Giovanni_&_Oscar_Twitter_Disasters_Final_Report.ipynb

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      "source": [
        "# Twitter Disasters Final Report | April 2023"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Authors\n",
        "\n",
        "- [Giovanni Rivera](https://github.com/grivera64); Intro to Machine Learning, Section A\n",
        "- [Oscar Jesus Zambrano](https://github.com/osc-zam22); Intro to Machine Learning, Section B"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "WPFnCCd7O6fN"
      },
      "source": [
        "#### Setup Dependencies\n"
      ]
    },
    {
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          "base_uri": "https://localhost:8080/"
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      "outputs": [
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          "text": [
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Requirement already satisfied: pydot in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (1.4.2)\n",
            "Requirement already satisfied: pyparsing>=2.1.4 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from pydot) (3.0.9)\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "# Ensure that we have the newest version of pip installed\n",
        "%pip install -q --upgrade pip\n",
        "\n",
        "# Install necessary libraries\n",
        "%pip install -q numpy\n",
        "%pip install -q pandas\n",
        "%pip install -q matplotlib\n",
        "%pip install -q seaborn\n",
        "%pip install -q plotly\n",
        "\n",
        "# Helps avoid showing plots in a separate line\n",
        "%matplotlib inline\n",
        "\n",
        "%pip install -q scikit-learn\n",
        "%pip install -q tensorflow\n",
        "\n",
        "# Helps run plot_model from keras\n",
        "%pip install pydot\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "ycGSQ31nQRa8"
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      "source": [
        "# Import the modules\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.graph_objs as plotly\n",
        "from sklearn import datasets\n",
        "\n",
        "# Set the styling of the plt plots to darkgrid\n",
        "sns.set_style('darkgrid')\n",
        "\n",
        "# Removes error messsages and sets precision to 3 decimal places\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "np.set_printoptions(precision=3, suppress=True)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 1. Introduction"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Our Story:\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In light of recent events around the globe, we have seen the rise of misinformation being spread on social media. In order to combat this we are using real tweets from real users on Twitter to find a way to stop misinformation from spreading, and promote real useful information to those living in or have relatives in affected areas."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Our Task:\n",
        "\n",
        "We are designing machine learning models using Tensorflow/Keras to identify whether a given tweet is a natural distaster.\n",
        "\n",
        "Throughout the notebook, we will refer to tweets about a natural distaster as part of the `postive class`, while tweets that aren't about a natural disaster as part of the `negative class`."
      ]
    },
    {
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      "source": [
        "#### 1.1 Load the Data in a Colab Notebook\n",
        "\n",
        "We use a dataset from the Kaggle competition [Tech Exchange 2023 ML Project](https://www.kaggle.com/competitions/techexchange-2023-ml-project).\n",
        "\n",
        "You may find the dataset by navigating to the 'Data' tab in the link aforementioned. Under 'Data Explorer', you can download the following files:\n",
        "\n",
        "- `train.csv`: Contains the training data; we will use this dataset for our training and validation data.\n",
        "- `test.csv`: Contains our testing data; we will use this dataset for making our submissions to Kaggle."
      ]
    },
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              "      <th>id</th>\n",
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              "   id keyword location                                               text   \n",
              "0   1     NaN      NaN  Our Deeds are the Reason of this #earthquake M...  \\\n",
              "1   4     NaN      NaN             Forest fire near La Ronge Sask. Canada   \n",
              "2   5     NaN      NaN  All residents asked to 'shelter in place' are ...   \n",
              "3   6     NaN      NaN  13,000 people receive #wildfires evacuation or...   \n",
              "4   7     NaN      NaN  Just got sent this photo from Ruby #Alaska as ...   \n",
              "\n",
              "   target  \n",
              "0       1  \n",
              "1       1  \n",
              "2       1  \n",
              "3       1  \n",
              "4       1  "
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      "source": [
        "# Please update the Path here to the location of your train.csv and test.csv files\n",
        "path_to_csv = 'Data/techexchange-2023-ml-project'\n",
        "\n",
        "# Load the Data Frames from the Training and Testing Data Frame\n",
        "train_df = pd.read_csv(f'{path_to_csv}/train.csv')\n",
        "test_df = pd.read_csv(f'{path_to_csv}/test.csv')\n",
        "display(train_df.head())"
      ]
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              "      <th>text</th>\n",
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              "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
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              "   id keyword location                                               text\n",
              "0   1     NaN      NaN  Our Deeds are the Reason of this #earthquake M...\n",
              "1   4     NaN      NaN             Forest fire near La Ronge Sask. Canada\n",
              "2   5     NaN      NaN  All residents asked to 'shelter in place' are ...\n",
              "3   6     NaN      NaN  13,000 people receive #wildfires evacuation or...\n",
              "4   7     NaN      NaN  Just got sent this photo from Ruby #Alaska as ..."
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              "   target\n",
              "0       1\n",
              "1       1\n",
              "2       1\n",
              "3       1\n",
              "4       1"
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      "source": [
        "# Separate Data Frame for input and outputs\n",
        "\n",
        "input_names = ['id', 'keyword', 'location', 'text']\n",
        "input_df = train_df[input_names]\n",
        "display(input_df.head())\n",
        "\n",
        "output_names = ['target']\n",
        "output_df = train_df[output_names]\n",
        "display(output_df.head())"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 1.2 Convert our data into numpy arrays for usage in our ML models.\n",
        "\n",
        "Numpy will help us take full advantage of our GPU power to quickly perform training operations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1 nan nan\n",
            "  'Our Deeds are the Reason of this #earthquake May ALLAH Forgive us all']\n",
            " [4 nan nan 'Forest fire near La Ronge Sask. Canada']\n",
            " [5 nan nan\n",
            "  \"All residents asked to 'shelter in place' are being notified by officers. No other evacuation or shelter in place orders are expected\"]\n",
            " [6 nan nan\n",
            "  '13,000 people receive #wildfires evacuation orders in California ']\n",
            " [7 nan nan\n",
            "  'Just got sent this photo from Ruby #Alaska as smoke from #wildfires pours into a school ']]\n",
            "[1 1 1 1 1]\n",
            "(7613, 4)\n",
            "(7613,)\n"
          ]
        }
      ],
      "source": [
        "# Convert into numpy data\n",
        "X_data = input_df.to_numpy()\n",
        "Y_data = output_df.to_numpy().flatten()\n",
        "\n",
        "print(X_data[:5])\n",
        "print(Y_data[:5])\n",
        "\n",
        "print(X_data.shape)\n",
        "print(Y_data.shape)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 1.3 Split the training data into 90% training and 10% for validation.\n",
        "\n",
        "We use Sklearn's train_test_split to split our `train.csv` dataset to create a 90:10 testing/validation split to use further down this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the data into train and test\n",
        "X_train, X_validation, Y_train, Y_validation = train_test_split(X_data, Y_data, train_size=0.90)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Text\n",
            "[4917 'exploded' nan\n",
            " 'Im Dead!!! My two Loves in 1 photo! My Heart exploded into a Million Pieces!!!  ?????????????? @BrandonSkeie @samsmithworld http://t.co/yEtagC2d8A']\n",
            "\n",
            "Is Natural Disaster?\n",
            "0 No\n",
            "======================\n",
            "\n",
            "Text\n",
            "[8280 'rioting' 'heart of darkness, unholy ?'\n",
            " \"@Georgous__ what alternatives? Legal alternatives? Protesting? Rioting may not be the most peaceful thing but it's a demonstration of how\"]\n",
            "\n",
            "Is Natural Disaster?\n",
            "0 No\n",
            "======================\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Going through a few examples in the training split\n",
        "\n",
        "for index in range(2):\n",
        "    print('Text')\n",
        "    print(X_train[index])\n",
        "    print()\n",
        "    \n",
        "    print('Is Natural Disaster?')\n",
        "    print(Y_train[index], 'Yes' if Y_train[index] == 1 else 'No')\n",
        "    print('======================')\n",
        "    print()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2. Baseline"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 2.1 Create a Simple Baseline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Returns a positive result, regardless of the input\n",
        "def baseline_model(text_inputs):\n",
        "    return 1\n",
        "\n",
        "# Vectorized version of the method to apply to numpy arrays properly\n",
        "baseline_model_np = np.vectorize(baseline_model, signature='(n) -> ()')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input:\n",
            "[4917 'exploded' nan\n",
            " 'Im Dead!!! My two Loves in 1 photo! My Heart exploded into a Million Pieces!!!  ?????????????? @BrandonSkeie @samsmithworld http://t.co/yEtagC2d8A']\n",
            "\n",
            "Output\n",
            "0\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[8280 'rioting' 'heart of darkness, unholy ?'\n",
            " \"@Georgous__ what alternatives? Legal alternatives? Protesting? Rioting may not be the most peaceful thing but it's a demonstration of how\"]\n",
            "\n",
            "Output\n",
            "0\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[4606 'emergency%20services' 'London, UK'\n",
            " 'I am not an American but I have family who have served in the military work in the emergency services and work in... http://t.co/Pl2VzLrKVK']\n",
            "\n",
            "Output\n",
            "1\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[7668 'panic' 'Topeka, KS'\n",
            " \"The good thing is that the #Royals won't face a newbie in the playoffs. No real reason to panic.\"]\n",
            "\n",
            "Output\n",
            "0\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[7397 'obliterated' 'Valparaiso '\n",
            " 'RIZZO IS ON ???????? THAT BALL WAS OBLITERATED']\n",
            "\n",
            "Output\n",
            "0\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Testing the baseline on the \n",
        "baseline_predictions_train = baseline_model_np(X_train)\n",
        "\n",
        "for i in range(5):\n",
        "    print('Input:')\n",
        "    print(X_train[i])\n",
        "    print()\n",
        "\n",
        "    print('Output')\n",
        "    print(Y_train[i])\n",
        "    print()\n",
        "\n",
        "    print('Prediction')\n",
        "    print(baseline_predictions_train[i])\n",
        "    print('==================')\n",
        "    print()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 2.2 Calculate the Log Loss of our baseline model\n",
        "\n",
        "Since our baseline is simple, we can use this loss value to determine whether our models are more accurate than a naive approach."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Calculates Log Loss\n",
        "def calculate_loss(labels, predictions):\n",
        "    epsilon = 0.000001  # Prevents taking the natural log of non-positive values\n",
        "    ce_values = -labels * np.log(predictions + epsilon) - (1 - labels) * np.log(1 - predictions + epsilon)\n",
        "    loss = ce_values.mean()\n",
        "    return loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Loss: 7.87469942823704\n",
            "Validation Loss: 7.923067964344554\n"
          ]
        }
      ],
      "source": [
        "# Calculate the loss on the training portion of our train data\n",
        "training_loss = calculate_loss(Y_train, baseline_predictions_train)\n",
        "print('Training Loss:', training_loss)\n",
        "\n",
        "# Calculate the loss on the validation portion our our train data\n",
        "baseline_predictions_validation = baseline_model_np(X_validation)\n",
        "testing_loss = calculate_loss(Y_validation, baseline_predictions_validation)\n",
        "print('Validation Loss:', testing_loss)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Accuracy: 0.4300102174864983\n",
            "Validation Accurracy: 0.42650918635170604\n"
          ]
        }
      ],
      "source": [
        "training_accuracy = (baseline_predictions_train == Y_train).sum() / len(Y_train)\n",
        "print(\"Training Accuracy:\", training_accuracy)\n",
        "\n",
        "validation_accuracy = (baseline_predictions_validation == Y_validation).sum() / len(Y_validation)\n",
        "print('Validation Accurracy:', validation_accuracy)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 2.3 Upload the baseline model to Kaggle\n",
        "\n",
        "We can then upload a `submission.csv` file to Kaggle to find the F1 score of our baseline model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Create the submission CSV file for our Kaggle submission\n",
        "def save_to_submissions_csv(text_inputs, prediction_labels, name='submission.csv'):\n",
        "    print(f'Generating \"{name}\" file...')\n",
        "\n",
        "    # Extract the ids of the text inputs and flatten to a 1D ndarray\n",
        "    test_ids = text_inputs[:,0].flatten()\n",
        "\n",
        "    # Write the submission file and save to 'submission.csv'\n",
        "    np.savetxt(\n",
        "        name,\n",
        "        np.rec.fromarrays([test_ids, prediction_labels]),\n",
        "        fmt=['%s', '%d'],\n",
        "        delimiter=',',\n",
        "        header='id,target',\n",
        "        comments=''\n",
        "    )\n",
        "\n",
        "    # Show success!\n",
        "    print(f'Successfully created \"{name}\"')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generating \"baseline_submission.csv\" file...\n",
            "Successfully created \"baseline_submission.csv\"\n"
          ]
        }
      ],
      "source": [
        "# Reformat the single training dataframe to an input dataframe\n",
        "input_names = ['id', 'keyword', 'location', 'text']\n",
        "test_input_df = test_df[input_names]\n",
        "\n",
        "# Reformat the input dataframe into a numpy array for running through our model\n",
        "test_input_np = test_input_df.to_numpy()\n",
        "\n",
        "# Predict by using the baseline model on the test input and save to a .csv\n",
        "baseline_predictions_test = baseline_model_np(test_input_np)\n",
        "save_to_submissions_csv(test_input_np, baseline_predictions_test, 'baseline_submission.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  target\n",
              "0   0       1\n",
              "1   2       1\n",
              "2   3       1\n",
              "3   9       1\n",
              "4  11       1"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Look at the first few predictions to ensure things went smoothly\n",
        "pd.read_csv('baseline_submission.csv').head()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 3. Data Analysis"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 3.1 Explore the frequency of common words\n",
        "\n",
        "Tweets can have all sorts of words inside of them, but we can always find common words that we can use for our vocabulary\n",
        "\n",
        "The motivation for this graph to see what type of words are commonly used within our tweets dataset to see what we should and should not include in our vocabulary."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [],
      "source": [
        "from collections import Counter\n",
        "\n",
        "# Plots the frequency of the top commonly used words in the provided tweets in\n",
        "# the positive and negative class\n",
        "def plot_frequency(tweets_np, labels_np, max_word_num=20):\n",
        "    # Creates a counter that keeps track of the frequency of words (similar to defaultdict)\n",
        "    pos_counter = Counter()\n",
        "    neg_counter = Counter()\n",
        "    tweets_np = np.copy(tweets_np) # Deep Copy of input\n",
        "\n",
        "    # Total Counter Variables\n",
        "    pos_counter_all = 0\n",
        "    neg_counter_all = 0\n",
        "\n",
        "    # Go through the tweets dataset\n",
        "    total_words = set()\n",
        "    for entry_index in range(tweets_np.shape[0]):\n",
        "        # Flatten all of the features into a single string\n",
        "        words = ' '.join([str(feature).lower() for feature in tweets_np[entry_index]])\n",
        "        # Count the frequency of each word\n",
        "        for word in words.split():\n",
        "            # Group all links as 1 token\n",
        "            if word.startswith('http'):\n",
        "                word = '<LINK>'\n",
        "            # Group all articles as 1 token\n",
        "            elif word in ['the', 'a', 'an']:\n",
        "                word = '<ARTICLE>'\n",
        "            \n",
        "            if labels_np[entry_index]:\n",
        "                pos_counter[word] += 1  # Positive entry\n",
        "                pos_counter_all += 1\n",
        "            else:\n",
        "                neg_counter[word] += 1  # Negative entry\n",
        "                neg_counter_all += 1\n",
        "\n",
        "            # For Debug purposes, saving all of the words we encounter\n",
        "            total_words.add(word)\n",
        "    \n",
        "    # Extract at most max_word_num words that are the most common words\n",
        "    # for both classes (and removes overlap)\n",
        "    top_pos_words = [word for word, _ in pos_counter.most_common(max_word_num // 2)]\n",
        "    top_neg_words = [word for word, _ in neg_counter.most_common(max_word_num // 2)]\n",
        "    top_words = set(top_pos_words + top_neg_words)\n",
        "\n",
        "    # Create a Data Frame for the collected data\n",
        "    result = {\n",
        "        'word': [word for word in top_words],\n",
        "        'pos count': [pos_counter[word] for word in top_words],\n",
        "        'neg count': [neg_counter[word] for word in top_words],\n",
        "        '% chance is pos': [(pos_counter[word] / (pos_counter[word] + neg_counter[word])) * 100 for word in top_words]\n",
        "    }\n",
        "    word_count_df = pd.DataFrame(data=result, columns=result.keys())\n",
        "    word_count_df = word_count_df.set_index('word')\n",
        "    display(word_count_df)\n",
        "    \n",
        "    # Plot a bar graph that groups pos and neg count for a few of the most used words\n",
        "    pd.concat([word_count_df[['pos count']], word_count_df[['neg count']]], axis=1).plot.bar()\n",
        "\n",
        "    print(f'DEBUG: Total Words Len: {len(total_words)}')\n",
        "    print(f'DEBUG: Total Positive Examples: {pos_counter_all}')\n",
        "    print(f'DEBUG: Total Negative Examples: {neg_counter_all}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pos count</th>\n",
              "      <th>neg count</th>\n",
              "      <th>% chance is pos</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>word</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>with</th>\n",
              "      <td>188</td>\n",
              "      <td>382</td>\n",
              "      <td>32.982456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>for</th>\n",
              "      <td>399</td>\n",
              "      <td>489</td>\n",
              "      <td>44.932432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>to</th>\n",
              "      <td>761</td>\n",
              "      <td>1195</td>\n",
              "      <td>38.905930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>-</th>\n",
              "      <td>419</td>\n",
              "      <td>396</td>\n",
              "      <td>51.411043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>and</th>\n",
              "      <td>512</td>\n",
              "      <td>927</td>\n",
              "      <td>35.580264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>&lt;ARTICLE&gt;</th>\n",
              "      <td>2426</td>\n",
              "      <td>3372</td>\n",
              "      <td>41.842014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i</th>\n",
              "      <td>292</td>\n",
              "      <td>1061</td>\n",
              "      <td>21.581670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>my</th>\n",
              "      <td>134</td>\n",
              "      <td>566</td>\n",
              "      <td>19.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>that</th>\n",
              "      <td>181</td>\n",
              "      <td>357</td>\n",
              "      <td>33.643123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>of</th>\n",
              "      <td>957</td>\n",
              "      <td>935</td>\n",
              "      <td>50.581395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>you</th>\n",
              "      <td>125</td>\n",
              "      <td>629</td>\n",
              "      <td>16.578249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>in</th>\n",
              "      <td>1186</td>\n",
              "      <td>854</td>\n",
              "      <td>58.137255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nan</th>\n",
              "      <td>1117</td>\n",
              "      <td>1479</td>\n",
              "      <td>43.027735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>new</th>\n",
              "      <td>132</td>\n",
              "      <td>292</td>\n",
              "      <td>31.132075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>after</th>\n",
              "      <td>181</td>\n",
              "      <td>75</td>\n",
              "      <td>70.703125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>&lt;LINK&gt;</th>\n",
              "      <td>2510</td>\n",
              "      <td>2196</td>\n",
              "      <td>53.336167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is</th>\n",
              "      <td>348</td>\n",
              "      <td>590</td>\n",
              "      <td>37.100213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>at</th>\n",
              "      <td>305</td>\n",
              "      <td>237</td>\n",
              "      <td>56.273063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>it</th>\n",
              "      <td>164</td>\n",
              "      <td>342</td>\n",
              "      <td>32.411067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>as</th>\n",
              "      <td>171</td>\n",
              "      <td>160</td>\n",
              "      <td>51.661631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>this</th>\n",
              "      <td>179</td>\n",
              "      <td>287</td>\n",
              "      <td>38.412017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>from</th>\n",
              "      <td>247</td>\n",
              "      <td>183</td>\n",
              "      <td>57.441860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>by</th>\n",
              "      <td>279</td>\n",
              "      <td>242</td>\n",
              "      <td>53.550864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>on</th>\n",
              "      <td>418</td>\n",
              "      <td>444</td>\n",
              "      <td>48.491879</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>be</th>\n",
              "      <td>113</td>\n",
              "      <td>287</td>\n",
              "      <td>28.250000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           pos count  neg count  % chance is pos\n",
              "word                                            \n",
              "with             188        382        32.982456\n",
              "for              399        489        44.932432\n",
              "to               761       1195        38.905930\n",
              "-                419        396        51.411043\n",
              "and              512        927        35.580264\n",
              "<ARTICLE>       2426       3372        41.842014\n",
              "i                292       1061        21.581670\n",
              "my               134        566        19.142857\n",
              "that             181        357        33.643123\n",
              "of               957        935        50.581395\n",
              "you              125        629        16.578249\n",
              "in              1186        854        58.137255\n",
              "nan             1117       1479        43.027735\n",
              "new              132        292        31.132075\n",
              "after            181         75        70.703125\n",
              "<LINK>          2510       2196        53.336167\n",
              "is               348        590        37.100213\n",
              "at               305        237        56.273063\n",
              "it               164        342        32.411067\n",
              "as               171        160        51.661631\n",
              "this             179        287        38.412017\n",
              "from             247        183        57.441860\n",
              "by               279        242        53.550864\n",
              "on               418        444        48.491879\n",
              "be               113        287        28.250000"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DEBUG: Total Words Len: 33580\n",
            "DEBUG: Total Positive Examples: 61864\n",
            "DEBUG: Total Negative Examples: 80205\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Display and plot at most 40 words from the X_data set\n",
        "plot_frequency(X_data, Y_data, max_word_num=40)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### 3.1.1\n",
        "\n",
        "After reviewing this information, there appears to be a lot of words that are partially helpful, but we don't see any keywords that we expected such as \"fire\", or other natural disaster words.\n",
        "\n",
        "As a result, we will need to have a lot of words in our vocabulary to include these helpful words as well, as the expected keywords that we expected."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 3.2 Explore the length of the input\n",
        "\n",
        "We were thinking that the number of tokens in a tweet might help with determining whether a tween is a disaster or not."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [],
      "source": [
        "from collections import defaultdict\n",
        "\n",
        "def length_plot(tweets_np , labels_np, interval_list):\n",
        "\n",
        "    # initializes maps to count based on intervals of words\n",
        "    pos_intervals = defaultdict(int)\n",
        "    neg_intervals = defaultdict(int)\n",
        "\n",
        "    # Convert the interval list into ranges for use below\n",
        "    interval_map = {}\n",
        "    for interval in interval_list:\n",
        "        # Parses interval strings into useable ranges\n",
        "        if '-' in interval:\n",
        "            start, end = map(lambda x: int(x), interval.split('-'))\n",
        "        else:\n",
        "            start, end = interval.split('+')[0], 285\n",
        "\n",
        "        interval_map[interval] = range(int(start), int(end) + 1)\n",
        "\n",
        "    # Track the counts of positive and negative inputs for each range from above\n",
        "    for entry_index in range(tweets_np.shape[0]):\n",
        "        # Flatten all of the features into a single string\n",
        "        words = ' '.join([str(feature) for feature in tweets_np[entry_index]]).split()\n",
        "\n",
        "        for interval in interval_list:\n",
        "            # Ignore words counts outside of our intervals\n",
        "            if len(words) not in interval_map[interval]:\n",
        "                continue\n",
        "\n",
        "            # Updates the counts of positive and negative entries in the dictionary\n",
        "            # based on the interval they are in\n",
        "            if labels_np[entry_index]:\n",
        "                pos_intervals[interval] += 1\n",
        "            else:\n",
        "                neg_intervals[interval] += 1\n",
        "\n",
        "    # Create a Data Frame for the collected data\n",
        "    result= {\n",
        "        'interval' : [ interval for interval in interval_list],\n",
        "        'pos intervals' : [pos_intervals[interval] for interval in interval_list],\n",
        "        'neg intervals' : [neg_intervals[interval] for interval in interval_list],\n",
        "        '% chance is pos': [(pos_intervals[interval] / (pos_intervals[interval] + neg_intervals[interval])) * 100 for interval in interval_list],\n",
        "    }\n",
        "    word_count_df = pd.DataFrame(data = result , columns=result.keys())\n",
        "    word_count_df = word_count_df.set_index('interval')\n",
        "\n",
        "    # Plot and Display the collected data\n",
        "    pd.concat([word_count_df[['pos intervals']], word_count_df[['neg intervals']]], axis=1).plot.bar()\n",
        "    display(word_count_df)\n",
        "\n",
        "    # Debugging code\n",
        "    print(neg_intervals)\n",
        "    print(pos_intervals)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pos intervals</th>\n",
              "      <th>neg intervals</th>\n",
              "      <th>% chance is pos</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>interval</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0-5</th>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>6.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6-10</th>\n",
              "      <td>164</td>\n",
              "      <td>463</td>\n",
              "      <td>26.156300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11-15</th>\n",
              "      <td>740</td>\n",
              "      <td>1017</td>\n",
              "      <td>42.117245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16-20</th>\n",
              "      <td>1041</td>\n",
              "      <td>1165</td>\n",
              "      <td>47.189483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21-25</th>\n",
              "      <td>973</td>\n",
              "      <td>1010</td>\n",
              "      <td>49.067070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26-30</th>\n",
              "      <td>317</td>\n",
              "      <td>552</td>\n",
              "      <td>36.478711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31-35</th>\n",
              "      <td>34</td>\n",
              "      <td>106</td>\n",
              "      <td>24.285714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36+</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          pos intervals  neg intervals  % chance is pos\n",
              "interval                                               \n",
              "0-5                   2             28         6.666667\n",
              "6-10                164            463        26.156300\n",
              "11-15               740           1017        42.117245\n",
              "16-20              1041           1165        47.189483\n",
              "21-25               973           1010        49.067070\n",
              "26-30               317            552        36.478711\n",
              "31-35                34            106        24.285714\n",
              "36+                   0              1         0.000000"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "defaultdict(<class 'int'>, {'6-10': 463, '0-5': 28, '16-20': 1165, '11-15': 1017, '21-25': 1010, '26-30': 552, '31-35': 106, '36+': 1})\n",
            "defaultdict(<class 'int'>, {'16-20': 1041, '6-10': 164, '21-25': 973, '11-15': 740, '26-30': 317, '31-35': 34, '0-5': 2, '36+': 0})\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plots the count of positive and negative classes of X_data based on the given intervals\n",
        "intervals = ['0-5', '6-10' , '11-15' , '16-20' , '21-25' , '26-30' , '31-35', '36+']\n",
        "length_plot(X_data, Y_data, intervals)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### 3.2.1\n",
        "\n",
        "It looks like there is a fair distribution based on the number of words in the tweet.\n",
        "\n",
        "After viewing this graph, we figured that it may be best to not include the number of words in our model inputs. Bucketing the tweets based on these values may not be worth the extra model complexity."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 4. Our Experiments"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We were able to come up with three differing models using different algorithms learned in class, each varying in complexity. various differing variations of each model were ran however we kept the best versions of each model along with an explanation of how we reached that point"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 4.1 Normalize our data\n",
        "\n",
        "Since tweets can contain lots of punctuation, we want to specify a standardization to sanitize our inputs. In addition, all links are changed to <LINK> as a way of reducing the creation of unique features. \n",
        "> **NOTE** Though standarization helps ensure that our model works for unusual input, there may be a lost \n",
        "> of information when applying normalizations."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### 4.1.1 Normalization Technique 1: Remove punctuation and Links"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DEBUG: i am cool <LINK> <LINK>\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=string, numpy=b'i am cool <LINK> <LINK>'>"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Stardardizes the input\n",
        "def normalize_punctuation_and_links(tweets_text, show_debug=False):\n",
        "    # Make all letters lowercase\n",
        "    result_tensor = tf.strings.lower(tweets_text)\n",
        "\n",
        "    # Replace links with <LINK> token\n",
        "    link_regex = r'(https?:\\/\\/)([\\da-z\\.-]+)\\.([a-z\\.]{2,6})([\\/\\w\\.-]*)'\n",
        "    result_tensor = tf.strings.regex_replace(result_tensor, link_regex, '<LINK>')\n",
        "\n",
        "    # Remove punctuation (but don't remove <> from our Link tokens)\n",
        "    punctuation_regex = r'[^\\w\\s<>]'\n",
        "    result_tensor = tf.strings.regex_replace(result_tensor, punctuation_regex, ' ')\n",
        "\n",
        "    # Remove extra spaces\n",
        "    multi_space_regex = r'\\s{2,}'\n",
        "    result_tensor = tf.strings.regex_replace(result_tensor, multi_space_regex, ' ')\n",
        "\n",
        "    if show_debug:\n",
        "        print('DEBUG: ', end='')\n",
        "        tf.print(result_tensor)\n",
        "\n",
        "    return result_tensor\n",
        "\n",
        "normalize_punctuation_and_links('I.am.cool  http://www.example.com, https://github.com/example', show_debug=True)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 4.2 Build Our Model(s)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### 4.2.1 FFNN Model (Bag Of Embeddings)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "##### 4.2.1.1\n",
        "Our first model is a fairly simple FFNN. We used the concept of \"Bag of Embeddings\", where we use embeddings with dim=3 to try to identify which words are related to each other.\n",
        "\n",
        "A problem with this model is that, as the name suggests, the words are not in any particular order (just in a messy \"bag\"), but there is a lot of information that the model can learn without word order."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [],
      "source": [
        "def build_ffnn_model(tweets_np, max_vocab, max_tokens, embedding_dim):\n",
        "    # Remove randomness\n",
        "    tf.keras.backend.clear_session()\n",
        "    tf.keras.utils.set_random_seed(0)\n",
        "    \n",
        "    # Build the normalization layer and vocabulary ahead of time\n",
        "    norm_layer = tf.keras.layers.TextVectorization(\n",
        "        max_tokens,\n",
        "        standardize=normalize_punctuation_and_links,\n",
        "        split='whitespace',\n",
        "        output_mode='int',\n",
        "        encoding='utf-8',\n",
        "        name='Normalization_Layer',\n",
        "        output_sequence_length=max_tokens,\n",
        "    )\n",
        "    norm_layer.adapt(tweets_np, batch_size=64)\n",
        "\n",
        "    # display(norm_layer.get_vocabulary())\n",
        "\n",
        "    # Build our FFNN Model using embeddings and average pooling\n",
        "    model = tf.keras.models.Sequential()\n",
        "    model.add(tf.keras.Input(shape=(1,), dtype=tf.string))\n",
        "    model.add(norm_layer)\n",
        "    model.add(tf.keras.layers.Embedding(\n",
        "        input_dim=max_vocab,\n",
        "        output_dim=embedding_dim,\n",
        "        input_length=max_tokens,\n",
        "        name='Embeddings_Layer',\n",
        "    ))\n",
        "    # max pool layer\n",
        "    model.add(tf.keras.layers.GlobalMaxPooling1D(\n",
        "        name='Max_Pool_Layer'\n",
        "    ))\n",
        "    # two dense layer with a sigmoid activation\n",
        "    model.add(tf.keras.layers.Dense(\n",
        "        units=128,  \n",
        "        activation='sigmoid',\n",
        "        name='Dense_Layer_1',\n",
        "    ))\n",
        "    model.add(tf.keras.layers.Dense(\n",
        "        units=64,\n",
        "        activation='sigmoid',\n",
        "        name='Dense_Layer_2',\n",
        "    ))\n",
        "\n",
        "    # output layer with a sigmoid activation \n",
        "    model.add(tf.keras.layers.Dense(\n",
        "        units=1,\n",
        "        activation='sigmoid',\n",
        "        name='Output_Layer',\n",
        "    ))\n",
        "\n",
        "    # Compile and return the model\n",
        "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "    return model"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "A large vocabulary with seems to have a direct benefit to the model as more features can be identified. An input of 300 with a dimension of 12 was also found to have a good impact through tedious trial and error. The biggest obstacle was finding the correct amount of training. This model was very prone to overfitting data if it was trained past 11 epochs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " Normalization_Layer (TextVe  (None, 300)              0         \n",
            " ctorization)                                                    \n",
            "                                                                 \n",
            " Embeddings_Layer (Embedding  (None, 300, 12)          420000    \n",
            " )                                                               \n",
            "                                                                 \n",
            " Max_Pool_Layer (GlobalMaxPo  (None, 12)               0         \n",
            " oling1D)                                                        \n",
            "                                                                 \n",
            " Dense_Layer_1 (Dense)       (None, 128)               1664      \n",
            "                                                                 \n",
            " Dense_Layer_2 (Dense)       (None, 64)                8256      \n",
            "                                                                 \n",
            " Output_Layer (Dense)        (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 429,985\n",
            "Trainable params: 429,985\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "ffnn_model = build_ffnn_model(X_train[:,3], 35000, 300, 12)\n",
        "display(ffnn_model.summary())"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### 4.2.2 CNN Model"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "##### 4.2.2.1\n",
        "The second model made was a CNN. This model is also fairly standard. This model applies filters along the features to detect any patterns that are useful for our predictions. We still use embeddings since these is useful for identifying if words are related.\n",
        "\n",
        "Giovanni put this together and got it running with originally 2 conv1D layers, which later concantenated together."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [],
      "source": [
        "def build_cnn_model(tweets_np, max_vocab, max_tokens, embedding_dim):\n",
        "    # Remove randomness\n",
        "    tf.keras.backend.clear_session()\n",
        "    tf.keras.utils.set_random_seed(0)\n",
        "    \n",
        "    # Build the normalization layer ahead of time\n",
        "    norm_layer = tf.keras.layers.TextVectorization(\n",
        "        max_tokens,\n",
        "        standardize=normalize_punctuation_and_links,\n",
        "        split='whitespace',\n",
        "        output_mode='int',\n",
        "        encoding='utf-8',\n",
        "        name='Normalization_Layer',\n",
        "        output_sequence_length=max_tokens,\n",
        "    )\n",
        "    norm_layer.adapt(tweets_np, batch_size=64)\n",
        "\n",
        "    # Build our CNN Model using Keras' Functional API\n",
        "    input_layer = tf.keras.Input(shape=(1,), dtype=tf.string, name='Input Layer')\n",
        "    norm_layer = norm_layer(input_layer)\n",
        "    embedding_layer = tf.keras.layers.Embedding(\n",
        "        input_dim=max_vocab,\n",
        "        output_dim=embedding_dim,\n",
        "        input_length=max_tokens,\n",
        "        name='Embeddings_Layer',\n",
        "    )(norm_layer)\n",
        "    \n",
        "    # three conv1D layers, filter sizes increase as it progresses\n",
        "    conv_1 = tf.keras.layers.Conv1D(\n",
        "        filters=16,\n",
        "        kernel_size=3,\n",
        "        padding='same',\n",
        "        activation='relu',\n",
        "        name='Conv_Layer_1_1',\n",
        "    )(embedding_layer)\n",
        "    conv_2 = tf.keras.layers.Conv1D(\n",
        "        filters=16,\n",
        "        kernel_size=4,\n",
        "        padding='same',\n",
        "        activation='relu',\n",
        "        name='Conv_Layer_1_2',\n",
        "    )(embedding_layer)\n",
        "    conv_3 = tf.keras.layers.Conv1D(\n",
        "        filters=16,\n",
        "        kernel_size=5,\n",
        "        padding='same',\n",
        "        activation='relu',\n",
        "        name='Conv_Layer_1_3',\n",
        "    )(embedding_layer)\n",
        "\n",
        "    # combines layers into single layers (Dropout adds some randomness, while MaxPool1D helps shrink our layers)\n",
        "    concat_layer = tf.keras.layers.Concatenate(name='Concatenate_Layer')([conv_1, conv_2, conv_3])\n",
        "    dropout_layer = tf.keras.layers.Dropout(rate=0.05)(concat_layer)\n",
        "    max_pool_layer = tf.keras.layers.MaxPool1D(pool_size=max_tokens, name='Max_Pool_Layer')(dropout_layer)\n",
        "    flatten_layer = tf.keras.layers.Flatten(name='Flatten_Layer_1')(max_pool_layer)\n",
        "    \n",
        "    # hidden layer of size 32, sigmoid\n",
        "    hidden_layer = tf.keras.layers.Dense(\n",
        "        units=32,\n",
        "        activation='sigmoid',\n",
        "        name='Hidden_Layer_1',\n",
        "    )(flatten_layer)\n",
        "\n",
        "    # output layer\n",
        "    output_layer = tf.keras.layers.Dense(\n",
        "        units=1,\n",
        "        activation='sigmoid',\n",
        "        name='Output_Layer',\n",
        "    )(hidden_layer)\n",
        "\n",
        "    # adds input and output layer to model\n",
        "    model = tf.keras.Model(\n",
        "        inputs=input_layer,\n",
        "        outputs=output_layer,\n",
        "        name='CNN_Model',\n",
        "    )\n",
        "\n",
        "    # Compile and return the model\n",
        "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "    return model"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "##### 4.2.2.2\n",
        "\n",
        "As in the previous model, a larger vocab size, and embedding Layer (input/dimension), had a huge boost. This may be due to using the same preprocessing funcion across the board. We first started with just 2 convolutional layers. After doing some research online, and seeing that common practice was to use a smaller filter size and increase through each of the convolutional layers, we had some better success. Our reasoning behind this choice was that the context of the input is widened as it is processed through the model. Our final change was adding one more convolutional layer, which brought the total of those type of layers to three.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"CNN_Model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " Input Layer (InputLayer)       [(None, 1)]          0           []                               \n",
            "                                                                                                  \n",
            " Normalization_Layer (TextVecto  (None, 300)         0           ['Input Layer[0][0]']            \n",
            " rization)                                                                                        \n",
            "                                                                                                  \n",
            " Embeddings_Layer (Embedding)   (None, 300, 9)       180000      ['Normalization_Layer[0][0]']    \n",
            "                                                                                                  \n",
            " Conv_Layer_1_1 (Conv1D)        (None, 300, 16)      448         ['Embeddings_Layer[0][0]']       \n",
            "                                                                                                  \n",
            " Conv_Layer_1_2 (Conv1D)        (None, 300, 16)      592         ['Embeddings_Layer[0][0]']       \n",
            "                                                                                                  \n",
            " Conv_Layer_1_3 (Conv1D)        (None, 300, 16)      736         ['Embeddings_Layer[0][0]']       \n",
            "                                                                                                  \n",
            " Concatenate_Layer (Concatenate  (None, 300, 48)     0           ['Conv_Layer_1_1[0][0]',         \n",
            " )                                                                'Conv_Layer_1_2[0][0]',         \n",
            "                                                                  'Conv_Layer_1_3[0][0]']         \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 300, 48)      0           ['Concatenate_Layer[0][0]']      \n",
            "                                                                                                  \n",
            " Max_Pool_Layer (MaxPooling1D)  (None, 1, 48)        0           ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            " Flatten_Layer_1 (Flatten)      (None, 48)           0           ['Max_Pool_Layer[0][0]']         \n",
            "                                                                                                  \n",
            " Hidden_Layer_1 (Dense)         (None, 32)           1568        ['Flatten_Layer_1[0][0]']        \n",
            "                                                                                                  \n",
            " Output_Layer (Dense)           (None, 1)            33          ['Hidden_Layer_1[0][0]']         \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 183,377\n",
            "Trainable params: 183,377\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cnn_model = build_cnn_model(X_train[:,3], 20000, 300, 9)\n",
        "display(cnn_model.summary())"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### 4.2.3 RNN model\n",
        "\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "##### 4.2.3.1\n",
        "\n",
        "The last model we tried was an RNN. Redundant neural networks are still new to us, so it was really difficult to build this.\n",
        "\n",
        "In comparison to the other models, RNNs process input data using a Bidirectional layer (bi meaning 2 directions, forwards and backwards). This ensures that the model can use information from previous training and future training to change its weights in the back propagation process.\n",
        "\n",
        "Oscar was able to get this model working and we trust that this model will help us predict the best label."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [],
      "source": [
        "def build_rnn_model(tweets_np, max_vocab, max_tokens, embedding_dim):\n",
        "    \n",
        "    tf.keras.backend.clear_session()\n",
        "    tf.keras.utils.set_random_seed(0)\n",
        "\n",
        "\n",
        "    # create normalized vocab\n",
        "    norm = tf.keras.layers.TextVectorization(\n",
        "        max_tokens,\n",
        "        standardize=normalize_punctuation_and_links,\n",
        "        split='whitespace',\n",
        "        output_mode='int',\n",
        "        encoding='utf-8',\n",
        "        name='Normalization_Layer',\n",
        "        output_sequence_length=max_tokens,\n",
        "    )\n",
        "\n",
        "    norm.adapt(tweets_np , batch_size=64)\n",
        "\n",
        "    # Uses Sequential api with guidance from the tutorial https://www.tensorflow.org/text/tutorials/text_classification_rnn\n",
        "    model = tf.keras.Sequential()\n",
        "    model.add(tf.keras.Input(shape=(1,), dtype=tf.string, name='Input Layer'))\n",
        "    model.add(norm)\n",
        "    model.add(tf.keras.layers.Embedding(\n",
        "        input_dim=max_vocab,\n",
        "        output_dim=embedding_dim,\n",
        "        input_length=max_tokens,\n",
        "        name='Embeddings_Layer',\n",
        "    ))\n",
        "\n",
        "    # bidirectional layers\n",
        "    model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences= True), name='Bidirectional_Layer_1'))\n",
        "    model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences=True ), name='Bidirectional_Layer_2'))\n",
        "    model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64), name='Bidirectional_Layer_3'))\n",
        "    \n",
        "    # hidden sigmoid layers\n",
        "    model.add(tf.keras.layers.Dense(128, activation='sigmoid', name='hidden_layer_1'))\n",
        "    model.add(tf.keras.layers.Dense(64, activation='sigmoid', name='hidden_layer_2'))\n",
        "    \n",
        "    # output layers\n",
        "    model.add(tf.keras.layers.Dense(\n",
        "        units=1,\n",
        "        activation='sigmoid',\n",
        "        name='Output_Layer',\n",
        "    ))\n",
        "\n",
        "    \n",
        "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " Normalization_Layer (TextVe  (None, 300)              0         \n",
            " ctorization)                                                    \n",
            "                                                                 \n",
            " Embeddings_Layer (Embedding  (None, 300, 9)           270000    \n",
            " )                                                               \n",
            "                                                                 \n",
            " Bidirectional_Layer_1 (Bidi  (None, 300, 128)         37888     \n",
            " rectional)                                                      \n",
            "                                                                 \n",
            " Bidirectional_Layer_2 (Bidi  (None, 300, 128)         98816     \n",
            " rectional)                                                      \n",
            "                                                                 \n",
            " Bidirectional_Layer_3 (Bidi  (None, 128)              98816     \n",
            " rectional)                                                      \n",
            "                                                                 \n",
            " hidden_layer_1 (Dense)      (None, 128)               16512     \n",
            "                                                                 \n",
            " hidden_layer_2 (Dense)      (None, 64)                8256      \n",
            "                                                                 \n",
            " Output_Layer (Dense)        (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 530,353\n",
            "Trainable params: 530,353\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "rnn_model = build_rnn_model(X_train[:,3], 30000, 300, 9)\n",
        "display(rnn_model.summary())"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 5. Error Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [],
      "source": [
        "def plot_history(history, epochs):\n",
        "    history = pd.DataFrame(history)\n",
        "\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.title('Loss vs Epoch')\n",
        "\n",
        "    plt.plot(list(range(1, epochs + 1)), history['loss'], label=\"Train\")\n",
        "    plt.plot(list(range(1, epochs + 1)), history['val_loss'], label=\"Validation\")\n",
        "\n",
        "    plt.legend(loc='best')\n",
        "    plt.show()\n",
        "\n",
        "    print('Loss:', history['loss'].iloc[-1])\n",
        "    print('Val Loss:', history['val_loss'].iloc[-1])\n",
        "\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Accuracy (in %)')\n",
        "    plt.title('Accuracy vs Epoch')\n",
        "\n",
        "    plt.plot(list(range(1, epochs + 1)), history['accuracy'] * 100, label=\"Train\")\n",
        "    plt.plot(list(range(1, epochs + 1)), history['val_accuracy'] * 100, label=\"Validation\")\n",
        "\n",
        "    plt.legend(loc='best')\n",
        "    plt.show()\n",
        "\n",
        "    print('Accuracy:', history['accuracy'].iloc[-1])\n",
        "    print('Val Accuracy:', history['val_accuracy'].iloc[-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [],
      "source": [
        "def train_and_analyze_model(model, tweet_features, labels, num_of_epochs=16, num_per_batch=16, validation=0.1):\n",
        "    # Remove randomness\n",
        "    tf.keras.backend.clear_session()\n",
        "    tf.keras.utils.set_random_seed(0)\n",
        "        \n",
        "    history = model.fit(\n",
        "        tweet_features[:,3],\n",
        "        labels,\n",
        "        epochs=num_of_epochs,\n",
        "        batch_size=num_per_batch,\n",
        "        validation_split=validation,\n",
        "        verbose=1,\n",
        "    )\n",
        "\n",
        "    plot_history(history.history, num_of_epochs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [],
      "source": [
        "def plot_predictions(predictions, actual):\n",
        "    from matplotlib.ticker import PercentFormatter\n",
        "    plt.title('Prediction Distribution')\n",
        "    plt.xlabel('Positive Confidence Level (in %)')\n",
        "    plt.ylabel('# of predictions')\n",
        "    plt.gca().xaxis.set_major_formatter(PercentFormatter())\n",
        "\n",
        "    plt.hist([predictions[actual == 0] * 100, predictions[actual == 1] * 100], label=['negative', 'positive'])\n",
        "    plt.legend(loc='best')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [],
      "source": [
        "def plot_difference(predictions, actual):\n",
        "    plt.title('Prediction Difference Distribution')\n",
        "    plt.xlabel('Positive Confidence Level (in %)')\n",
        "    plt.ylabel('Difference')\n",
        "\n",
        "    diff = abs(predictions - actual)\n",
        "\n",
        "    plt.hist([diff[actual == 0] * 100, diff[actual == 1] * 100], label=['negative', 'positive'])\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {},
      "outputs": [],
      "source": [
        "def print_confusion_matrix(predictions, actual, threshold):\n",
        "    from sklearn.metrics import confusion_matrix\n",
        "\n",
        "    predictions[predictions >= threshold] = 1\n",
        "    predictions[predictions < threshold] = 0\n",
        "    tn, fp, fn, tp = confusion_matrix(actual, predictions).ravel()\n",
        "\n",
        "    print('True Positives:', tp)\n",
        "    print('True Negatives:', tn)\n",
        "    print('False Positives:', fp)\n",
        "    print('False Negatives:', fn)\n",
        "    print()\n",
        "\n",
        "    accuracy = (tp + tn) / (tp + tn + fp + fn)\n",
        "    precision = tp / (tp + fp)\n",
        "    recall = tp / (tp + fn)\n",
        "\n",
        "    print('Accuracy:', accuracy)\n",
        "    print('Precision:', precision)\n",
        "    print('Recall:', recall)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 5.1 Train and Anaylze the FFNN Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "97/97 [==============================] - 2s 12ms/step - loss: 0.6862 - accuracy: 0.5689 - val_loss: 0.6785 - val_accuracy: 0.5802\n",
            "Epoch 2/10\n",
            "97/97 [==============================] - 1s 7ms/step - loss: 0.6715 - accuracy: 0.5732 - val_loss: 0.6377 - val_accuracy: 0.5875\n",
            "Epoch 3/10\n",
            "97/97 [==============================] - 1s 7ms/step - loss: 0.5872 - accuracy: 0.7155 - val_loss: 0.5403 - val_accuracy: 0.7391\n",
            "Epoch 4/10\n",
            "97/97 [==============================] - 1s 7ms/step - loss: 0.5152 - accuracy: 0.7543 - val_loss: 0.5100 - val_accuracy: 0.7522\n",
            "Epoch 5/10\n",
            "97/97 [==============================] - 1s 7ms/step - loss: 0.4819 - accuracy: 0.7739 - val_loss: 0.4972 - val_accuracy: 0.7711\n",
            "Epoch 6/10\n",
            "97/97 [==============================] - 1s 8ms/step - loss: 0.4651 - accuracy: 0.7849 - val_loss: 0.4946 - val_accuracy: 0.7682\n",
            "Epoch 7/10\n",
            "97/97 [==============================] - 1s 7ms/step - loss: 0.4538 - accuracy: 0.7893 - val_loss: 0.4952 - val_accuracy: 0.7726\n",
            "Epoch 8/10\n",
            "97/97 [==============================] - 1s 8ms/step - loss: 0.4459 - accuracy: 0.7942 - val_loss: 0.4987 - val_accuracy: 0.7595\n",
            "Epoch 9/10\n",
            "97/97 [==============================] - 1s 7ms/step - loss: 0.4386 - accuracy: 0.7958 - val_loss: 0.4976 - val_accuracy: 0.7726\n",
            "Epoch 10/10\n",
            "97/97 [==============================] - 1s 7ms/step - loss: 0.4332 - accuracy: 0.8016 - val_loss: 0.4995 - val_accuracy: 0.7580\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 0.4331941604614258\n",
            "Val Loss: 0.4994606077671051\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.8016220331192017\n",
            "Val Accuracy: 0.7580174803733826\n"
          ]
        }
      ],
      "source": [
        "train_and_analyze_model(\n",
        "    ffnn_model,\n",
        "    X_train,\n",
        "    Y_train,\n",
        "    num_of_epochs=10,\n",
        "    num_per_batch=64,\n",
        "    validation=0.1,\n",
        ")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### 5.1.1\n",
        "Here above we see that the model converges pretty well. One small issue that we have noticed is that it does not generalize for all data too well, but the results are acceptable for a simple model that is not too complex.\n",
        "\n",
        "**Notes**\n",
        "\n",
        "After multiple runs with different parameters and layers/layer sizes, we noticed that it trained and converged faster then the rest of the models. Adding more hidden layers with more nuerons did not have a significant impact on this model's accuracy, in fact it may have hurt it. As the writing of this, it is the best performing model we have however, it does not have much room to grow. One way that can maybe help this function is the using another process to pre-process the data that is far more complex that also can handle potential typos that can occur in online discourse."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "215/215 [==============================] - 1s 2ms/step\n",
            "Train Loss: 0.43176578291667506\n",
            "24/24 [==============================] - 0s 2ms/step\n",
            "Val Loss: 0.522468330900813\n"
          ]
        }
      ],
      "source": [
        "ffnn_train_predictions = ffnn_model.predict(X_train[:, 3]).flatten()\n",
        "ffnn_train_loss = calculate_loss(Y_train, ffnn_train_predictions)\n",
        "print(f'Train Loss: {ffnn_train_loss}')\n",
        "\n",
        "ffnn_validation_predictions = ffnn_model.predict(X_validation[:, 3]).flatten()\n",
        "ffnn_validation_loss = calculate_loss(Y_validation, ffnn_validation_predictions)\n",
        "print(f'Val Loss: {ffnn_validation_loss}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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8/LoDKDwc5uPTHu3b+6JKFUcAgJ/fQEycOAEAYGVVGVKpFBYWFrCwsFCqpWXL1hAEBS5fvgRv75a4ePEPmJiYoGlTL0RG/oVHjx4iImI9pFIpatWqjbFjv8ScOTMwYkSARnoFMBBpnLqOP1e048pERKRZTk41xd/NzSshPz8fd+8m4vffz6JzZx9xLD8/HzVr1gIAxMfH4tNPh4pjVlZWqFXLGUDh9X/69u2PEyeO4vr1q7h79w5u3Yop1QUSjY2N4ePTHqdPn4S3d0ucPn0S7dt3hIGBAe7eTURGRjo++KCdOL9CoUBubi7S09NQqZLV27aiVBiItESXjz8TEVHFZ2SkfPNTQRBQUFCALl264bPPhimNGRoWxgEDAwMAQpHnAYUh5auvxuL58+fo2LEz3n+/LfLy8hAcPLFU9XTs2AVz5szAhAnf4ty5MwgNXQgAKCgoQK1atTF37qIiz6lUyaLItPLCc4iIiIj0RM2azvjnnyQ4OdUUf86ePY1jx44AAOrUeQ+3bsWI8794kYl//vkHAHDnTgKioi7jxx+X4bPPhqF16zZITU0B8L/Q9LqrSHt5eUOhKMCOHVtgamoKmcxTrOnx40ewtrYRa3r48D7WrFmp0auEMxARERHpCT+/AYiJ+RsREcuQlHQPx479ioiIcFStWg0A0K/fR9i1axtOnz6JO3cSERo6C9nZWZBIJLCwsIRUKsVvvx3Fo0cP8X//dwJr164EUHiSNACYmpri7t07yMhIL7JuQ0NDtGvni40b16FDh45i2PH2bomqVati5swfEB8fh+joK5g/fw5MTU3/u8dKM3jIjIiISEV1q2jmUI6611O1ajXMm7cYy5eHYdu2TbC3r4Jx475Ely7dAACdOn2Af/5JwoIFoZDL5ejduy+qVq0GQ0NDVKniiG+++Q7r16/GypXhqFnTGRMmfIvZs6chNvYWXF3d0bfvACxfvgRJSffQv/9HRdbfsWMX/PzzXnTs+IE4zcDAAHPnLsaPPy5AQMAQmJmZo0OHThg3boJat/1NJMK/r6JESlJSnqM03ZFIAHt7y1LNb2gohY1NJfRYcvatziFqUt0Kv4z3wbNnL5Cfr947/lYUqvSd1Id91x72XrPy8uRITX0IO7tqMDMzRX6+4p2/UvWVK5GoXr0GHB2rFq47Px89e3bCnDkL0bSp5m/DYWgoLfIZ9+/XxcjIWGns5b+RMq2rzFUSERHpGYVCQHpalkavA6dQCBr7NvHZs6dw7dpVTJwYBHPzSti1axvMzSuhSRM3jaxfmxiIiIiIVKDJgKJpI0aMxqJF8/DVV2ORm5sLV1d3LFoUBhMTE22XVu4YiIiIiAhA4fWKfvhhprbL0Ap+y4yIiIj0HgMRERER6T0GIiIiItJ7DERERESk9xiIiIiISO8xEBEREZHeYyAiIiJSgVQqgaGhVGM/mrwI5Ov0798Lhw8fBABkZb3AkSOHih2rqHgdIiIiolKSSiWwtTaFxEBzH59CQT6epuVo/WKQq1ZthLm5GQBg+/YtuHz5Erp161lkrKJiICIiIiolqVRSGIb2jABSbpf/Cu3rQ9JvNaRSidYDkY2Njfj7q7dB/fdYRcVAREREpKqU28DDaG1XUayHDx9gwIDemDp1NpYt+wk5Odno2rUnxo37EoaGhvj997NYs2YF7ty5g+rVq2PkyDFo184XABAbexuLFs1FbOwtWFpa4cMP/fD55yMBFB4WGzYsAACwbt0qAECbNl44d+6SOGZra4fg4In45ZffYGpqCgC4ePEPfP/9ZBw8eBTGxibYsGEN9u3bjdzcHLi7e+LrryejatWqWuiUMp5DRERE9A5aty4CM2aEYs6chTh9+iTWrFmJyMi/EBw8EV279sD69VvRs+eHmDo1CDExfwMAZs+ehnr1GmDTpp347rsfsGXLBly4cE5puR07dsagQZ/C1dUdP//8q9KYl5c3zMzM8Mcfv4vTTp36DW3atIWJiSn27NmBY8eOYNq02Vi5cj1sbW3x9ddjkZ+fX/4NeQOdCERyuRw9e/bEn3/+WWTs+fPn8PHxwd69e5WmHzp0CJ06dYJMJsPYsWPx9OlTcUwQBCxcuBAtW7aEt7c35s+fD4VCUe7bQUREpCsCA8dDJvNA06ZeGDFiNA4e3I/du3egffuOGDjwY9Sq5YxBgz5F+/a+2LZtEwDg0aMHqFy5MqpWrYaWLVvjxx+XoX79hkrLNTExhZmZGQwNDWFnZ680ZmhoiHbtfHHq1EkAQEFBAc6ePQ1f384AgK1bNyEwcAKaNvWCs3NtTJw4BRkZGfjjj/Ma6MjraT0Q5ebm4uuvv0ZsbGyx4wsWLMCTJ0+Upl29ehXBwcEYN24cduzYgYyMDAQFBYnj69atw6FDh7B06VIsWbIEBw8exLp168p1O4iIiHSJm5uH+HvDho2RlvYM9+7dQePGrkrzubrKcPfuHQCAv//n2LBhDT78sCtCQ2dCLpcXCT1v0rFjF1y4cA55eXmIjr6CvLw8tGjRCllZWXjy5DGmTQtC584+6NzZB127tkdGRjqSku697ea+Na2eQxQXF4dvvvmmyMlZL126dAl//PEHHBwclKZv3rwZ3bp1Q58+fQAA8+fPR4cOHZCUlISaNWti48aNGD9+PLy8vAAA3377LX766ScMHz68XLeHiIhIVxga/u8jXqEoAFB4ROZVCkWBOP7pp0Ph69sZZ878H37//SwmTBiDSZOC0atXn1Kv18OjKczMzPHXX3/izz/Po23b9jAyMkJOTg4AYNaseahVy1npOVZWVqpuntppdQ/RxYsX0aJFC+zYsaPImFwuxw8//ICpU6fC2NhYaSw6OloMOwBQrVo1VK9eHdHR0Xj8+DEePnyI5s2bi+PNmjXD/fv3i+xpIiIielfFxt4Sf4+J+Rv29g5o3LgJbty4pjTf9evXUKuWM3Jzc/HjjwthZGSEQYM+RVjYSvTu3Vc8/PVvEknJ10aSSqXo0KETzp8/h7NnT6NTpw8AAJaWlrCxscXTpylwcqoJJ6eacHSsimXLluDevbtq2uqy0+oeoo8//rjEsRUrVqBx48Zo06ZNkbEnT56gSpUqStPs7Ozw6NEjJCcnA4DSuL194e6+R48eFXne67zm9S52vtLOr27aWq+2abvv+op91x72XrMqep9/+mkRvvvuezx//hyrV69Av34D0ayZNwIDh2Pnzm1o1ep9nD9/FmfO/B8WL14KExMTXL0ahSdPHmP06LHIyspCdPQV+Pi0L7JsU1MzpKSk4OHDB6hWrXqR8Y4du+Crr8bCxMQETZv+bwfGRx99jIiI5bC2toWzc22sX78a165Fo1atH1TePomk6Gv0Nq+ZTn7tPi4uDtu3b8eBAweKHc/JySmy18jY2BhyuVzcJffv8Ze/F7er8HXs7CzLdX51sLGppPF16hpt9J3Yd21i7zUjJycHT59KYWBQ+ClraCiFgcF/D6zY19dMEf9dj7jeUng5b+fOXTBx4pcQBAX8/AZg6NBhkEqlmD59NlavXonly5egVi1nzJ49Fy1atAAAhITMxcKFczFixBAYGhqgY8fOGD58JAwNC5f58irdvr6+OHBgDz79dCD27TukNAYAHh4yWFtbo2XL1jA1/d/nsb//EOTkZGPBgjl48SITjRo1xk8/hcPW1rrE7Xm5zJcUCgmkUilsbCqJX+1XB50LRIIg4Pvvv8f48ePFPTuvMjExKRJu5HI5zMzMlMKPiYmJ+DsAmJmpdhXN1NTnKOH0JiUSSeEfqNLMb2AgVWuIefbsBQoK9PMbdKr0ndSHfdce9l6z8vLkUCgUKCgobHZ+vgIKhQChIB+Sfqs1VodQkI+8vIJSX5jx5WeCr28XfPLJUHG6QgEoFAp06NAZHTp0VnpOfn7hc6pVc8KiRUuLLDM/X4Hduw+Kv1etWgPbt+8Xx/899tKuXQeKTAMkGDFiDEaMGFPs+l9laCgtMlZQIEChUODZsxcwMspTGnv5b6QsdC4QPXjwAFeuXMGtW7cwb948AEB2djamTZuGw4cPY/Xq1XB0dERKSorS81JSUuDg4ABHR0cAQHJyMpycnMTfARQ5OftNBAEq/dFRdX510fc/jNrqu75j37WHvdeM4nqsUAh4mpaj0fuLKRSC1q9SrYvU/e9A5wKRo6Mjjh07pjTN398f/v7+6N27NwBAJpMhMjISfn5+AICHDx/i4cOHkMlkcHR0RPXq1REZGSkGosjISFSvXl2l84eIiIiKw4DybtK5QGRoaAhnZ+ci0+zs7MS9P4MHD4a/vz88PDzg5uaGkJAQtG/fHjVr1hTHFy5cKF4KfNGiRRg2bJhmN4SIiEgLqlWrjnPnLmm7jApH5wJRaXh6emLmzJlYsmQJ0tPT8f7772PWrFni+PDhw5Gamopx48bBwMAA/fv3x9ChQ7VXMBEREek0nQlEt27dKnHs5Mmi10Dw8/MTD5m9ysDAAEFBQUpXryYiIiqLki4eTNpRXq+H1m/dQUREpIuk0sKPyIIC7d94lP7n5evx8vVRF53ZQ0RERKRLpFIDGBmZIjMzDcbGRuA9wjVPoZCIlz0AAEFQ4PnzNBgbm0IqNVDruhiIiIiIiiGRSFC5si1SUx8hNfUxFExEGieVSov0XSKRwsrK9rW3DykLBiIiIqISGBoawdHRCZaWxnj27AWv/6RBEknh3Rhe7buhoZHawxDAQERERPRaEokEpqamMDLKYyDSIIkEGu07T6omIiIivcdARERERHqPgYiIiIj0HgMRERER6T0GIiIiItJ7DERERESk9xiIiIiISO8xEBEREZHeYyAiIiIivcdARERERHqPgYiIiIj0HgMRERER6T0GIiIiItJ7DERERESk9xiIiIiISO8xEBEREZHeYyAiIiIivcdARERERHqPgYiIiIj0HgMRERER6T0GIiIiItJ7DERERESk9xiIiIiISO8xEBEREZHeYyAiIiIivcdARERERHqPgYiIiIj0HgMRERER6T0GIiIiItJ7OhGI5HI5evbsiT///FOcFhUVhUGDBsHT0xMffPABdu3apfSc8+fPo2fPnpDJZPjss8+QlJSkNL5+/Xr4+PjA09MTU6ZMQXZ2tka2hYiIiCoerQei3NxcfP3114iNjRWnJScnY+TIkfD29sa+ffswfvx4zJo1C6dOnQIAPHjwAGPHjoWfnx92794NW1tbBAYGQhAEAMDRo0exdOlSzJw5Exs2bEB0dDQWLFigjc0jIiKiCkCrgSguLg4DBw7EvXv3lKafOHEC9vb2+Prrr1G7dm306NEDffr0wcGDBwEAu3btgqurK4YNG4Z69eohNDQU9+/fx8WLFwEAGzduxJAhQ9ChQwe4u7tjxowZ2LNnD/cSERERUbG0GoguXryIFi1aYMeOHUrTfXx8EBoaWmT+zMxMAEB0dDS8vLzE6WZmZmjSpAmioqJQUFCAa9euKY17eHggLy8PMTEx5bQlREREVJEZanPlH3/8cbHTnZyc4OTkJD5OTU3FL7/8gi+++AJA4SG1KlWqKD3Hzs4Ojx49QkZGBnJzc5XGDQ0NYW1tjUePHqlUn0Si2nylnV/dtLVebdN23/UV+6497L12sO/aUZa+v81rpNVAVBo5OTn44osvYG9vj48++ggAkJ2dDWNjY6X5jI2NIZfLkZOTIz4ublwVdnaW5Tq/OtjYVNL4OnWNNvpO7Ls2sffawb5rh6b6rtOB6MWLFwgMDMSdO3ewdetWmJmZAQBMTEyKhBu5XA4rKyuYmJiIj18df/n80kpNfY7/nqf9WhJJ4QtWmvkNDKRqDTHPnr1AQYFCbcurSFTpO6kP+6497L12sO/aUZa+v3xOWehsIMrMzMSIESNw7949bNiwAbVr1xbHHB0dkZKSojR/SkoKGjVqBGtra5iYmCAlJQUuLi4AgPz8fKSlpcHBwUGlGgQBKr35VZ1fXfT9H6i2+q7v2HftYe+1g33XDk31Xetfuy+OQqHAuHHj8M8//2DTpk2oV6+e0rhMJkNkZKT4ODs7Gzdv3oRMJoNUKoWbm5vSeFRUFAwNDdGwYUONbQMRERFVHDoZiHbv3o0///wTs2fPhpWVFZKTk5GcnIy0tDQAQL9+/XD58mVEREQgNjYWQUFBcHJyQosWLQAUnqy9Zs0anDhxAlevXsX06dMxcOBAlQ+ZERERkX7QyUNmR48ehUKhwKhRo5Sme3t7Y9OmTXByckJYWBjmzJmD8PBweHp6Ijw8HJL/nl7eo0cP3L9/H1OnToVcLkeXLl0wceJEbWwKERERVQA6E4hu3bol/r5mzZo3zt+uXTu0a9euxPGAgAAEBASopTYiIiJ6t+nkITMiIiIiTWIgIiIiIr3HQERERER6j4GIiIiI9B4DEREREek9BiIiIiLSewxEREREpPcYiIiIiEjvMRARERGR3mMgIiIiIr3HQERERER6j4GIiIiI9B4DEREREek9BiIiIiLSewxEREREpPcYiIiIiEjvMRARERGR3mMgIiIiIr3HQERERER6j4GIiIiI9B4DEREREek9BiIiIiLSewxEREREpPcYiIiIiEjvMRARERGR3mMgIiIiIr3HQERERER6j4GIiIiI9B4DEREREek9BiIiIiLSewxEREREpPcYiIiIiEjvMRARERGR3mMgIiIiIr3HQERERER6TycCkVwuR8+ePfHnn3+K05KSkjB06FB4eHige/fuOHfunNJzzp8/j549e0Imk+Gzzz5DUlKS0vj69evh4+MDT09PTJkyBdnZ2RrZFiIiIqp4tB6IcnNz8fXXXyM2NlacJggCxo4dC3t7e+zZswcffvghxo0bhwcPHgAAHjx4gLFjx8LPzw+7d++Gra0tAgMDIQgCAODo0aNYunQpZs6ciQ0bNiA6OhoLFizQyvYRERGR7tNqIIqLi8PAgQNx7949pel//PEHkpKSMHPmTLi4uGDUqFHw8PDAnj17AAC7du2Cq6srhg0bhnr16iE0NBT379/HxYsXAQAbN27EkCFD0KFDB7i7u2PGjBnYs2cP9xIRERFRsbQaiC5evIgWLVpgx44dStOjo6PRuHFjmJubi9OaNWuGqKgocdzLy0scMzMzQ5MmTRAVFYWCggJcu3ZNadzDwwN5eXmIiYkp3w0iIiKiCslQmyv/+OOPi52enJyMKlWqKE2zs7PDo0eP3jiekZGB3NxcpXFDQ0NYW1uLzy8tiUS1+Uo7v7ppa73apu2+6yv2XXvYe+1g37WjLH1/m9dIq4GoJNnZ2TA2NlaaZmxsDLlc/sbxnJwc8XFJzy8tOzvLcp1fHWxsKml8nbpGG30n9l2b2HvtYN+1Q1N918lAZGJigrS0NKVpcrkcpqam4vir4UYul8PKygomJibi41fHzczMVKojNfU5/nue9mtJJIUvWGnmNzCQqjXEPHv2AgUFCrUtryJRpe+kPuy79rD32sG+a0dZ+v7yOWVRpkB05swZNGnSBHZ2dti9ezeOHTuGxo0bIzAwsMiembJwdHREXFyc0rSUlBTxMJijoyNSUlKKjDdq1AjW1tYwMTFBSkoKXFxcAAD5+flIS0uDg4ODSnUIAlR686s6v7ro+z9QbfVd37Hv2sPeawf7rh2a6rvKJ1WHh4djwoQJ+Oeff3Dx4kVMnToV1apVw/HjxxEaGqqWomQyGW7cuCEe/gKAyMhIyGQycTwyMlIcy87Oxs2bNyGTySCVSuHm5qY0HhUVBUNDQzRs2FAt9REREdG7ReVAtHPnToSFhUEmk+Hnn39G8+bNMWPGDMydOxeHDx9WS1He3t6oVq0agoKCEBsbi4iICFy9ehX9+/cHAPTr1w+XL19GREQEYmNjERQUBCcnJ7Ro0QJA4cnaa9aswYkTJ3D16lVMnz4dAwcOVPmQGSmTSiUwNJS+9Y9UyjMTiYhIt6h8yCw9PR3vvfceBEHAqVOnMHLkSACAhYUFCgoK1FKUgYEBli1bhuDgYPj5+cHZ2Rnh4eGoXr06AMDJyQlhYWGYM2cOwsPD4enpifDwcEj+e3p5jx49cP/+fUydOhVyuRxdunTBxIkT1VKbvpJKJahsbQ5Dg7e/UkN+gQLpaVlQKLjvmYiIdIPKgahhw4ZYs2YNrK2t8fTpU3Tu3BmPHz/G4sWL4eHhUeZCbt26pfTY2dkZmzdvLnH+du3aoV27diWOBwQEICAgoMz1kDKpVAJDAykmbL+CuCeZZV5O3SoW+GmQJ6RSCQMRERHpDJUD0fTp0zF58mTcv38f33zzDWrUqIGQkBDcv38fP/30U3nUSDok7kkmbjzI0HYZREREalWmPUQ///yz0rSJEyeq5dtlRERERNpQpq/d3717F9evX0deXl6RsT59+rxtTUREREQapXIgWr16NRYuXIjKlSujUiXlCwxKJBIGIiIiIqpwVA5Ea9euxcSJEzF8+PDyqIeIiIhI41T+DnVubi66dOlSHrUQERERaYXKgahXr17YunUrBF6/nIiIiN4RKh8yy8zMxO7du3Ho0CE4OTnByMhIaXzjxo1qK46IiIhIE1QORLVr18bo0aPLoxYiIiIirVA5EI0bN078PTMzEwUFBahcubJaiyIiIiLSpDJdh2jDhg1YvXo1UlJSAAC2trYYPHiwUlgiIiIiqihUDkTh4eHYvHkzJkyYAE9PTygUCly+fBlLly6FsbEx7x9GREREFY7KgWjnzp0ICQmBr6+vOK1Ro0ZwdHRESEgIAxERERFVOCp/7T4zMxO1a9cuMr1OnTp4+vSpOmoiIiIi0iiVA5GnpyfWrl0LhUIhTisoKMDatWvh7u6u1uKIiIiINEHlQ2ZBQUH45JNPcP78eTRp0gQAcOPGDcjlcqxevVrtBRIRERGVN5UDkYuLC44cOYKDBw8iISEBJiYmeP/999GrV68iN3slIiIiqgjK9LV7GxsbfPbZZ+quhYiIiEgrShWIOnbsiN27d8PGxga+vr6QSCQlzvvbb7+prTgiIiIiTShVIBo3bpx4OOyLL74o14KIiIiINK1Ugahv377i7/fv38fw4cNhZmamNE9mZiaWLl2q3uqIiIiINKBUgSghIQGpqakACq9U3bBhwyL3L7t9+za2b9+O7777Tv1VEhEREZWjUgWiJ0+eYOjQoeLj4u5ZZmZmhiFDhqitMCIiIiJNKVUgatmyJWJiYgAAvr6+2LNnD2xsbMq1MCIiIiJNUflK1SdPnsTVq1dx9uxZcVpISAjOnDmj1sKIiIiINEXlQLR582Z89dVXSElJEacZGhriyy+/xM6dO9VaHBEREZEmqByI1q5di0WLFil982zy5MlYsGABIiIi1FocERERkSaoHIiePXuGWrVqFZlep04dpb1GRERERBWFyoGoWbNmCAsLQ3Z2tjgtNzcXK1asgKenp1qLIyIiIt0hlUpgaChVy49UWvJdL7RB5XuZTZ06FcOGDUObNm1Qu3ZtAMC9e/dgb2+PZcuWqbs+IiIi0gFSqQS21qaQGJTpNqhFCAX5eJqWA4VCUMvy3pbKW1WrVi0cPnwYZ8+exZ07d2BoaIjatWujTZs2MDAwKI8aiYiISMukUklhGNozAki5/XYLs68PSb/VkEolFTcQAYCxsTE6duyo7lqIiIhI16XcBh5Ga7sKtStVIGrUqBHOnTsHOzs7NGzY8LV3u//777/VVhwRERGRJpQqEG3YsEG8d9mGDRteG4iIiIiIKppSBSJvb2/x9xYtWpRbMa96+PAhpk+fjr/++gvW1tb47LPPxHuq3bx5E9OmTcPt27dRt25dzJgxA66uruJzDx06hB9//BHJyclo06YNZs2aBVtbW43VTkRERBVHqQKRr69vqfcK/fbbb29V0L99+eWXqF69Ovbu3Yu4uDh8++23qFGjBt5//30EBASgV69emDt3LrZt24ZRo0bh+PHjMDc3x9WrVxEcHIwZM2agYcOGCAkJQVBQEFauXKm22oiIiOjdUapA9MUXX4i/37t3Dxs2bMDgwYPh5uYGIyMj3Lx5E5s3b1br3e7T09MRFRWFWbNmoXbt2qhduzZ8fHxw4cIFpKenw8TEBJMmTYJEIkFwcDDOnDmDX3/9FX5+fti8eTO6deuGPn36AADmz5+PDh06ICkpCTVr1lRbjURERPRuKNWFGfv27Sv+nD59GiEhIZg4cSK6du2Kjh074osvvkBISAgOHz6stsJMTU1hZmaGvXv3Ii8vDwkJCbh8+TIaNWqE6OhoNGvWTNxrJZFI0LRpU0RFRQEAoqOj4eXlJS6rWrVqqF69OqKj372z4omIiOjtqfy1+8TERNSvX7/I9Jo1a+L+/ftqKQoATExMMHXqVMyaNQsbN25EQUEB/Pz8MGDAAPz222+oW7eu0vx2dnaIjY0FADx58gRVqlQpMv7o0SOVaijtueMv59PWueYV9Rz3t61b233XV+y79rD32sG+l6+S+lqWvr/Na6RyIGrWrBnmzJmDOXPmwNHREQCQlJSE2bNnw8fHp+yVFCM+Ph4dOnTA559/jtjYWMyaNQutWrVCdnY2jI2NleY1NjaGXC4HAOTk5Lx2vLTs7CzLdX51sLGppPF1qoM669ZG34l91yb2XjvYd/UrzWeBpvquciCaM2cOxo8fj/bt26Ny5coQBAEZGRlo1aoVZs2apbbCLly4gN27d+P06dMwNTWFm5sbHj9+jOXLl6NmzZpFwo1cLoepqSmAwr1LxY2bmZmpVENq6nMIpbiApkRS+IKVZn4DA6law8CzZy9QUKBQ2/JKoot1q9J3Uh/2XXvYe+1g3wup+3MAeP1nQVn6/vI5ZaFyIKpSpQq2b9+OuLg4xMXFAQDq1asHFxeXMhVQkuvXr8PZ2VkMOQDQuHFjrFixAl5eXkhJSVGaPyUlRTxM5ujoWOy4g4ODSjUIAlR686s6v7pU1H+g6qpbW33Xd+y79rD32sG+l4839VRTfVf5bvcAUFBQgH/++QePHj1C69atkZmZiefPn6u1sCpVquDu3btKe3oSEhLg5OQEmUyGK1euQPhvhwRBwOXLlyGTyQAAMpkMkZGR4vMePnyIhw8fiuNERERE/6ZyIHr48CF69uyJKVOmYMGCBUhPT8fq1avRrVs33Lp1S22F+fr6wsjICN9//z0SExNx8uRJrFixAv7+/ujatSsyMjIQEhKCuLg4hISEIDs7G926dQMADB48GD///DN27dqFmJgYTJo0Ce3bt+dX7omIiKhYKgeimTNnwsvLC2fPnhVPXF68eDFat26N2bNnq60wS0tLrF+/HsnJyejfvz9CQ0MxZswYfPTRR7CwsMDKlSsRGRkJPz8/REdHIyIiAubm5gAAT09PzJw5E+Hh4Rg8eDAqV66M0NBQtdVGRERE7xaVzyG6dOkSdu7cCQMDA3GakZERAgMD0bdvX7UWV7duXaxbt67YMXd3d+zbt6/E5/r5+cHPz0+t9RAREdG7SeU9RKampkhNTS0yPTExERYWFmopioiIiEiTVA5EgwYNwtSpU3Hq1CkAhUFoz549+OGHH9C/f39110dERERU7lQ+ZDZ27FhYWVlh+vTpyM7ORkBAAOzs7DB06FAMHz68PGokIiIiKlcqB6JDhw6hV69e8Pf3R1ZWFgoKCmBpyat3EhERUcWl8iGzGTNm4OnTpwAAc3NzhiEiIiKq8FQORC1atMChQ4dUvi8YERERka5S+ZBZamoqli1bhhUrVsDW1hYmJiZK47/99pvaiiMiIiLSBJUD0cCBAzFw4MDyqIWIiIhIK1QORC8vvpiZmYk7d+5AKpWiTp06Kt9JnoiIiEhXqByIsrOzMXXqVBw5cgT5+fkAAGNjY/Tt2xfff/89jIyM1F4kERERUXlS+aTqH374ATExMVizZg0iIyPx119/YcWKFbh06RLvF0ZEREQVksqB6OTJk5g7dy5atGiBSpUqwdLSEq1bt8acOXNw6NCh8qiRiIiIqFypHIjs7OyKvZeZXC7nvcyIiIioQlL5HKJRo0YhODgYo0aNgqenJwwNDfH3339jyZIl6Nu3L/766y9x3ubNm6u1WCIiIqLyoHIg+v777wEAs2fPLjIWHh6O8PBwAIBEIsHff//9luURERERlT+VA1FMTEx51EFERESkNSqfQ0RERET0rmEgIiIiIr3HQERERER6r1SBaMmSJXj+/DkA4MGDBxAEoVyLIiIiItKkUgWiNWvWID09HQDQsWNHPHv2rFyLIiIiItKkUn3LrHbt2vjiiy/QsGFDCIKA2bNnw8TEpNh5efsOIiIiqmhKtYcoLCxM6SKLPGRGRERE75JS7SGqVasWpkyZIj4ODg7mbTqIiIjonaHyhRlDQ0Px5MkTrFq1CvHx8SgoKMB7772HAQMGoHbt2uVQIhEREVH5Uvlr95cuXcIHH3yAP//8E05OTnBycsJff/2FDz/8EJGRkeVRIxEREVG5UnkP0dy5c/Hpp5/im2++UZq+cOFCLFiwANu3b1dbcURERESaoPIeotjYWPTr16/I9P79+/NmrkRERFQhqRyIatSogatXrxaZHh0dDXt7e7UURURERKRJKh8yGzFiBKZNm4aEhAS4u7sDKAxDmzZtwtdff632AomIiIjKm8qByM/PDwCwefNmrFu3DiYmJqhTpw5CQkLQrVs3tRdIREREVN5UDkRAYSh6GYyIiIiIKjre7Z6IiIj0HgMRERER6T0GIiIiItJ7KgeipUuXIjs7W3wcERGBjIwMtRb1klwux4wZM9C8eXO0bt0aixcvFm8se/PmTQwYMAAymQz9+vXD9evXlZ576NAhdOrUCTKZDGPHjsXTp0/LpUYiIiKq+EoViHr37o0pU6Zgy5YtCA8PR2pqqji2YsUKpKenl0txs2fPxvnz57FmzRosWrQIO3fuxI4dO5CVlYWAgAB4eXlh79698PT0xKhRo5CVlQUAuHr1KoKDgzFu3Djs2LEDGRkZCAoKKpcaiYiIqOIr1bfM5s2bh+vXr+PGjRsQBAG9evWCjY0N6tatC7lcjjNnzqBt27aoWbOm2gpLS0vDnj17sG7dOvF6R8OGDUN0dDQMDQ1hYmKCSZMmQSKRIDg4GGfOnMGvv/4KPz8/bN68Gd26dUOfPn0AAPPnz0eHDh2QlJSk1hqJiIjo3VCqPUSNGjXCgAEDMH36dADAsWPHsHHjRgwePBhSqRQnT57EkCFD0LJlS7UVFhkZCQsLC3h7e4vTAgICEBoaiujoaDRr1gwSiQQAIJFI0LRpU0RFRQEovFCkl5eX+Lxq1aqhevXqiI6OVlt9RERE9O4o1R6iCRMmoEmTJmjcuDEkEgkMDAzEO90bGBhg+vTpqFmzplrP00lKSkKNGjWwf/9+rFixAnl5efDz88OYMWOQnJyMunXrKs1vZ2eH2NhYAMCTJ09QpUqVIuOPHj1SqYb/5q1Sz1fa+dVNW+t9W29bt7b7rq/Yd+1h77WDfS9fJfW1LH1/m9eoVIGoW7duuHHjBtauXSseMqtXrx7q1q2LgoICxMfHo1q1arC1tS17Ja/IysrC3bt3sX37doSGhiI5ORlTp06FmZkZsrOzYWxsrDS/sbEx5HI5ACAnJ+e146VlZ2dZrvOrg41NJY2vUx3UWbc2+k7suzax99rBvqtfaT4LNNX3UgWirl27omvXrgCAhg0bYvXq1Xj27BliY2OhUCgwb948fPXVV6hXrx527typnsIMDZGZmYlFixahRo0aAIAHDx5g27ZtcHZ2LhJu5HI5TE1NAQAmJibFjpuZmalUQ2rqc/z3S22vJZEUvmClmd/AQKrWMPDs2QsUFCjUtryS6GLdqvSd1Id91x72XjvY90Lq/hwAXv9ZUJa+v3xOWah8647q1aujatWqaNSoEVq3bo1du3Zh1apVsLOzQ0xMTJmKKI6DgwNMTEzEMAQAderUwcOHD+Ht7Y2UlBSl+VNSUsTDZI6OjsWOOzg4qFSDIEClN7+q86tLRf0Hqq66tdV3fce+aw97rx3se/l4U0811XeVr0N08uRJ2NjYiI8PHTqEatWqwdjYWPw2mDrIZDLk5uYiMTFRnJaQkIAaNWpAJpPhypUr4jWJBEHA5cuXIZPJxOdGRkaKz3v48CEePnwojhMRERH9m85eqfq9995D+/btERQUhJiYGJw9exYREREYPHgwunbtioyMDISEhCAuLg4hISHIzs5Gt27dAACDBw/Gzz//jF27diEmJgaTJk1C+/bt+ZV7IiIiKpbOBiIAWLhwIWrVqoXBgwdj8uTJ+OSTT+Dv7w8LCwusXLkSkZGR8PPzQ3R0NCIiImBubg4A8PT0xMyZMxEeHo7BgwejcuXKCA0N1fLWEBERka5S+RwiTbK0tMT8+fOLHXN3d8e+fftKfK6fnx/8/PzKqzQiIiJ6h+j0HiIiIiIiTWAgIiIiIr3HQERERER6j4GIiIiI9B4DEREREek9BiIiIiLSewxEREREpPcYiIiIiEjvMRARERGR3tPpK1UTqYNUKoFUKnnr5SgUAhQK3uqaiOhdxEBE7zSpVILK1uYwNHj7naH5BQqkp2UxFBERvYMYiOidJpVKYGggxYTtVxD3JLPMy6lbxQI/DfKEVCphICIiegcxEJFeiHuSiRsPMrRdBhER6SieVE1ERER6j4GIiIiI9B4DEREREek9BiIiIiLSewxEREREpPcYiIiIiEjvMRARERGR3mMgIiIiIr3HQERERER6j4GIiIiI9B4DEREREek9BiIiIiLSewxEREREpPcYiIiIiEjvMRARERGR3mMgIiIiIr3HQERERER6j4GIiIiI9B4DEREREek9BiIiIiLSewxEREREpPcqTCAKCAjAd999Jz6+efMmBgwYAJlMhn79+uH69etK8x86dAidOnWCTCbD2LFj8fTpU02XTERERBVEhQhEv/zyC06fPi0+zsrKQkBAALy8vLB37154enpi1KhRyMrKAgBcvXoVwcHBGDduHHbs2IGMjAwEBQVpq3wiIiLScTofiNLS0jB//ny4ubmJ0w4fPgwTExNMmjQJLi4uCA4ORqVKlfDrr78CADZv3oxu3bqhT58+aNiwIebPn4/Tp08jKSlJW5tBREREOkznA9G8efPw4Ycfom7duuK06OhoNGvWDBKJBAAgkUjQtGlTREVFieNeXl7i/NWqVUP16tURHR2t0dqJiIioYtDpQHThwgVcunQJgYGBStOTk5NRpUoVpWl2dnZ49OgRAODJkyevHSciItImqVQCQ0OpWn6kUom2N+edYKjtAkqSm5uLadOmYerUqTA1NVUay87OhrGxsdI0Y2NjyOVyAEBOTs5rx0tLUsr32Mv5Sju/umlrvW/rbevWVt8rar/VRdvvd33G3muHuvsulUpgU9kUEgP1fAQLBfl4lp4DhUJQy/I0raS+lqXvb/Ma6WwgWrp0KVxdXeHj41NkzMTEpEi4kcvlYnAqadzMzEylGuzsLMt1fnWwsamk8XWqgzrr1mTfK2q/y4M23u9UiL3XDrX3fc8IIOX22y3Dvj4k/VbD1tZCPTVpWGn+pmrq/a6zgeiXX35BSkoKPD09AUAMOEePHkXPnj2RkpKiNH9KSop4mMzR0bHYcQcHB5VqSE19DqEUgVsiKXzBSjO/gYFUrR+qz569QEGBQm3LK4ku1l2avuti3RWdKu93Ui/2XjvU3Xfx71LKbeChes5t1cTfJnX/PQVeX3dZ+v7yOWWhs4Fo06ZNyM/PFx8vXLgQAPDtt9/ir7/+wqpVqyAIAiQSCQRBwOXLlzF69GgAgEwmQ2RkJPz8/AAADx8+xMOHDyGTyVSqQRCg0ptf1fnVpaL+YVRX3Zrue0Xtt7pp6/1O7L226Hrfdbm213lT3Zrqu84Goho1aig9rlSpMJU6OzvDzs4OixYtQkhICAYNGoTt27cjOzsb3bp1AwAMHjwY/v7+8PDwgJubG0JCQtC+fXvUrFlT49tBREREuk+nv2VWEgsLC6xcuVLcCxQdHY2IiAiYm5sDADw9PTFz5kyEh4dj8ODBqFy5MkJDQ7VcNREREekqnd1D9Kq5c+cqPXZ3d8e+fftKnN/Pz088ZEZERET0OhVyDxERERGROjEQERERkd5jICIiIiK9x0BEREREeo+BiIiIiPQeAxERERHpvQrztXsiIqJXSaUStd3tXaEQKuwNUuntMRAREVGFJJVKYGut3rvGP02ruHeNp7fDQERERBWSVCopDENqvGu8VCphINJTDERERFSxqfGu8aS/eFI1ERER6T0GIiIiItJ7DERERESk9xiIiIiISO8xEBEREZHeYyAiIiIivcdARERERHqPgYiIiIj0HgMRERER6T0GIiIiItJ7DERERESk93gvMyIdJZVKIJVK3no5CoXAm1USEb0BAxGRDpJKJahsbQ5Dg7ffiZtfoEB6WhZDERHRazAQEekgqVQCQwMpJmy/grgnmWVeTt0qFvhpkCekUgkDERHRazAQEemwuCeZuPEgQ9tlEBG983hSNREREek9BiIiIiLSewxEREREpPcYiIiIiEjvMRARERGR3uO3zEgrDNRwfR1tUFfdvFgiEZFuYSAijXKwMAEUBbCyMlPPAhUFkEolKCgo33Ch7rqFgnw8TcthKCIi0hEMRKRRVmaGgNQA2DMCSLn9dguzrw/0Ww2JRAKgfIOFuuuW9FvNiyUSEekQBiLSjpTbwMNobVehuopaNxERvVbFPJGDiIiISI10OhA9fvwY48ePh7e3N3x8fBAaGorc3FwAQFJSEoYOHQoPDw90794d586dU3ru+fPn0bNnT8hkMnz22WdISkrSxiYQEVUIUqkEhoZStfxIpRJtbw6RynT2kJkgCBg/fjysrKywZcsWpKenY8qUKZBKpZg0aRLGjh2L+vXrY8+ePThx4gTGjRuHw4cPo3r16njw4AHGjh2LL774Aj4+PggPD0dgYCAOHDjw3/NNiIjoJalUAltrU0gM1PORwC8NUEWks4EoISEBUVFR+P3332Fvbw8AGD9+PObNm4e2bdsiKSkJ27dvh7m5OVxcXHDhwgXs2bMHX3zxBXbt2gVXV1cMGzYMABAaGor3338fFy9eRIsWLbS5WUREOkcqlRSGIX5pgPSYzgYiBwcHrF69WgxDL2VmZiI6OhqNGzeGubm5OL1Zs2aIiooCAERHR8PLy0scMzMzQ5MmTRAVFcVARFTOpFKJWg6Z8FpNWsAvDZAe09lAZGVlBR8fH/GxQqHA5s2b0bJlSyQnJ6NKlSpK89vZ2eHRo0cA8Mbx0irt0bWX82nraJy+HwWUSCpuDzRZt7rW9br3u1QqgVVlcxiq4QKW+QUKZKRnVbhQJJVK1HZoXhCUQ6G2/9aoqqLU+apX664ofdf1+kpSUt1l6fvb9EBnA9GrFixYgJs3b2L37t1Yv349jI2NlcaNjY0hl8sBANnZ2a8dLy07O8tynV8dbGwqaXydusbaumL2QJOvXXms63Xv9wnbryDuSWaZl123igV+GuQJW1uLMi9DaxQFhdesKsdlaeNvjaoq6t+m19Wty31/F/v9kqb6XiEC0YIFC7Bhwwb85z//Qf369WFiYoK0tDSleeRyOUxNTQEAJiYmRcKPXC6HlZWVSutNTX0OoRT/cyqRFL5gpZnfwECq1jfus2cvUFCgUNvySqLuutUpLe0F8vOL74Eu1/26106X3yeve7+/rDvuSSZuPMh463Vp6v2tLuLrpsYLj/67B6r8rVFFefw70cRrp6m61d139vt/Xld3Wfr+8jllofOBaNasWdi2bRsWLFiADz74AADg6OiIuLg4pflSUlLEw2SOjo5ISUkpMt6oUSOV1i0IUOnNr+r86qKNdeoSbfVdHTRZt7rXpam+V8jXVs3n4rzag4rynq8INRanpLp1ve+6XNvrvKluTfVdp69DtHTpUmzfvh2LFy9Gjx49xOkymQw3btxATk6OOC0yMhIymUwcj4yMFMeys7Nx8+ZNcZyIiIjo33Q2EMXHx2PZsmUYOXIkmjVrhuTkZPHH29sb1apVQ1BQEGJjYxEREYGrV6+if//+AIB+/frh8uXLiIiIQGxsLIKCguDk5MRvmBFVMAYGvFAgEWmGzh4y++2331BQUIDly5dj+fLlSmO3bt3CsmXLEBwcDD8/Pzg7OyM8PBzVq1cHADg5OSEsLAxz5sxBeHg4PD09ER4ezosyElUQDhYmgKIAVlZmalkeLxRIRG+is4EoICAAAQEBJY47Oztj8+bNJY63a9cO7dq1K4/SiKicWZkZFn67ihcKJCIN0dlARETqY6CG6wIBhRdLFDR55iYvFEhEGsJARPQOK49DT8/Sc948IxFRBcNARPQOK49DTzwXj4jeRQxERPqAh56IiF5LZ792T0RERKQpDERERESk93jIjIgIhXepf9sLOKrr23xEpHkMRESk96RSCSpbm8OQgYZIbzEQEZHek0olMDSQYsL2K4h7klnm5bRv4ICJHzRUY2VEpCkMRERE/xX3JBM3HmSU+fkuDpXUWA0RaRL3DxMREZHe4x4iIqIKjCeDE6kHA1EFp857VPHGl0QVC08GJ1IfBqIKqjzuUfU0LYehiKgC4cngROrDQFRBlcc9qqRSCQMRUQXEk8GJ3h4DUUXHe1QRERG9NR54JiIiIr3HQERERER6j4GIiIiI9B4DEREREek9nlRNREQaxwtKkq5hICIiIo3iBSVJFzEQERGRRvGCkqSLGIiIiEgreEFJ0iXcX0lERER6j4GIiIiI9B4DEREREek9BiIiIiLSezypmoiIqJR4/aR3FwMRERFRKUilElhV5vWT3lUMRERERKUgkfD6Se8yBiIiIiIV8PpJ7ybu9yMiIiK9xz1ERERE7zieDP5m72wgys3NxYwZM3Ds2DGYmppi2LBhGDZsmLbLIiIi0ijeTLd03tlANH/+fFy/fh0bNmzAgwcPMHnyZFSvXh1du3bVdmlEREQaw5vpls47GYiysrKwa9curFq1Ck2aNEGTJk0QGxuLLVu2MBAREZFe4sngr/dO7j+LiYlBfn4+PD09xWnNmjVDdHQ0FAqFFisjIiIiXfROBqLk5GTY2NjA2NhYnGZvb4/c3FykpaVprzAiIiLSSe/kIbPs7GylMARAfCyXy0u9HKkUEIQ3zyeRqDY/ADSpbgUzY4NS1/IqFweLwl+qugNG5mVeDgDArq74q/QNEVkX65ZIKmbdQMWsuzTvd12sG6iY/Qb+V3dJvdf1uktSUep+2feX/60odb+qotVdls9WyVt8kU4iCKVdTcVx5MgRzJ49G7///rs4LT4+Ht27d8eff/4Ja2tr7RVHREREOuedPGTm6OiIZ8+eIT8/X5yWnJwMU1NTWFlZabEyIiIi0kXvZCBq1KgRDA0NERUVJU6LjIyEm5sbpG/ap0hERER6551MB2ZmZujTpw+mT5+Oq1ev4sSJE1i7di0+++wzbZdGREREOuidPIcIKDyxevr06Th27BgsLCwwfPhwDB06VNtlERERkQ56ZwMRERERUWm9k4fMiIiIiFTBQERERER6j4GIiIiI9B4DEREREek9BqJSys3NxZQpU+Dl5YU2bdpg7dq1AACFQoGgoCA0bdoU/v7+SE1NFZ9z+/Zt+Pn5geetl97jx48xfvx4eHt7w8fHB6GhocjNzQUAJCUlYejQofDw8ED37t1x7tw58XnJycn4+OOP0bRpU/zwww9KPd+yZQtCQkI0vi0VVUBAAL777jvx8c2bNzFgwADIZDL069cP169fF8fi4+PRu3dvNG/eHGFhYUrLmTdvHtavX6+psiskuVyOGTNmoHnz5mjdujUWL14svnfZ9/Lz8OFDjBo1Ck2bNoWvr69Sv9j38iGXy9GzZ0/8+eef4rTX/U0HgPPnz6Nnz56QyWT47LPPkJSUJI5FRkaiU6dOaNmyJXbu3Kn0vPHjx+PEiROqFylQqcycOVPo1auXcP36deHYsWOCp6encOTIEeH48eOCr6+vEBcXJ4wZM0aYM2eO+Jzx48cLx48f12LVFYtCoRAGDhwojBgxQrh9+7bw119/CZ07dxbmzp0rKBQKoVevXsI333wjxMXFCStWrBBkMplw//59QRAEISQkRBg7dqwQGxsrdOjQQThx4oQgCIKQm5srdOnSRXj8+LE2N63COHTokFC/fn1h8uTJgiAIwosXL4T3339fmDt3rhAXFyfMmjVLaN26tfDixQtBEAQhMDBQmDlzpnD9+nWhadOmwt9//y0IgiCkpqYKnTt3FrKzs7W2LRXBDz/8IHTp0kWIjo4Wzp8/L7Ro0ULYtm0b+17OBg4cKHz55ZdCYmKicPz4cUEmkwnHjh1j38tJTk6OMHbsWKF+/frCH3/8IQiC8Ma/6ffv3xc8PDyENWvWCLdv3xYmTJgg9OzZU1AoFIIgCMKHH34orFy5Ujh//rzg5uYmpKamCoIgCLdu3RL69u0rzqcK7iEqhaysLOzatQvBwcFo0qQJOnfujBEjRmDLli1ISEiAh4cHXFxc0LZtWyQkJAAAYmNjkZSUhI4dO2q5+oojISEBUVFRCA0NRb169eDl5YXx48fj0KFD+OOPP5CUlISZM2fCxcUFo0aNgoeHB/bs2SM+18fHB3Xr1oWHh4f4OuzevRtt27ZFlSpVtLlpFUJaWhrmz58PNzc3cdrhw4dhYmKCSZMmwcXFBcHBwahUqRJ+/fVXAIV979ChA5o0aYK6deuKfV+zZg0+/vhjmJqaamVbKoK0tDTs2bMHs2bNgru7O1q1aoVhw4YhOjqafS9H6enpiIqKwpgxY1C7dm106tQJPj4+uHDhAvteDuLi4jBw4EDcu3dPafqb/qbv2rULrq6uGDZsGOrVq4fQ0FDcv38fFy9eBFD4WnTu3BmtWrWClZUV/vnnHwDAsmXLEBgYCEkZ7vLKQFQKMTExyM/Ph6enpzitWbNmiI6ORtWqVREXFwe5XI6bN2+iWrVqAApflDFjxpTpRdFXDg4OWL16Nezt7ZWmZ2ZmIjo6Go0bN4a5+f/usNysWTPx9izVq1fHzZs3kZubi9jYWFSvXh1yuRwbNmzAyJEjNbkZFda8efPw4Ycfom7d/92FOjo6Gs2aNRPfxxKJBE2bNi3S94yMDNy7dw/Vq1fH06dPcfz4cQwaNEgbm1FhREZGwsLCAt7e3uK0gIAAhIaGsu/lyNTUFGZmZti7dy/y8vKQkJCAy5cvo1GjRux7Obh48SJatGiBHTt2KE1/09/06OhoeHl5iWNmZmZo0qSJOF6tWjXcvHkT9+/fR3p6OhwdHREXF4d79+6VeUcEA1EpJCcnw8bGBsbGxuI0e3t75ObmonXr1rCwsIBMJsO5c+cwcuRIxMfH4+7du+jUqZMWq654rKys4OPjIz5WKBTYvHkzWrZsieTk5CJ7eezs7PDo0SMAwLBhw3Dq1Cl4eHjAzs4OXbp0wd69e+Hj48O9Q6Vw4cIFXLp0CYGBgUrT39T3sWPHYvXq1WjRogXatm0LDw8PrFu3jv+3XApJSUmoUaMG9u/fj65du6Jjx44IDw+HQqFg38uRiYkJpk6dih07dkAmk6Fbt25o27YtBgwYwL6Xg48//hhTpkyBmZmZ0vQ39fpN49988w2mTJkiHrFxdHR86x0RhmV6lp7Jzs5WCkMAxMf5+fnYsmULUlJSYGtrC6lUim+++QZjxoxBdHQ0fvjhB+Tn5+P777/H+++/r43yK6wFCxbg5s2b2L17N9avX1/sayCXywEAtWvXxsmTJ5GWlgY7Ozvk5eVh/fr12LhxI7Zv345Vq1ahatWqmDdvHpycnLSxOTorNzcX06ZNw9SpU4v8US/pvf+y702bNsW5c+fw4sUL2NjY4NmzZzh69CgOHDiAJUuWYN++fWjQoAFCQ0NhY2OjsW2qCLKysnD37l1s374doaGhSE5OxtSpU2FmZsa+l7P4+Hh06NABn3/+OWJjYzFr1iy0atWKfdegN/X6TeNdunRB27ZtIZfLYWVlhfj4eCQmJqJDhw6YNm0aTp06hRYtWmDWrFkwMTEpVU3cQ1QKJiYm4ovw0svHLz9A7O3tIZVKER8fj4SEBHTq1AlBQUGYMGECFixYgIkTJ4rflqI3W7BgATZs2IAFCxagfv36Jb4G//4ANzAwgJ2dHQBg3759aNOmDQRBwMKFC7F161Z06tQJs2fP1uh2VARLly6Fq6ur0t65l0rTd2NjY/GP//r16zF48GDcunUL+/btw4EDB+Dk5ISlS5eW70ZUQIaGhsjMzMSiRYvg6emJLl26YPTo0dixYwf7Xo4uXLiA3bt3Y86cOXBzc4Ofnx8CAgKwfPly9l2D3tTrksb/vafJ1NQUVlZWAP53msqxY8dw8+ZNHD16FJmZmdiyZUupa2IgKgVHR0c8e/YM+fn54rTk5GSlF+Ol5cuXY8yYMUhPT0dCQgLatGkDV1dXAEBiYqJG666oZs2ahXXr1mHBggX44IMPABS+BikpKUrzpaSkFHs4LC8vD+vWrcPIkSMRHR2NOnXqwNHREW3btkVkZKRGtqEi+eWXX3DixAl4enrC09MTBw8exMGDB+Hp6alS39PS0vDrr79i8ODBuHz5MmQyGSwtLeHj48O+F8PBwQEmJiaoUaOGOK1OnTp4+PAh+16Orl+/DmdnZ6WQ07hxYzx48IB916A39bqkcQcHhyLLSkhIQGJiIjp37ozLly/D29sbpqamaNOmjUqvBQNRKTRq1AiGhobiyVxA4QmRbm5ukEr/18LExETEx8ejc+fO4nSFQgEAKCgo4PWISmHp0qXYvn07Fi9ejB49eojTZTIZbty4gZycHHFaZGQkZDJZkWXs378f77//PhwdHSGVSsXXID8/n69BMTZt2oSDBw9i//792L9/P3x9feHr64v9+/dDJpPhypUrYt8EQRD/+L9q/fr1GDRoEExNTSGRSPjefwOZTIbc3Fyl/1FKSEhAjRo12PdyVKVKFdy9e1dp70NCQgKcnJzYdw160990mUymFGays7Nx8+bNYl+L5cuXY/To0ZBIJG/1WjAQlYKZmRn69OmD6dOn4+rVqzhx4gTWrl2Lzz77TGm+f78oVlZWcHZ2xs6dO3H48GEAhee5UMni4+OxbNkyjBw5Es2aNUNycrL44+3tjWrVqiEoKAixsbGIiIjA1atX0b9/f6Vl5OfnY/369eI3yxo1aoS4uDhcunQJe/bsgYeHhxa2TLfVqFEDzs7O4k+lSpVQqVIlODs7o2vXrsjIyEBISAji4uIQEhKC7OxsdOvWTWkZ6enpOHLkiPhNGzc3N1y8eBE3b97EgQMH2PdivPfee2jfvj2CgoIQExODs2fPIiIiAoMHD2bfy5Gvry+MjIzw/fffIzExESdPnsSKFSvg7+/PvmvQm/6m9+vXD5cvX0ZERARiY2MRFBQEJycntGjRQmk5d+7cQVxcHDp37gyg8LU4deoU4uLicOTIEdVei7Jfakm/ZGVlCZMmTRI8PDyENm3aCOvWrVMav3PnjtCnTx+li0H98ccfQocOHQQfHx/xQoFUspUrVwr169cv9kcQCnv8ySefCK6urkKPHj2E33//vcgydu/eLcycOVNp2rp16wRvb2+hb9++QkJCgka2pSKbPHmyeGFGQRCE6OhooU+fPoKbm5vQv39/4caNG0We89NPPwlr1qxRmjZ37lyhWbNmwqeffiokJyeXe90VUUZGhjBx4kTBw8NDaNWqlRAWFib+DWHfy09sbKwwdOhQoWnTpkKnTp2EdevWse8a8O8LMwrCm/+mnzp1SujSpYvg7u4uDBkyRLh3716RZU6aNEn49ddfxcd5eXnCxIkThaZNmwrjxo0TL6pZGhJB4L49IiIi0m88ZEZERER6j4GIiIiI9B4DEREREek9BiIiIiLSewxEREREpPcYiIiIiEjvMRARERGR3mMgItIQX19fNGjQQPxp0qQJunbtivXr17/1sv/55x80aNAA//zzDwAgKSkJp0+fLnZM3eLi4vDVV1+hdevW8PT0xKBBg8R1q8PTp0/x6aefws3NDZMnT4a/vz/CwsJKnL9Bgwb4888/1bZ+dXhTzdpc7zfffIPz58+/1ftEoVAgKCgITZs2hb+/P1JTU8Wx27dvw8/Pr8gtFPz9/REXF6fyuojKCwMRkQZNmTIF586dw7lz53DixAmMGjUK8+fPx/79+99qudWqVcO5c+dQrVo1cT1Xr14tdkydLl++jAEDBsDS0hKrVq3Cvn374Ovri8DAQBw5ckQt6zhw4ADu3LmD/fv3Y/LkyQgLC8OwYcPUsmx998cff+Dx48do3br1W71PTp48iYsXL2LXrl2wtLRERESEOBYeHo7AwEBIJBKl54wdOxYzZsx4620gUhcGIiINsrS0hIODAxwcHFCtWjX07dsXrVq1wrFjx95quQYGBnBwcICBgYFKY29DEAQEBQWhe/fumDlzJpo0aYLatWsjICAA/v7+mD9/PgoKCt56PZmZmahduzZcXFxga2sLa2trVKpUSQ1bQMuWLcPgwYMBvN37JCEhAR4eHnBxcUHbtm2RkJAAAIiNjUVSUhI6duxY5DktW7ZESkoKLl269HYbQaQmDEREWmZoaAgjIyMAhYceVq9ejY4dO8Ld3R3+/v64deuWOO/hw4fxwQcfwM3NDd27d8eJEycAKB8W++6773Dx4kUsXboU/v7+SmMLFy7Ep59+qrT+xYsXY+jQoQCAjIwMTJw4EU2bNkWbNm0wa9YspbtR/9vly5dx586dYvfWBAQEICwsDFJp4Z+Y+Ph4DB8+HE2bNoWPjw+WLl0q3pE6LCwM33zzDaZNm4amTZuiVatWWLVqlTgWFhaGv/76SzwU9uphoKVLl6JVq1Zo0aIFdu3apVSHXC7H7Nmz0aJFC7Ro0QLffvst0tLSlHp27NgxdOrUCW5ubhg1apQ4DgBnzpxB3759IZPJ0Lt3b1y4cEEcO378OLp37w6ZTIb+/fvj4sWLxb/ApXD79m34+/vD3d0dH3zwAbZs2QIAeP78Odzc3PDHH3+I82ZmZsLNzU0MEmWtIyEhAZcvX0a7du2U+vHykFmDBg3w888/o2fPnnB1dcXHH3+MpKSkYpdVvXp1xMXFQS6X4+bNm+JepmXLlmHMmDFF9g695Ovri23btpWqXqLyxkBEpCV5eXk4duwYfv/9d/H/oMPDw7F27VpMmTIF+/btQ40aNTBixAhkZWUhNTUVkyZNwqhRo/Drr7+iX79++Prrr5U+wAEgODgYnp6eGDZsWJHzR3r06IHIyEilczyOHj2KHj16iM99/vw5tm3bhmXLluHatWuYOXNmsfXHxMSgUqVKcHFxKTJma2sLV1dXSCQSPH36FB9//DGqVKmCXbt2Ydq0adi8eTM2btyoVIOJiQn27duH4cOHY+HChUhMTMSwYcMwbNgweHp64ty5c/D09FRaz44dO7Bx40bMmTMH69evx549e5TGFy9ejOvXr2PVqlXYuHEjMjMzMWHCBKV5VqxYgcWLF2Pz5s24du0a1q1bB6Bw78aYMWPQuXNnMRgEBgYiOTkZMTExmDx5MsaMGYMDBw6gd+/eGDlyJO7evVtsr14nJycHI0eORLNmzXDgwAFMnjwZy5Ytw/79+2FpaQkfHx8cP35cnP/UqVOwtbVFs2bN3qqOs2fPwt3dHRYWFiXOExYWhuDgYOzduxfPnj3Djz/+WOx8Xbp0gYWFBWQyGc6dO4eRI0ciPj4ed+/eRadOnUpc/vvvv49z584VOb+ISBsYiIg0aNq0afD09ISnpyfc3d0xefJkDBkyBL1794YgCNi8eTMmTJiAjh07wsXFBbNmzYKBgQEOHDiAx48fIy8vD1WrVkWNGjUwbNgwLFu2DCYmJkrrsLS0hJGREczNzWFtba001qhRI9SuXVvcs3Tr1i3cv38fnTt3xr1793DixAksWLAADRo0gLu7O2bNmoV9+/bh+fPnRbbl+fPnr/0wfenQoUMwMzPDrFmz4OLigk6dOmHChAlYvXq1OI+1tTUmT54MZ2dnjBgxAtbW1rh+/ToqVaoEc3NzGBkZwcHBAcbGxkrL3rlzJ4YMGYIOHTqgUaNGmD17tjiWnZ2NzZs3Y8aMGXB3d0eDBg0wf/58XLx4UWmv2/jx4+Hu7g6ZTIZevXrh2rVrAIDdu3ejadOmCAwMFA8FDhkyBBkZGVizZg0GDhyIXr16wdnZGZ999hnatm1bpr0dBw8ehJ2dHb788kvUrl0bvr6+GD16tBgYe/TogePHj4uh4ejRo+jWrRskEslb1XHz5s1iw+y/ff7552jVqhXq16+PwYMH4/r168XOZ2xsjC1btuDs2bM4ceIEatasKe4dio6ORq9evdCtWzf8/vvvSs9zcXFBWloa7t+/X5pWEZUrQ20XQKRPxo8fjy5dugAATExMlM7ZSE1NRVpaGmQymTi/kZERXF1dER8fj48++gjt27fH559/jjp16qBjx44YMGAAzMzMVKqhe/fuOHbsGD766CMcO3YMrVu3hrW1Na5cuQKFQoG2bdsqza9QKHD37l24uroqTbe2ti42KL0qPj4eTZo0gaHh//7ceHp6Ijk5GRkZGQAAJycnpXNXKlWqhPz8/FIte+zYseLjunXrwtzcHEDhN+3y8vIwaNCgIttz584dNGnSBADg7OwsjllYWCAvLw8AkJiYKM7z0pdffimu98iRI9ixY4c4lpeXhzZt2ryx5lclJCQgJiZGae9XQUGB2I8OHTogODgY0dHRaNCgAc6ePSuGpbep4+nTp2jUqNFr5ympNyWxt7cX60pISECnTp3QvXt3fPPNN6hatSoCAgLwf//3f2KIt7GxEWtxcnJ6Y81E5YmBiEiD7OzslD5k/u3VPT0vFRQUQKFQQCKRYOXKlbh69Sp+++03HD9+HFu3bsXWrVthaWlZ6hq6d++OlStXIiMjA8eOHcPw4cPF9VhaWhY57AQAjo6ORaY1adIEWVlZiI+PL7KnISkpCTNmzMDs2bOL3a6X5w+9POn65TlU/1bawyivzvcyeL1c9tatW8WQ9JKdnZ14qLG4df97OcUpKCjAyJEj0adPH6Xppqampar53/Lz89GqVStMnTq12HFzc3N06NABR48exePHj2Fvbw93d/e3rkMikbzxpPeSevMmy5cvx5gxY5Ceno6EhAS0adNGrCkxMRENGzYE8L/3QUnnGBFpEg+ZEekIS0tL2NvbIyoqSpyWl5eHGzduoE6dOoiPj8e8efPg7u6Or776Cr/88guqVauGs2fPqrQeFxcXuLi4YPv27bhz5454jkedOnXw/PlzSCQSODs7w9nZGTk5OZg/fz7kcnmR5bi6usLFxaXY6yht2bIFMTExcHBwQJ06dXDjxg2lvQtXrlwRvzH2NurVqyce4gIKTwx+udepZs2aMDAwQFpamrg9FhYWCA0NVTqHqiTOzs6IiYlRmjZo0CD88ssvqFOnDv755x9xuc7OztixYwfOnDmj8jbUqVMHiYmJcHJyEpcVFRWFTZs2ifP06NEDp0+fxokTJ9C9e3el55a1jn+HQnVKTExEfHw8OnfuLJ5U/+8A/O8A++zZMwCAg4OD2usgUhUDEZEOGTp0KJYsWYKTJ08iPj4eP/zwA3Jzc9G9e3dYWVmJJzsnJSXh1KlTuH//Pho3blxkOebm5rhz506JH/w9evTA8uXL0bZtW/E8IBcXF/j4+ODbb7/F1atXcePGDQQFBSErKwtWVlZFliGRSDB16lTs378f06ZNQ0xMDOLi4vCf//wHGzduxNSpU2FgYIBevXpBLpdj6tSpiI+Px4kTJxAWFobBgwe/9Z6BTz/9FBs3bsTRo0dx+/ZtBAcHix/CFhYWGDBgAKZPn44///wTcXFxmDRpEu7evVuqwzODBw/GpUuXsG7dOty9excrV65EbGwsvLy8MHToUBw+fBgbN27EvXv3sH79eqxfvx61a9cucXl3797FmTNnlH4ePXqE3r17IycnR+zP6dOnERISAjs7O/G5bdu2xZMnT4oEorLU8VLjxo2VzqVSl+XLl2P06NGQSCSwsrKCs7Mzdu7cicOHDwOAUm23bt2Cvb19sXsgiTSNh8yIdMiwYcOQmZmJH374AZmZmfD09MSmTZtga2sLoPBbPwsXLsSKFStgZ2eHr7/+Gm3atClydeEBAwZgypQpGDFiRLFXKu7evTsWLVokfrvspfnz52P27NkYOnQoDA0N4ePjg++//77Eelu2bIkNGzZg2bJlGDp0KORyORo0aICVK1fCx8cHQGEwWb16NUJCQtCnTx/Y2tpiyJAhGDVq1Nu2Cx9++CGePXsmXh4gICBAaa/Od999h3nz5mH8+PHIy8tD8+bNERERUapr7dSqVQthYWFYtGgRFi9ejHr16mHFihVwdHSEo6Mj5s+fj7CwMMyfPx+1atXCokWL0Lx58xKXd/DgQRw8eFBp2uzZszFgwACsWrUKc+bMQZ8+fWBtbY1PPvlEqT/Gxsbo1KkToqOjxcNNAODh4aFyHS/5+Phg3rx5ePHihdqu63T37l3ExsZi3rx54rRZs2YhKCgI+fn5mDNnjtI5b5GRkWjTpg0PmZFOkAj8viMRkV7y9/dHv379ipyDpAmCIKBTp06YN28evLy8NL5+olfxkBkRkZ4aNWoUtm/frpV1//7776hSpQrDEOkMBiIiIj3Vpk0bVK1aFefOndP4upcvX47p06drfL1EJeEhMyIiItJ73ENEREREeo+BiIiIiPQeAxERERHpPQYiIiIi0nsMRERERKT3GIiIiIhI7zEQERERkd5jICIiIiK9x0BEREREeu//AaMGzuCuPXohAAAAAElFTkSuQmCC",
            "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": [
        "plot_predictions(ffnn_train_predictions, Y_train)\n",
        "plot_difference(ffnn_train_predictions, Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True Positives: 1587\n",
            "True Negatives: 3756\n",
            "False Positives: 149\n",
            "False Negatives: 1359\n",
            "\n",
            "Accuracy: 0.7798861480075902\n",
            "Precision: 0.9141705069124424\n",
            "Recall: 0.5386965376782077\n"
          ]
        }
      ],
      "source": [
        "print_confusion_matrix(ffnn_train_predictions, Y_train, 0.7)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### 5.1.2\n",
        "The performance of this model was middle of the pack after modifications were made. The data tables above show the distribution of predictions using two different metrics. Looking at the raw distribution of predictions, we can see that the model might fair best using a threshold between .7 and .8 to get the maximum results. In addition, when looking at the difference in distributions, we can see the model is making a lot of errors in which many false negatives are being predicted. This can be derived from both graphs. The prediciton distribution graph shows a fair amount of positives near the middle of the graph, which will all be missed with a high threshold. In the second graph we see larger amount of positives to the right of the graph signaling false negatives. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "102/102 [==============================] - 0s 2ms/step\n",
            "Generating \"ffnn_submission.csv\" file...\n",
            "Successfully created \"ffnn_submission.csv\"\n"
          ]
        }
      ],
      "source": [
        "ffnn_predictions = ffnn_model.predict(test_input_np[:, 3]).flatten()\n",
        "\n",
        "# Apply threshold\n",
        "THRESHOLD = 0.7\n",
        "ffnn_predictions[ffnn_predictions >= THRESHOLD] = 1\n",
        "ffnn_predictions[ffnn_predictions < THRESHOLD] = 0\n",
        "\n",
        "save_to_submissions_csv(test_input_np, ffnn_predictions, 'ffnn_submission.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  target\n",
              "0   0       1\n",
              "1   2       1\n",
              "2   3       0\n",
              "3   9       0\n",
              "4  11       1"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.read_csv('ffnn_submission.csv').head()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 5.2 Train and Evaluate the CNN Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "193/193 [==============================] - 4s 14ms/step - loss: 0.7237 - accuracy: 0.5306 - val_loss: 0.6533 - val_accuracy: 0.5875\n",
            "Epoch 2/10\n",
            "193/193 [==============================] - 2s 13ms/step - loss: 0.6209 - accuracy: 0.6701 - val_loss: 0.5798 - val_accuracy: 0.7012\n",
            "Epoch 3/10\n",
            "193/193 [==============================] - 2s 12ms/step - loss: 0.5476 - accuracy: 0.7294 - val_loss: 0.5098 - val_accuracy: 0.7595\n",
            "Epoch 4/10\n",
            "193/193 [==============================] - 2s 12ms/step - loss: 0.4848 - accuracy: 0.7721 - val_loss: 0.4892 - val_accuracy: 0.7770\n",
            "Epoch 5/10\n",
            "193/193 [==============================] - 2s 13ms/step - loss: 0.4684 - accuracy: 0.7826 - val_loss: 0.4871 - val_accuracy: 0.7755\n",
            "Epoch 6/10\n",
            "193/193 [==============================] - 3s 13ms/step - loss: 0.4613 - accuracy: 0.7857 - val_loss: 0.4884 - val_accuracy: 0.7653\n",
            "Epoch 7/10\n",
            "193/193 [==============================] - 3s 13ms/step - loss: 0.4546 - accuracy: 0.7896 - val_loss: 0.4866 - val_accuracy: 0.7770\n",
            "Epoch 8/10\n",
            "193/193 [==============================] - 3s 13ms/step - loss: 0.4495 - accuracy: 0.7909 - val_loss: 0.4870 - val_accuracy: 0.7711\n",
            "Epoch 9/10\n",
            "193/193 [==============================] - 3s 13ms/step - loss: 0.4443 - accuracy: 0.7990 - val_loss: 0.4929 - val_accuracy: 0.7638\n",
            "Epoch 10/10\n",
            "193/193 [==============================] - 3s 14ms/step - loss: 0.4402 - accuracy: 0.7982 - val_loss: 0.4905 - val_accuracy: 0.7682\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 0.44017520546913147\n",
            "Val Loss: 0.4905254542827606\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.7982157468795776\n",
            "Val Accuracy: 0.7682215571403503\n"
          ]
        }
      ],
      "source": [
        "train_and_analyze_model(\n",
        "    cnn_model,\n",
        "    X_train,\n",
        "    Y_train,\n",
        "    num_of_epochs=10,\n",
        "    num_per_batch=32,\n",
        "    validation=0.1,\n",
        ")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### 5.2.1 \n",
        "Using the scatter plots above, we can see that this model might actually be overfitting the data. During the validation, it does not generalize new data well and struggles a little."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "215/215 [==============================] - 1s 4ms/step\n",
            "Train Loss: 0.43194361795525693\n",
            "24/24 [==============================] - 0s 5ms/step\n",
            "Val Loss: 0.5099057625701744\n"
          ]
        }
      ],
      "source": [
        "cnn_train_predictions = cnn_model.predict(X_train[:, 3]).flatten()\n",
        "cnn_train_loss = calculate_loss(Y_train, cnn_train_predictions)\n",
        "print(f'Train Loss: {cnn_train_loss}')\n",
        "\n",
        "cnn_validation_predictions = cnn_model.predict(X_validation[:, 3]).flatten()\n",
        "cnn_validation_loss = calculate_loss(Y_validation, cnn_validation_predictions)\n",
        "print(f'Val Loss: {cnn_validation_loss}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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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": [
        "plot_predictions(cnn_train_predictions, Y_train)\n",
        "plot_difference(cnn_train_predictions, Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True Positives: 1613\n",
            "True Negatives: 3771\n",
            "False Positives: 134\n",
            "False Negatives: 1333\n",
            "\n",
            "Accuracy: 0.7858706758137498\n",
            "Precision: 0.9232970807097882\n",
            "Recall: 0.5475220638153429\n"
          ]
        }
      ],
      "source": [
        "print_confusion_matrix(cnn_train_predictions, Y_train, 0.7)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 5.2.2\n",
        "This model has a similar problem to the first model, the same pattern appears here where an decent chunk of false negatives are being mislabeled. One reason for this may be the text processing techinque and use of embeddings in combination. In contrast, negative results are being predicted with a reasonable amount of accuracy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "102/102 [==============================] - 0s 4ms/step\n",
            "Generating \"cnn_submission.csv\" file...\n",
            "Successfully created \"cnn_submission.csv\"\n"
          ]
        }
      ],
      "source": [
        "cnn_predictions = cnn_model.predict(test_input_np[:, 3]).flatten()\n",
        "\n",
        "# Apply threshold\n",
        "THRESHOLD = 0.7\n",
        "cnn_predictions[cnn_predictions >= THRESHOLD] = 1\n",
        "cnn_predictions[cnn_predictions < THRESHOLD] = 0\n",
        "\n",
        "save_to_submissions_csv(test_input_np, cnn_predictions, 'cnn_submission.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  target\n",
              "0   0       1\n",
              "1   2       1\n",
              "2   3       0\n",
              "3   9       0\n",
              "4  11       1"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.read_csv('cnn_submission.csv').head()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 5.3 Train and Evaluate the RNN Model\n",
        "\n",
        "> **NOTE:** This RNN model has the longest training time. If you are in a time crunch and are only interested in viewing our results, please view the already saved output for the next few cells."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "193/193 [==============================] - 119s 547ms/step - loss: 0.6519 - accuracy: 0.6076 - val_loss: 0.5534 - val_accuracy: 0.7362\n",
            "Epoch 2/10\n",
            "193/193 [==============================] - 99s 513ms/step - loss: 0.5288 - accuracy: 0.7457 - val_loss: 0.5017 - val_accuracy: 0.7566\n",
            "Epoch 3/10\n",
            "193/193 [==============================] - 99s 515ms/step - loss: 0.4947 - accuracy: 0.7674 - val_loss: 0.4997 - val_accuracy: 0.7566\n",
            "Epoch 4/10\n",
            "193/193 [==============================] - 97s 500ms/step - loss: 0.4834 - accuracy: 0.7734 - val_loss: 0.4887 - val_accuracy: 0.7697\n",
            "Epoch 5/10\n",
            "193/193 [==============================] - 94s 485ms/step - loss: 0.4741 - accuracy: 0.7810 - val_loss: 0.4818 - val_accuracy: 0.7799\n",
            "Epoch 6/10\n",
            "193/193 [==============================] - 98s 508ms/step - loss: 0.4710 - accuracy: 0.7815 - val_loss: 0.4940 - val_accuracy: 0.7609\n",
            "Epoch 7/10\n",
            "193/193 [==============================] - 97s 501ms/step - loss: 0.4586 - accuracy: 0.7899 - val_loss: 0.4816 - val_accuracy: 0.7799\n",
            "Epoch 8/10\n",
            "193/193 [==============================] - 95s 494ms/step - loss: 0.4538 - accuracy: 0.7953 - val_loss: 0.5022 - val_accuracy: 0.7653\n",
            "Epoch 9/10\n",
            "193/193 [==============================] - 97s 501ms/step - loss: 0.4498 - accuracy: 0.7908 - val_loss: 0.4894 - val_accuracy: 0.7566\n",
            "Epoch 10/10\n",
            "193/193 [==============================] - 97s 503ms/step - loss: 0.4435 - accuracy: 0.7968 - val_loss: 0.4822 - val_accuracy: 0.7741\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 0.44350114464759827\n",
            "Val Loss: 0.48224204778671265\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.796755850315094\n",
            "Val Accuracy: 0.7740525007247925\n"
          ]
        }
      ],
      "source": [
        "                                                                   \n",
        "train_and_analyze_model(\n",
        "    rnn_model,\n",
        "    X_train,\n",
        "    Y_train,\n",
        "    num_of_epochs=10,\n",
        "    num_per_batch=32,\n",
        "    validation=0.1,\n",
        ")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### 5.3.1\n",
        "\n",
        "This model is the most chaotic of all three. As we can see, it is not performing well in its current state. The model does not converge well, however it seems to generalize well. It is possible that if left training for a long enough time it will perform slightly better.\n",
        "\n",
        "After several trial and errors while working on this model, we expanded our bidirectional layers to three. This however was not the best decision. The thought process was that adding more layers can help the model learn word contexts of the tweets, rather then making predictions on single words, such as the FFNN. Another unforseen effect was the amount of time it would take to fully train a model. Some variations lasted for up to 30 minutes. We also learned that more complexity does not mean better results. Three bidirectional layers performed worse then a model with two layers.\n",
        "\n",
        "Out of all our experiments, this one is the worst in terms of accuracy and time taken to train. As we trained with different variations, it was noted that adding complexity does not necessarily add any benefit and may instead hinder the learning process. Despite these current results, we still think that using an RNN model as a basis may still help us.\n",
        "\n",
        "As of writing this, the model is still a work in progess and has shown promise thus far. We will continue to work on this model to try to get the highest kaggle score possible as it still is promising."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "215/215 [==============================] - 1s 4ms/step\n",
            "Train Loss: 0.43194361795525693\n",
            "24/24 [==============================] - 0s 4ms/step\n",
            "Val Loss: 0.5099057625701744\n"
          ]
        }
      ],
      "source": [
        "rnn_train_predictions = cnn_model.predict(X_train[:, 3]).flatten()\n",
        "rnn_train_loss = calculate_loss(Y_train, rnn_train_predictions)\n",
        "print(f'Train Loss: {rnn_train_loss}')\n",
        "\n",
        "rnn_validation_predictions = cnn_model.predict(X_validation[:, 3]).flatten()\n",
        "rnn_validation_loss = calculate_loss(Y_validation, rnn_validation_predictions)\n",
        "print(f'Val Loss: {rnn_validation_loss}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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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": [
        "plot_predictions(rnn_train_predictions, Y_train)\n",
        "plot_difference(rnn_train_predictions, Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True Positives: 1613\n",
            "True Negatives: 3771\n",
            "False Positives: 134\n",
            "False Negatives: 1333\n",
            "\n",
            "Accuracy: 0.7858706758137498\n",
            "Precision: 0.9232970807097882\n",
            "Recall: 0.5475220638153429\n"
          ]
        }
      ],
      "source": [
        "print_confusion_matrix(rnn_train_predictions, Y_train, 0.7)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### 5.3.2\n",
        "Here the same problem emerges, a large amount of false positves are being predicted. Interestingly however, it has done a very good job of prediciting negative values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "102/102 [==============================] - 19s 157ms/step\n",
            "Generating \"rnn_submission.csv\" file...\n",
            "Successfully created \"rnn_submission.csv\"\n"
          ]
        }
      ],
      "source": [
        "rnn_predictions = rnn_model.predict(test_input_np[:, 3]).flatten()\n",
        "\n",
        "# Apply threshold\n",
        "THRESHOLD = 0.7\n",
        "rnn_predictions[rnn_predictions >= THRESHOLD] = 1\n",
        "rnn_predictions[rnn_predictions < THRESHOLD] = 0\n",
        "\n",
        "save_to_submissions_csv(test_input_np, rnn_predictions, 'rnn_submission.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  target\n",
              "0   0       0\n",
              "1   2       0\n",
              "2   3       1\n",
              "3   9       0\n",
              "4  11       1"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.read_csv('rnn_submission.csv').head()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 6. Conclusion"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 6.1 Overall Results\n",
        "\n",
        "Overall, our models much better than the baseline, showing that they have learned something from the training process, rather than predicting 1 or 0 all the time. Below is a chart that shows the accuracy of all of our models.\n",
        "\n",
        "> **Note:** Some of these values change due to some inner randomness of each layer (despite using a set global seed), but the values are still very close each training time.\n",
        "\n",
        "\n",
        "Model | Training Loss | Training Accuracy | Validation Loss | Validation Accuracy | F1 Score\n",
        "---|---|---|---|---|---\n",
        "Baseline | 7.900914820187636 | 0.4308860020434973 | 7.687370260599765 | 0.41863517060367456 | 0.42966\n",
        "FFNN | 0.43176037073135376 | 0.8029196858406067 | 0.5280683636665344 | 0.7434402108192444 | 0.75176\n",
        "CNN | 0.4304109811782837 | 0.8103811740875244 | 0.5113658905029297 | 0.7594752311706543 | 0.74287\n",
        "RNN | 0.44599589705467224 | 0.7956204414367676 | 0.5282750725746155 | 0.7448979616165161 | 0.72816\n",
        "\n",
        "**Most General:** RNN\n",
        "\n",
        "**Highest F1:** FFNN\n",
        "\n",
        "**Smallest Validation Loss:** CNN"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 6.2 Future Work\n",
        "After sifting through the graphs on all the models, the main hurdle we seem to struggle with the most is the amount of false negatives the models continuously miss. The root of the problem may be the use of custom made pre-processing function for this project. It is present in all models, so it may be the biggest factor that hinders the learning process. One possibility for a better pre-processing function is to break apart words into n-grams, that way links can be broken down to individual websites that may appear frequeantly (e.g.common news site).\n"
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