCTVo1WkSkmVAAEjkBm9XCw5d3Z/KlnQB4fulW7nv/J8ocWjBRRKSpUwASOQmLxcKdF3XkieE9sFrgnVW7+dPrqzlS4jC7NBERqQUFIJFqGNmvNS9e3xd/HysZm7IZ/Z/vOVhUYnZZIiJSQwpAItV0Sbc43rg5lfBAX9bsPMTVLy5n76EjZpclIiI1oAAkchr6Jkfy7m0DSAgPYEt2IVc9/x2/ZhWYXZaIiJwmBSCR09QpLpT3bz+bDrEhZOYXM2L2d6zafsDsskRE5DSYHoCee+45kpOTCQgIIDU1lRUrVpyw7c8//8zw4cNJTk7GYrEwc+bMWh9TpCYSWwTy3m0D6NMmgvziMkb/5wcW/5JldlkiIlJNpgagt99+m0mTJjFt2jTWrFlDSkoK6enpZGdnV9n+8OHDtGvXjhkzZhAfH18nxxSpqRZBfvzvplQu7hKLvczJn15fxbwVO80uS0REqsFimLiyW2pqKv369WPWrFkAOJ1OkpKSmDBhAvfff/9J35ucnMzEiROZOHFinR2zQn5+PuHh4eTl5REWFnb6JyZepczh5K/z1/POqt0A3HNJJ+68qAMWi8XkykREvMvp/P02rQeopKSE1atXk5aWdrQYq5W0tDSWL1/eoMe02+3k5+d7bCLV5WOz8sTwntx5YQcAnl78K9M++hmHU6tGi4g0VqYFoJycHBwOB3FxcR774+LiyMzMbNBjTp8+nfDwcPeWlJRUo88X72WxWJic3pmHL++OxQKvLd/BhLfWUFyqBRNFRBoj0ydBNwZTpkwhLy/Pve3atcvskqSJGnt2Ms+O6o2fzcqn6zO5Yc4K8otLzS5LRESOY1oAio6OxmazkZXleeVMVlbWCSc419cx/f39CQsL89hEauoPPROZO64fIf4+fP/7AUa++D3Z+cVmlyUiIscwLQD5+fnRp08fMjIy3PucTicZGRkMGDCg0RxTpCbO7hDNvFvPIjrEn4378rlq9nf8vr/Q7LJERKScqUNgkyZN4uWXX+bVV19l48aN3H777RQVFTFu3DgAxowZw5QpU9ztS0pKWLt2LWvXrqWkpIQ9e/awdu1atmzZUu1jijSUM1qG88HtZ5McFcTug0cY8cJy1u06ZHZZIiKCyZfBA8yaNYsnn3ySzMxMevXqxTPPPENqaioAF1xwAcnJycydOxeA7du307Zt20rHOP/881m6dGm1jlkdugxe6lJOoZ1xc1ayfk8eQX42Zv+xD+d3ijG7LBGRZud0/n6bHoAaIwUgqWuF9jJu/99qvvktBx+rhcE9EriydyLndozB16ZrEURE6oICUC0pAEl9KClzct/7PzH/xz3ufVHBfgxNSWRY75aktArX4okiIrWgAFRLCkBSXwzDYN3uPBb8uIf/W7eX3KIS92tto4O5olciw3q1JDk62MQqRUSaJgWgWlIAkoZQ6nDy7ZYcFvy4h0U/Z1Jc6nS/1rt1C67s3ZI/9EwkMtjPxCpFRJoOBaBaUgCShlZoL+PznzOZ/+Melm3JoeIuGj5WCxd0jmFY75akdY0jwNdmbqEiIo2YAlAtKQCJmbLzi/lo3V4WrN3Dhj1H70sX4u/D4DPiubJ3S1LbRWGzar6QiMixFIBqSQFIGovfsgpYsHYPC37cy55DR9z748MCXPOFereka4J+RkVEQAGo1hSApLFxOg1W7TjI/B/38MlPe8kvLnO/1iU+lGG9W3J5SiKJLQJNrFJExFwKQLWkACSNmb3MwZJN+1nw4x6+3JRNicM1edpigbPaRnFl75YM6hFPWICvyZWKiDQsBaBaUgCSpiLvcCmfbtjH/B/3sGLbAfd+Px8rl3SNY1jvlpzfKQY/Hy22KCLNnwJQLSkASVO0++BhPly7l/k/7mFL9tEbr7YI8uUPPRO4sndLzmwdocUWRaTZUgCqJQUgacoMw+Dnvfks+HEPH67by/4Cu/u1pMhAruzVkit6t6R9TIiJVYqI1D0FoFpSAJLmwuE0+G5rDvN/3MOiDZkUlTjcr6W0CmdY+WKLMaH+JlYpIlI3FIBqSQFImqPDJWUs/iWLBT/u4evfcnCUr7Zos1o4t2M0V/ZuySXd4gjy8zG5UhGRmlEAqiUFIGnucgrtfLxuL/PX7mXdrkPu/UF+NgZ1j2dY75ac3T4KH92pXkSaEAWgWlIAEm/y+/5CFqzdy4If97DzwGH3/phQfy5PSeTK3i3pnhimydMi0ugpANWSApB4I8MwWLPzEAt+3MPHP+3l4OFS92sdYkO4snyxxaTIIBOrFBE5MQWgWlIAEm9XUubk61/3M3/tHr74JQt72dE71fdPjmRY75YM6ZFAeJAWWxSRxkMBqJYUgESOyi8uZeGGTBb8uIflv+dS8RvDz2blwi4xXNm7JRd2icXfR3eqFxFzKQDVkgKQSNX25R3ho/LFFjdlFrj3hwX4MKRnAsN6taRfciRW3aleREygAFRLCkAip7ZxXz4L1u7hwx/3kplf7N7fskUgV/RyTZ7uGBdqYoUi4m0UgGpJAUik+pxOg++35bLgxz18tj6TAvvRO9V3TwxzT56ODQswsUoR8QYKQLWkACRSM8WlDjI2ZjP/xz189Ws2pQ7XrxerBQZ2iGZYr5aknxFPiL8WWxSRuqcAVEsKQCK1d7CohI/X72PBj3tYveOge3+Ar5WB7aPpmxxJ3+QIerQMJ8BXE6hFpPYUgGpJAUikbu3MPcyCtXtY8OMefs8p8njNz2alR6tw+iZH0LdNJH3aRBAZ7GdSpSLSlCkA1ZICkEj9qLhT/fe/57Jq+0FW7ThITqG9Urv2McH0bePqIeqbHElyVJBWohaRU1IAqiUFIJGGYRgGO3IPs2rHQVbvOMDK7QfZkl1YqV10iB992kS4Q1H3xHD8fHSfMhHxpABUSwpAIuY5WFTC6h0H3aFo3a48ShxOjzb+PlZ6JbVw9xCd2TqC8ECtSi3i7RSAakkBSKTxsJc52LAnj5XbD7JquysUHXufMgCLBTrFhpYHIldPUauIQA2biXgZBaBaUgASabwMw2Dr/iJWbT9Q3kt0kG3HTawGiAvzPzqPqE0kXRNC8bFp2EykOVMAqiUFIJGmZX+B3TVsVh6KNuzJo8zp+astyM9G79Yt3KGod+sIrUck0swoANWSApBI03akxMG63YdYveMgK7cfYPWOgxQUl3m0sVqga0IYfdtEuNckSggPNKliEakLCkC1pAAk0rw4nQa/ZheUzyFyhaLdB49UateyRWD5kJkrFHWKC8WmG7uKNBkKQLWkACTS/GXmFbNqx4Hy9YgO8MvefI4bNSPU34cz2xwNRL2SWhDop1WrRRorBaBaUgAS8T6F9jLW7jzEqh2uIbM1Ow5SVOLwaONjtdA9Mcw1ZNYmgj7JEcSG6iavIo2FAlAtKQCJSJnDyabMAvfE6lXbD5KZX1ypXZuooGOuNougfUwIVg2biZhCAaiWFIBE5HiGYbDn0BH3HKJV2w+yOauA43+DtgjypU/rCN3sVcQECkC1pAAkItWRd6SUH3cenVi9dtchiks9V63WzV5FGo4CUC0pAIlITZQ6nPy8N981bHaSm70mRwWRHB1Mm8ggWke5viZHB9EqIki9RSK10OQC0HPPPceTTz5JZmYmKSkpPPvss/Tv3/+E7d99910efPBBtm/fTseOHXniiSe47LLL3K/fcMMNvPrqqx7vSU9PZ+HChdWqRwFIROpCdW/2WsFigfiwAFpHBtEmKog2UcG0jgwiOSqY1lFBut+ZyCmczt9v05dBffvtt5k0aRIvvPACqampzJw5k/T0dDZv3kxsbGyl9t999x2jRo1i+vTp/OEPf+DNN99k2LBhrFmzhjPOOMPdbtCgQcyZM8f93N/fv0HOR0SkgsViITk6mOToYEb0aQW4bva6cV8+Ow4cZntuETtzD7Mj9zA7Dxym0F7Gvrxi9uUV88O2A5WO1yLI16PXqHWUKxy1iQoiNtRf9z4TOQ2m9wClpqbSr18/Zs2aBYDT6SQpKYkJEyZw//33V2o/cuRIioqK+Pjjj937zjrrLHr16sULL7wAuHqADh06xIIFC2pUk3qARKShGYbBgaISdhw4zI7cIlcoyj1c/vxwlUNpxwrwtdI6MojWka5AlBx1NCi1jAjEV/dBEy/QZHqASkpKWL16NVOmTHHvs1qtpKWlsXz58irfs3z5ciZNmuSxLz09vVLYWbp0KbGxsURERHDRRRfx6KOPEhUVVeUx7XY7dvvRXy75+fk1PCMRkZqxWCxEhfgTFeLPma0jKr1eZC9j5zHhaMeBioBUxJ6DRygudfJrViG/ZlUeYrNZLSS2CKBNZHB5r9HRoNQmKoggP9MHA0QanKk/9Tk5OTgcDuLi4jz2x8XFsWnTpirfk5mZWWX7zMxM9/NBgwZx1VVX0bZtW7Zu3cpf//pXBg8ezPLly7HZKk8wnD59Og8//HAdnJGISP0I9veha0IYXRMq/6u21OFkz8EjriG18h4j17Ca63lxqZNdB46w68AR2FL52NEh/q4wFOmad9QmyjW81iYyiMhgPw2tSbPULGP/tdde637co0cPevbsSfv27Vm6dCkXX3xxpfZTpkzx6FXKz88nKSmpQWoVEaktX5vVPdfoeE6nQXaB3dVzdODosNrO3CK25x4m70gpOYV2cgrtrN5xsNL7Q/x9XBOxo4/pNSqff5QQHqh7pUmTZWoAio6OxmazkZWV5bE/KyuL+Pj4Kt8THx9/Wu0B2rVrR3R0NFu2bKkyAPn7+2uStIg0S1arhfjwAOLDA0htV3kaQN7hUnYcKHJPxHYPseUeJjO/mEJ7Gb/sy+eXfZWnBvjZrLSKCPS4Yq1iWE2X9EtjZ2oA8vPzo0+fPmRkZDBs2DDANQk6IyODO++8s8r3DBgwgIyMDCZOnOjet3jxYgYMGHDCz9m9eze5ubkkJCTUZfkiIk1eeJAvPYNa0LNVi0qvFZc62FUxpFbea1QxKXv3wcOUOJz8nlPE7zlFwH6P91oskBAWUD6U5pp71OaYx7qkX8xm+hDYpEmTGDt2LH379qV///7MnDmToqIixo0bB8CYMWNo2bIl06dPB+Duu+/m/PPP5+mnn2bIkCHMmzePVatW8dJLLwFQWFjIww8/zPDhw4mPj2fr1q3ce++9dOjQgfT0dNPOU0SkqQnwtdExLpSOcaGVXnM4DfYeOnJ0ztGBInbkHA1KRSUO9uYVszevmO9/r3xJf4i/D7Fh/sSHBRAfFkBcePnXMFdvVXxYANEhfvjo6jWpJ6YHoJEjR7J//36mTp1KZmYmvXr1YuHChe6Jzjt37sRqPfo/wNlnn82bb77J3/72N/7617/SsWNHFixY4F4DyGaz8dNPP/Hqq69y6NAhEhMTufTSS3nkkUc0zCUiUkdsVgtJkUEkRQYxsIPna4ZhkFtU4p6IXTGktqN8knZOYQmF9jIK95fx+/6iE36G1QIxof7uYFQRjuLKQ1N8uD9xYQGEBqg3SU6f6esANUZaB0hEpP4U2cvIzC8mK6+YzPzi4x7byc4vJrvAjsNZvT9PwX42dw9SRW9SXKj/0bAUHkBMiL96k7xAk1kHSEREvE+wvw/tY0JoHxNywjYOp0Fuod0VivKKySoPSpl5dvfjrLxiCuxlFJU4+H1/0Sl7k6JD/I/rQTr6OC7Mn7jwAEL9fXTZv5dQABIRkUbHZrUQGxZAbFgAPVuduF2RvexoIDo2IOUd3VfRm5RdYCe7wA7knfB4QX42j7lIroCk3qTmSAFIRESarGB/H9rFhNCuGr1JWfn2SkNux4alguIyDpc4jrmyrWoVvUlH5yX5V5rArd6kxk8BSEREmrVje5N6EH7CdodLytxhKDu/8vBbVp6rN6nsmN6k9Xuq15tUMcRWMU8pNsyfyGB/IoP8CA3wwaoFJRucApCIiAgQ5Hfq3iSn0yCnyE5W3tHepOzjhtwy84rJr2ZvErgCWkSQH5HBvkQG+xEZ7EdEkB9RwX5ElD937wtxfdUik7WnACQiIlJNVquF2NAAYkNP3ZuUlW/37EE6JiDlFJZwoMi1HIDDabhvR1JdwX42j3AUGVQeko7dd8xr4YG+6mU6jgKQiIhIHQvy86FttA9tq7g/27HsZQ4OFpVyoMgViA4cLuFgUQm5Ra6vFfsPHj66r8xpUFTioKjkCLsPHqlWPVYLRAQdE5DKH1f0Mrl7m4L8iAxxfQ30a969TApAIiIiJvH3sREfbiM+PKBa7Q3DIL+4zBWODpdwoLD863GBqWLfgaISCorLcBqQWx6sqivQ11beq+RbPl+p/Guw79HQdMywXIsgvyZ1c1wFIBERkSbCYrEQHuhLeKAvyZy8d6lCSZmTQ4crB6aK0JRb0cNU6Pp6oKiEUofBkVIHew4dYc+h6vUyWSzQItAzHB0/HBdxzHBddIi/qb1MCkAiIiLNmJ+P1X0VXHUYhkGhvYyDRaXkFtnLQ1EpB4rslb4ePOwavss7UophwMHDpRw8XHrSRSkr3DiwLVOHdqvt6dWYApCIiIi4WSwWQgN8CQ3wpXVUULXeU+pwcuhwaZVzljz2FR59LTLY3Hu4KQCJiIhIrfjarMSE+hMTWr2bjhuGQTVv9VZvFIBERESkQVksFmwmz5fWzUxERETE6ygAiYiIiNdRABIRERGvowAkIiIiXkcBSERERLyOApCIiIh4HQUgERER8ToKQCIiIuJ1FIBERETE6ygAiYiIiNdRABIRERGvowAkIiIiXkcBSERERLyO7gZfBcMwAMjPzze5EhEREamuir/bFX/HT0YBqAoFBQUAJCUlmVyJiIiInK6CggLCw8NP2sZiVCcmeRmn08nevXsJDQ3FYrHU6bHz8/NJSkpi165dhIWF1emxmwKdv3efP+h74O3nD/oe6Pzr7/wNw6CgoIDExESs1pPP8lEPUBWsViutWrWq188ICwvzyh/8Cjp/7z5/0PfA288f9D3Q+dfP+Z+q56eCJkGLiIiI11EAEhEREa+jANTA/P39mTZtGv7+/maXYgqdv3efP+h74O3nD/oe6Pwbx/lrErSIiIh4HfUAiYiIiNdRABIRERGvowAkIiIiXkcBSERERLyOAlADeu6550hOTiYgIIDU1FRWrFhhdkkN5uuvv2bo0KEkJiZisVhYsGCB2SU1qOnTp9OvXz9CQ0OJjY1l2LBhbN682eyyGtTs2bPp2bOne/GzAQMG8Nlnn5ldlmlmzJiBxWJh4sSJZpfSIB566CEsFovH1qVLF7PLanB79uzhj3/8I1FRUQQGBtKjRw9WrVpldlkNIjk5udLPgMViYfz48abUowDUQN5++20mTZrEtGnTWLNmDSkpKaSnp5OdnW12aQ2iqKiIlJQUnnvuObNLMcVXX33F+PHj+f7771m8eDGlpaVceumlFBUVmV1ag2nVqhUzZsxg9erVrFq1iosuuogrrriCn3/+2ezSGtzKlSt58cUX6dmzp9mlNKju3buzb98+9/btt9+aXVKDOnjwIAMHDsTX15fPPvuMX375haeffpqIiAizS2sQK1eu9Pjvv3jxYgCuvvpqcwoypEH079/fGD9+vPu5w+EwEhMTjenTp5tYlTkAY/78+WaXYars7GwDML766iuzSzFVRESE8Z///MfsMhpUQUGB0bFjR2Px4sXG+eefb9x9991ml9Qgpk2bZqSkpJhdhqnuu+8+45xzzjG7jEbj7rvvNtq3b284nU5TPl89QA2gpKSE1atXk5aW5t5ntVpJS0tj+fLlJlYmZsnLywMgMjLS5ErM4XA4mDdvHkVFRQwYMMDschrU+PHjGTJkiMfvA2/x22+/kZiYSLt27Rg9ejQ7d+40u6QG9dFHH9G3b1+uvvpqYmNj6d27Ny+//LLZZZmipKSE//3vf9x44411ftPx6lIAagA5OTk4HA7i4uI89sfFxZGZmWlSVWIWp9PJxIkTGThwIGeccYbZ5TSo9evXExISgr+/P7fddhvz58+nW7duZpfVYObNm8eaNWuYPn262aU0uNTUVObOncvChQuZPXs227Zt49xzz6WgoMDs0hrM77//zuzZs+nYsSOLFi3i9ttv56677uLVV181u7QGt2DBAg4dOsQNN9xgWg26G7xIAxs/fjwbNmzwuvkPAJ07d2bt2rXk5eXx3nvvMXbsWL766iuvCEG7du3i7rvvZvHixQQEBJhdToMbPHiw+3HPnj1JTU2lTZs2vPPOO9x0000mVtZwnE4nffv25fHHHwegd+/ebNiwgRdeeIGxY8eaXF3D+u9//8vgwYNJTEw0rQb1ADWA6OhobDYbWVlZHvuzsrKIj483qSoxw5133snHH3/MkiVLaNWqldnlNDg/Pz86dOhAnz59mD59OikpKfz73/82u6wGsXr1arKzsznzzDPx8fHBx8eHr776imeeeQYfHx8cDofZJTaoFi1a0KlTJ7Zs2WJ2KQ0mISGhUtjv2rWr1w0F7tixgy+++IKbb77Z1DoUgBqAn58fffr0ISMjw73P6XSSkZHhdfMfvJVhGNx5553Mnz+fL7/8krZt25pdUqPgdDqx2+1ml9EgLr74YtavX8/atWvdW9++fRk9ejRr167FZrOZXWKDKiwsZOvWrSQkJJhdSoMZOHBgpeUvfv31V9q0aWNSReaYM2cOsbGxDBkyxNQ6NATWQCZNmsTYsWPp27cv/fv3Z+bMmRQVFTFu3DizS2sQhYWFHv/S27ZtG2vXriUyMpLWrVubWFnDGD9+PG+++SYffvghoaGh7rlf4eHhBAYGmlxdw5gyZQqDBw+mdevWFBQU8Oabb7J06VIWLVpkdmkNIjQ0tNKcr+DgYKKiorxiLtjkyZMZOnQobdq0Ye/evUybNg2bzcaoUaPMLq3B/PnPf+bss8/m8ccf55prrmHFihW89NJLvPTSS2aX1mCcTidz5sxh7Nix+PiYHEFMufbMSz377LNG69atDT8/P6N///7G999/b3ZJDWbJkiUGUGkbO3as2aU1iKrOHTDmzJljdmkN5sYbbzTatGlj+Pn5GTExMcbFF19sfP7552aXZSpvugx+5MiRRkJCguHn52e0bNnSGDlypLFlyxazy2pw//d//2ecccYZhr+/v9GlSxfjpZdeMrukBrVo0SIDMDZv3mx2KYbFMAzDnOglIiIiYg7NARIRERGvowAkIiIiXkcBSERERLyOApCIiIh4HQUgERER8ToKQCIiIuJ1FIBERETE6ygAiYhUg8ViYcGCBWaXISJ1RAFIRBq9G264AYvFUmkbNGiQ2aWJSBOle4GJSJMwaNAg5syZ47HP39/fpGpEpKlTD5CINAn+/v7Ex8d7bBEREYBreGr27NkMHjyYwMBA2rVrx3vvvefx/vXr13PRRRcRGBhIVFQUt956K4WFhR5tXnnlFbp3746/vz8JCQnceeedHq/n5ORw5ZVXEhQURMeOHfnoo4/q96RFpN4oAIlIs/Dggw8yfPhw1q1bx+jRo7n22mvZuHEjAEVFRaSnpxMREcHKlSt59913+eKLLzwCzuzZsxk/fjy33nor69ev56OPPqJDhw4en/Hwww9zzTXX8NNPP3HZZZcxevRoDhw40KDnKSJ1xOy7sYqInMrYsWMNm81mBAcHe2yPPfaYYRiGARi33Xabx3tSU1ON22+/3TAMw3jppZeMiIgIo7Cw0P36J598YlitViMzM9MwDMNITEw0HnjggRPWABh/+9vf3M8LCwsNwPjss8/q7DxFpOFoDpCINAkXXnghs2fP9tgXGRnpfjxgwACP1wYMGMDatWsB2LhxIykpKQQHB7tfHzhwIE6nk82bN2OxWNi7dy8XX3zxSWvo2bOn+3FwcDBhYWFkZ2fX9JRExEQKQCLSJAQHB1cakqorgYGB1Wrn6+vr8dxiseB0OuujJBGpZ5oDJCLNwvfff1/pedeuXQHo2rUr69ato6ioyP36smXLsFqtdO7cmdDQUJKTk8nIyGjQmkXEPOoBEpEmwW63k5mZ6bHPx8eH6OhoAN5991369u3LOeecwxtvvMGKFSv473//C8Do0aOZNm0aY8eO5aGHHmL//v1MmDCB66+/nri4OAAeeughbrvtNmJjYxk8eDAFBQUsW7aMCRMmNOyJikiDUAASkSZh4cKFJCQkeOzr3LkzmzZtAlxXaM2bN4877riDhIQE3nrrLbp16wZAUFAQixYt4u6776Zfv34EBQUxfPhw/vnPf7qPNXbsWIqLi/nXv/7F5MmTiY6OZsSIEQ13giLSoCyGYRhmFyEiUhsWi4X58+czbNgws0sRkSZCc4BERETE6ygAiYiIiNfRHCARafI0ki8ip0s9QCIiIuJ1FIBERETE6ygAiYiIiNdRABIRERGvowAkIiIiXkcBSERERLyOApCIiIh4HQUgERER8ToKQCIiIuJ1/h+D09FOrTlungAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-05-27T10:12:26.333527Z",
          "iopub.status.busy": "2024-05-27T10:12:26.333244Z",
          "iopub.status.idle": "2024-05-27T10:12:32.508624Z",
          "shell.execute_reply": "2024-05-27T10:12:32.507602Z",
          "shell.execute_reply.started": "2024-05-27T10:12:26.333502Z"
        },
        "id": "sqAKVy9XqE_n",
        "outputId": "69db1ccd-277d-4aca-e23e-989e22cab367",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m478/478\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 12ms/step\n",
            "Accuracy: 0.9623469320935105\n",
            "Precision: 0.9539609053497943\n",
            "Recall: 0.9715782580222658\n",
            "F1 Score: 0.9626889883849199\n",
            "Confusion Matrix:\n",
            " [[7278  358]\n",
            " [ 217 7418]]\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict(X_test)\n",
        "binary_predictions = (predictions > 0.5).astype(int)\n",
        "accuracy = accuracy_score(y_test, binary_predictions)\n",
        "precision = precision_score(y_test, binary_predictions)\n",
        "recall = recall_score(y_test, binary_predictions)\n",
        "f1 = f1_score(y_test, binary_predictions)\n",
        "conf_matrix = confusion_matrix(y_test, binary_predictions)\n",
        "\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"Precision:\", precision)\n",
        "print(\"Recall:\", recall)\n",
        "print(\"F1 Score:\", f1)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JFlgIMn1oOBD"
      },
      "source": [
        "## Hyperparameter tuning\n",
        "We'll try some configurations of hyperparameters in order to find the best parametrized one. Since we're dealing with binary classification we'll use the accuracy score as the objective of the search (notice that the dataset is balanced, thus giving accuracy representative power)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jHKYMnFFyyyJ",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "def build_model(hp):\n",
        "    model = Sequential()\n",
        "    model.add(Embedding(input_dim=vocab_size, output_dim=hp.Int('embedding_dim', min_value=32, max_value=512, step=32), input_length=input_length))\n",
        "    model.add(Bidirectional(LSTM(units=hp.Int('units', min_value=32, max_value=512, step=32), dropout=hp.Float('dropout', 0, 0.5, step=0.1))))\n",
        "    model.add(Dense(1, activation='sigmoid'))\n",
        "    model.compile(optimizer=Adam(hp.Float('learning_rate', 1e-4, 1e-2, sampling='LOG')),\n",
        "                  loss='binary_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "    return model\n",
        "\n",
        "early_stopping = EarlyStopping(monitor='val_loss',  min_delta=0.001, patience=3, verbose=1, mode='min', restore_best_weights=True)\n",
        "tuner = kt.RandomSearch(\n",
        "    build_model,\n",
        "    objective='val_accuracy',\n",
        "    max_trials=10,\n",
        "    executions_per_trial=3,\n",
        "    directory='results',\n",
        "    project_name='RNN_hlt')\n",
        "\n",
        "tuner.search(X_train, y_train, epochs=5, validation_split=0.2, callbacks=[early_stopping])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VXrmYFTtxbJ_",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "best_model = tuner.get_best_models(num_models=1)[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4SVodVQa3VPG",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "trials = tuner.oracle.get_best_trials()\n",
        "for trial in trials:\n",
        "    print('Trial ID:', trial.trial_id)\n",
        "    print('Score:', trial.score)\n",
        "    print('Hyperparameters:', trial.hyperparameters.values)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iHQ3s12o3Y4g",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "predictions = best_model.predict(X_test)\n",
        "binary_predictions = (predictions > 0.5).astype(int)\n",
        "\n",
        "accuracy = accuracy_score(y_test, binary_predictions)\n",
        "precision = precision_score(y_test, binary_predictions)\n",
        "recall = recall_score(y_test, binary_predictions)\n",
        "f1 = f1_score(y_test, binary_predictions)\n",
        "conf_matrix = confusion_matrix(y_test, binary_predictions)\n",
        "\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"Precision:\", precision)\n",
        "print(\"Recall:\", recall)\n",
        "print(\"F1 Score:\", f1)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G7YYoBxQoFcn"
      },
      "source": [
        "## Adding an attention layer\n",
        "The results obtained seem promising, and it might be advantageous to consider an attention based architecture in order to exploit the contextual relevance of each word considered."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-27T10:12:32.511367Z",
          "iopub.status.busy": "2024-05-27T10:12:32.511076Z",
          "iopub.status.idle": "2024-05-27T10:12:32.519283Z",
          "shell.execute_reply": "2024-05-27T10:12:32.518253Z",
          "shell.execute_reply.started": "2024-05-27T10:12:32.511330Z"
        },
        "id": "7zdvpOiw5DCs",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "class AttentionLayer(Layer):\n",
        "    def __init__(self, **kwargs):\n",
        "        super(AttentionLayer, self).__init__(**kwargs)\n",
        "\n",
        "    def build(self, input_shape):\n",
        "        self.W = self.add_weight(name=\"att_weight\", shape=(input_shape[-1], 1),\n",
        "                                 initializer=\"orthogonal\")\n",
        "        self.b = self.add_weight(name=\"att_bias\", shape=(input_shape[1], 1),\n",
        "                                 initializer=\"zeros\")\n",
        "        super(AttentionLayer, self).build(input_shape)\n",
        "\n",
        "    def call(self, inputs):\n",
        "        # Applying a simple attention mechanism\n",
        "        e = tf.nn.tanh(tf.tensordot(inputs, self.W, axes=1) + self.b)\n",
        "        a = tf.nn.softmax(e, axis=1)\n",
        "        output = inputs * a\n",
        "        return tf.reduce_sum(output, axis=1)\n",
        "\n",
        "    def get_config(self):\n",
        "        return super(AttentionLayer, self).get_config()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-05-27T10:12:32.520713Z",
          "iopub.status.busy": "2024-05-27T10:12:32.520410Z",
          "iopub.status.idle": "2024-05-27T10:12:32.565613Z",
          "shell.execute_reply": "2024-05-27T10:12:32.564758Z",
          "shell.execute_reply.started": "2024-05-27T10:12:32.520689Z"
        },
        "id": "JHgwhrue5ZZn",
        "outputId": "e795b57d-d393-42aa-e4d1-54a9d00c6426",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_1 (Embedding)     (None, 528, 512)          5523456   \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirecti  (None, 528, 2)            4112      \n",
            " onal)                                                           \n",
            "                                                                 \n",
            " batch_normalization_1 (Bat  (None, 528, 2)            8         \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " attention_layer (Attention  (None, 2)                 530       \n",
            " Layer)                                                          \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 3         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5528109 (21.09 MB)\n",
            "Trainable params: 5528105 (21.09 MB)\n",
            "Non-trainable params: 4 (16.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "embedding_dim = 512\n",
        "lstm_units = 1\n",
        "\n",
        "model = Sequential([\n",
        "    Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_shape=(input_length,)),\n",
        "    Bidirectional(LSTM(lstm_units, dropout=0.3, return_sequences=True)),\n",
        "    BatchNormalization(),\n",
        "    AttentionLayer(),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-05-27T10:12:32.566916Z",
          "iopub.status.busy": "2024-05-27T10:12:32.566656Z",
          "iopub.status.idle": "2024-05-27T10:35:43.142363Z",
          "shell.execute_reply": "2024-05-27T10:35:43.141411Z",
          "shell.execute_reply.started": "2024-05-27T10:12:32.566893Z"
        },
        "id": "_XTZRNZG5ax7",
        "outputId": "1c3b2e80-9139-4937-ac9a-4321515c1183",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 1s/step - accuracy: 0.7139 - loss: 0.5724 - val_accuracy: 0.8281 - val_loss: 0.6148\n",
            "Epoch 2/10\n",
            "\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 1s/step - accuracy: 0.8999 - loss: 0.3323 - val_accuracy: 0.9103 - val_loss: 0.4049\n",
            "Epoch 3/10\n",
            "\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 1s/step - accuracy: 0.9566 - loss: 0.2132 - val_accuracy: 0.9586 - val_loss: 0.2289\n",
            "Epoch 4/10\n",
            "\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 1s/step - accuracy: 0.9764 - loss: 0.1455 - val_accuracy: 0.9593 - val_loss: 0.1637\n",
            "Epoch 5/10\n",
            "\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 1s/step - accuracy: 0.9832 - loss: 0.1074 - val_accuracy: 0.9585 - val_loss: 0.1422\n",
            "Epoch 6/10\n",
            "\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 1s/step - accuracy: 0.9871 - loss: 0.0845 - val_accuracy: 0.9580 - val_loss: 0.1335\n",
            "Epoch 7/10\n",
            "\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 1s/step - accuracy: 0.9897 - loss: 0.0664 - val_accuracy: 0.9617 - val_loss: 0.1273\n",
            "Epoch 8/10\n",
            "\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 1s/step - accuracy: 0.9898 - loss: 0.0560 - val_accuracy: 0.9595 - val_loss: 0.1269\n",
            "Epoch 9/10\n",
            "\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 1s/step - accuracy: 0.9922 - loss: 0.0471 - val_accuracy: 0.9593 - val_loss: 0.1250\n",
            "Epoch 10/10\n",
            "\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 1s/step - accuracy: 0.9930 - loss: 0.0409 - val_accuracy: 0.9583 - val_loss: 0.1272\n",
            "Restoring model weights from the end of the best epoch: 9.\n"
          ]
        }
      ],
      "source": [
        "early_stopping = EarlyStopping(monitor='val_loss',  min_delta=0.001, patience=5, verbose=1, mode='min', restore_best_weights=True)\n",
        "history = model.fit(X_train, y_train, validation_split=0.2, epochs=10, batch_size=256, callbacks=[early_stopping])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fpCjOJHyo0Vo"
      },
      "source": [
        "Let's see the learning curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "execution": {
          "iopub.execute_input": "2024-05-27T10:35:43.144085Z",
          "iopub.status.busy": "2024-05-27T10:35:43.143820Z",
          "iopub.status.idle": "2024-05-27T10:35:43.625341Z",
          "shell.execute_reply": "2024-05-27T10:35:43.624511Z",
          "shell.execute_reply.started": "2024-05-27T10:35:43.144061Z"
        },
        "id": "f5Kzi73YhfSI",
        "outputId": "caa42c96-0f6f-4b94-9167-533a7aad66ba",
        "trusted": true
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-05-27T10:35:43.626991Z",
          "iopub.status.busy": "2024-05-27T10:35:43.626648Z",
          "iopub.status.idle": "2024-05-27T10:37:39.074293Z",
          "shell.execute_reply": "2024-05-27T10:37:39.073392Z",
          "shell.execute_reply.started": "2024-05-27T10:35:43.626959Z"
        },
        "id": "Es99wxqy5wBX",
        "outputId": "2d7da8d1-2c55-4a8f-d8df-31727b9ad971",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m478/478\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m115s\u001b[0m 240ms/step\n",
            "Accuracy: 0.9604479077990963\n",
            "Precision: 0.946980292434838\n",
            "Recall: 0.9755075311067453\n",
            "F1 Score: 0.9610322580645161\n",
            "Confusion Matrix:\n",
            " [[7219  417]\n",
            " [ 187 7448]]\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict(X_test)\n",
        "binary_predictions = (predictions > 0.5).astype(int)\n",
        "accuracy = accuracy_score(y_test, binary_predictions)\n",
        "precision = precision_score(y_test, binary_predictions)\n",
        "recall = recall_score(y_test, binary_predictions)\n",
        "f1 = f1_score(y_test, binary_predictions)\n",
        "conf_matrix = confusion_matrix(y_test, binary_predictions)\n",
        "\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"Precision:\", precision)\n",
        "print(\"Recall:\", recall)\n",
        "print(\"F1 Score:\", f1)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uI2vGcNLo0Vp"
      },
      "source": [
        "### Some advanced grid search"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BWDGf_CrhXqf"
      },
      "outputs": [],
      "source": [
        "def build_model(hp):\n",
        "    model = Sequential([\n",
        "        Embedding(input_dim=vocab_size, output_dim=512, input_length=2123),\n",
        "        Bidirectional(LSTM(units=hp.Int('units', min_value=32, max_value=512, step=32),\n",
        "                           dropout=hp.Float('dropout', min_value=0.1, max_value=0.5, step=0.1),\n",
        "                           return_sequences=True)),\n",
        "        BatchNormalization(),\n",
        "        AttentionLayer(),\n",
        "        Dense(1, activation='sigmoid')\n",
        "    ])\n",
        "\n",
        "    optimizer = Adam(\n",
        "        learning_rate=hp.Float('learning_rate', min_value=1e-4, max_value=1e-2, sampling='LOG'),\n",
        "        beta_1=hp.Float('beta_1', min_value=0.85, max_value=0.95, step=0.01),\n",
        "        beta_2=hp.Float('beta_2', min_value=0.990, max_value=0.999, step=0.001),\n",
        "        epsilon=hp.Float('epsilon', min_value=1e-8, max_value=1e-6, sampling='LOG')\n",
        "    )\n",
        "    model.compile(optimizer=optimizer,\n",
        "                  loss='binary_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "    return model\n",
        "\n",
        "tuner = kt.Hyperband(\n",
        "    build_model,\n",
        "    objective='val_accuracy',\n",
        "    max_epochs=50,\n",
        "    directory='my_dir',\n",
        "    project_name='intro_to_kt_optimizer_tuning'\n",
        ")\n",
        "\n",
        "stop_early = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)\n",
        "tuner.search(X_train, y_train, epochs=50, validation_split=0.2, callbacks=[stop_early])\n",
        "best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]\n",
        "\n",
        "print(f\"\"\"\n",
        "Optimal number of units: {best_hps.get('units')}\n",
        "Optimal dropout rate: {best_hps.get('dropout')}\n",
        "Optimal learning rate: {best_hps.get('learning_rate')}\n",
        "Optimal beta_1: {best_hps.get('beta_1')}\n",
        "Optimal beta_2: {best_hps.get('beta_2')}\n",
        "Optimal epsilon: {best_hps.get('epsilon')}\n",
        "\"\"\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9BM4xwAnoauy"
      },
      "outputs": [],
      "source": [
        "model = tuner.hypermodel.build(best_hps)\n",
        "model.fit(X_train, y_train, epochs=50, validation_split=0.2)\n",
        "\n",
        "predictions = model.predict(X_test)\n",
        "binary_predictions = (predictions > 0.5).astype(int)\n",
        "\n",
        "# Calculate metrics\n",
        "accuracy = accuracy_score(y_test, binary_predictions)\n",
        "precision = precision_score(y_test, binary_predictions)\n",
        "recall = recall_score(y_test, binary_predictions)\n",
        "f1 = f1_score(y_test, binary_predictions)\n",
        "\n",
        "# Print the results\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"Precision:\", precision)\n",
        "print(\"Recall:\", recall)\n",
        "print(\"F1 Score:\", f1)\n",
        "\n",
        "# Confusion Matrix\n",
        "conf_matrix = confusion_matrix(y_test, binary_predictions)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fmW05pW3snmq"
      },
      "source": [
        "## Multi-class classification\n",
        "Here we will address the problem of multi-class classification, i.e. we will classify each recipe as belonging to one or more dietary restrictions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XfHEy6eeo0Vq"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K47_mVfovBHk"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(recipes_sequences, labels_multi, test_size=0.3, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-27T10:39:24.531018Z",
          "iopub.status.busy": "2024-05-27T10:39:24.530370Z",
          "iopub.status.idle": "2024-05-27T10:39:24.558046Z",
          "shell.execute_reply": "2024-05-27T10:39:24.557337Z",
          "shell.execute_reply.started": "2024-05-27T10:39:24.530986Z"
        },
        "id": "HiKHqqMYxMCo",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "embedding_dim = 512\n",
        "lstm_units = 1\n",
        "num_labels = 6\n",
        "\n",
        "model = Sequential([\n",
        "    Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=input_length),\n",
        "    Bidirectional(LSTM(lstm_units, dropout=0.3, return_sequences=True)),  # Ensure return_sequences is True for attention\n",
        "    BatchNormalization(),\n",
        "    AttentionLayer(),\n",
        "    Dense(num_labels, activation='sigmoid')  # num_labels is the number of output classes\n",
        "])\n",
        "\n",
        "model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-05-27T10:40:09.928058Z",
          "iopub.status.busy": "2024-05-27T10:40:09.927704Z",
          "iopub.status.idle": "2024-05-27T13:37:04.360876Z",
          "shell.execute_reply": "2024-05-27T13:37:04.359886Z",
          "shell.execute_reply.started": "2024-05-27T10:40:09.928030Z"
        },
        "id": "WAyAgrK0yAOF",
        "outputId": "584c28f9-c1bf-417c-9d01-9424b3344286",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m891/891\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1060s\u001b[0m 1s/step - accuracy: 0.1622 - loss: 0.5603 - val_accuracy: 0.6102 - val_loss: 0.3422\n",
            "Epoch 2/10\n",
            "\u001b[1m891/891\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1056s\u001b[0m 1s/step - accuracy: 0.6140 - loss: 0.3241 - val_accuracy: 0.6018 - val_loss: 0.2750\n",
            "Epoch 3/10\n",
            "\u001b[1m891/891\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1053s\u001b[0m 1s/step - accuracy: 0.6422 - loss: 0.2463 - val_accuracy: 0.6127 - val_loss: 0.2343\n",
            "Epoch 4/10\n",
            "\u001b[1m891/891\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1054s\u001b[0m 1s/step - accuracy: 0.6582 - loss: 0.2136 - val_accuracy: 0.6293 - val_loss: 0.2203\n",
            "Epoch 5/10\n",
            "\u001b[1m891/891\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1056s\u001b[0m 1s/step - accuracy: 0.6732 - loss: 0.1920 - val_accuracy: 0.6700 - val_loss: 0.2132\n",
            "Epoch 6/10\n",
            "\u001b[1m891/891\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1054s\u001b[0m 1s/step - accuracy: 0.6797 - loss: 0.1777 - val_accuracy: 0.6388 - val_loss: 0.2090\n",
            "Epoch 7/10\n",
            "\u001b[1m891/891\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1108s\u001b[0m 1s/step - accuracy: 0.6845 - loss: 0.1714 - val_accuracy: 0.5939 - val_loss: 0.2116\n",
            "Epoch 8/10\n",
            "\u001b[1m891/891\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1055s\u001b[0m 1s/step - accuracy: 0.6829 - loss: 0.1640 - val_accuracy: 0.6334 - val_loss: 0.2102\n",
            "Epoch 9/10\n",
            "\u001b[1m891/891\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1059s\u001b[0m 1s/step - accuracy: 0.6758 - loss: 0.1563 - val_accuracy: 0.6978 - val_loss: 0.2238\n",
            "Epoch 10/10\n",
            "\u001b[1m891/891\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1059s\u001b[0m 1s/step - accuracy: 0.6679 - loss: 0.1555 - val_accuracy: 0.6430 - val_loss: 0.2190\n",
            "Restoring model weights from the end of the best epoch: 6.\n"
          ]
        }
      ],
      "source": [
        "early_stopping = EarlyStopping(monitor='val_loss', min_delta=0.001, patience=5, verbose=1, mode='min', restore_best_weights=True)\n",
        "history = model.fit(X_train, y_train, validation_split=0.2, epochs=10, callbacks=[early_stopping])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "execution": {
          "iopub.execute_input": "2024-05-27T14:03:14.294111Z",
          "iopub.status.busy": "2024-05-27T14:03:14.293366Z",
          "iopub.status.idle": "2024-05-27T14:03:14.847184Z",
          "shell.execute_reply": "2024-05-27T14:03:14.846295Z",
          "shell.execute_reply.started": "2024-05-27T14:03:14.294076Z"
        },
        "id": "AFOLxp2p6awa",
        "outputId": "2dd6129e-8da6-420b-c152-8d686aaa7d40",
        "trusted": true
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LikNiGP6o0Vs"
      },
      "source": [
        "#### Macro Average"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-05-27T14:03:18.235146Z",
          "iopub.status.busy": "2024-05-27T14:03:18.234795Z",
          "iopub.status.idle": "2024-05-27T14:05:13.423319Z",
          "shell.execute_reply": "2024-05-27T14:05:13.422223Z",
          "shell.execute_reply.started": "2024-05-27T14:03:18.235117Z"
        },
        "id": "ysyk7CiX6awb",
        "outputId": "15f71a3c-e0f5-47bd-c3ea-5c2ec6d42836",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m478/478\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m115s\u001b[0m 240ms/step\n",
            "Accuracy: 0.6207190098880231\n",
            "Precision (macro): 0.5061810393712823\n",
            "Recall (macro): 0.3940240067837122\n",
            "F1 Score (macro): 0.4090346161846749\n",
            "Confusion Matrix for label Vegetarian&Dessert:\n",
            "[[7282  362]\n",
            " [ 354 7273]]\n",
            "\n",
            "Confusion Matrix for label Low Fat:\n",
            "[[13582    59]\n",
            " [ 1540    90]]\n",
            "\n",
            "Confusion Matrix for label Low Sodium:\n",
            "[[11262   818]\n",
            " [ 1775  1416]]\n",
            "\n",
            "Confusion Matrix for label Low Carb:\n",
            "[[14580     0]\n",
            " [  691     0]]\n",
            "\n",
            "Confusion Matrix for label Gluten Free:\n",
            "[[7785 1061]\n",
            " [ 568 5857]]\n",
            "\n",
            "Confusion Matrix for label Dairy Free:\n",
            "[[15218     0]\n",
            " [   53     0]]\n",
            "\n",
            "Average Confusion Matrix:\n",
            " [[11618.16666667   383.33333333]\n",
            " [  830.16666667  2439.33333333]]\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is 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": [
        "predictions = model.predict(X_test)\n",
        "binary_predictions = (predictions > 0.5).astype(int)\n",
        "accuracy = accuracy_score(y_test, binary_predictions)\n",
        "precision = precision_score(y_test, binary_predictions, average='macro')\n",
        "recall = recall_score(y_test, binary_predictions, average='macro')\n",
        "f1 = f1_score(y_test, binary_predictions, average='macro')\n",
        "\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"Precision (macro):\", precision)\n",
        "print(\"Recall (macro):\", recall)\n",
        "print(\"F1 Score (macro):\", f1)\n",
        "\n",
        "conf_matrices = []\n",
        "for i in range(y_test.shape[1]):\n",
        "    conf_matrix = confusion_matrix(y_test.iloc[:, i], binary_predictions[:, i])\n",
        "    conf_matrices.append(conf_matrix)\n",
        "    print(f\"Confusion Matrix for label {y_test.columns[i]}:\\n{conf_matrix}\\n\")\n",
        "avg_conf_matrix = np.mean(conf_matrices, axis=0)\n",
        "print(\"Average Confusion Matrix:\\n\", avg_conf_matrix)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FTo1a9sVo0Vs"
      },
      "source": [
        "#### Micro Average"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-05-27T14:07:11.190326Z",
          "iopub.status.busy": "2024-05-27T14:07:11.189536Z",
          "iopub.status.idle": "2024-05-27T14:09:06.308272Z",
          "shell.execute_reply": "2024-05-27T14:09:06.307362Z",
          "shell.execute_reply.started": "2024-05-27T14:07:11.190294Z"
        },
        "id": "T31i-gTPyIb7",
        "outputId": "2e622049-f757-4eeb-f2dd-734e31d5f042",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m478/478\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m115s\u001b[0m 240ms/step\n",
            "Accuracy: 0.6207190098880231\n",
            "Precision (micro): 0.8641946150212565\n",
            "Recall (micro): 0.7460875771014936\n",
            "F1 Score (micro): 0.8008097830547425\n",
            "Confusion Matrix for label Vegetarian&Dessert:\n",
            "[[7282  362]\n",
            " [ 354 7273]]\n",
            "\n",
            "Confusion Matrix for label Low Fat:\n",
            "[[13582    59]\n",
            " [ 1540    90]]\n",
            "\n",
            "Confusion Matrix for label Low Sodium:\n",
            "[[11262   818]\n",
            " [ 1775  1416]]\n",
            "\n",
            "Confusion Matrix for label Low Carb:\n",
            "[[14580     0]\n",
            " [  691     0]]\n",
            "\n",
            "Confusion Matrix for label Gluten Free:\n",
            "[[7785 1061]\n",
            " [ 568 5857]]\n",
            "\n",
            "Confusion Matrix for label Dairy Free:\n",
            "[[15218     0]\n",
            " [   53     0]]\n",
            "\n",
            "Average Confusion Matrix:\n",
            " [[11618.16666667   383.33333333]\n",
            " [  830.16666667  2439.33333333]]\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict(X_test)\n",
        "binary_predictions = (predictions > 0.5).astype(int)\n",
        "accuracy = accuracy_score(y_test, binary_predictions)\n",
        "precision = precision_score(y_test, binary_predictions, average='micro')\n",
        "recall = recall_score(y_test, binary_predictions, average='micro')\n",
        "f1 = f1_score(y_test, binary_predictions, average='micro')\n",
        "\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"Precision (micro):\", precision)\n",
        "print(\"Recall (micro):\", recall)\n",
        "print(\"F1 Score (micro):\", f1)\n",
        "\n",
        "conf_matrices = []\n",
        "for i in range(y_test.shape[1]):\n",
        "    conf_matrix = confusion_matrix(y_test.iloc[:, i], binary_predictions[:, i])\n",
        "    conf_matrices.append(conf_matrix)\n",
        "    print(f\"Confusion Matrix for label {y_test.columns[i]}:\\n{conf_matrix}\\n\")\n",
        "avg_conf_matrix = np.mean(conf_matrices, axis=0)\n",
        "print(\"Average Confusion Matrix:\\n\", avg_conf_matrix)"
      ]
    }
  ],
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