Giovanni_&_Oscar_Milestone_3_Google_Colab.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "FWM79lj2LYI6"
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      "source": [
        "# Giovanni & Oscar | ML Final Project | Milestone 3\n",
        "\n",
        "Continuation of Milestone 2's Ipynb\n",
        "\n",
        "## Points (10 pts)\n",
        "## Tasks:\n",
        "\n",
        "1. Add a section to your colab with at least 2 different methods for learning about the data and understanding the differences between the classes.\n",
        "    > This can include visualization like histograms or tables/lists of features and their counts in each class, for example.\n",
        "    > The goal of data analysis is to help you understand the task more deeply.\n",
        "\n",
        "2. Everyone should submit a colab via Classroom even though we expect partners to submit identical copies.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPFnCCd7O6fN"
      },
      "source": [
        "### 0. Setup Dependencies and Milestone 2 Code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QiwopORsO-yC",
        "outputId": "414bbcbd-d798-478c-bf53-fdff0a3edf1b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "# Ensure that we have the newest version of pip installed\n",
        "%pip install -q --upgrade pip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pn1OWO_1PRrO",
        "outputId": "de288dcd-3a21-409a-ccaf-a4c30c20311b"
      },
      "outputs": [
        {
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          "text": [
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "# Install necessary libraries\n",
        "%pip install -q numpy\n",
        "%pip install -q pandas\n",
        "%pip install -q matplotlib\n",
        "%pip install -q seaborn\n",
        "\n",
        "# Helps avoid showing plots in a separate line\n",
        "# %matplotlib inline\n",
        "\n",
        "%pip install -q scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ycGSQ31nQRa8"
      },
      "outputs": [],
      "source": [
        "# Import the modules\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJCd5JzUNNuk"
      },
      "source": [
        "#### Load the Data in a Colab Notebook"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ct512u3DuP7E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "outputId": "21ec3399-7175-4b5d-b74b-e45270730775"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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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",
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              "    .colab-df-convert {\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Please update the Path here to the location of your train.csv and test.csv files\n",
        "path_to_csv = '/content/drive/MyDrive/Final_Project_Oscar_Giovanni'\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())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "Db58YW6mesfD",
        "outputId": "ad853d80-9d28-4038-d70d-4b005b9d58b8"
      },
      "outputs": [
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            "text/plain": [
              "   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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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-86370dd6-554c-439e-9488-177660cdfae9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-86370dd6-554c-439e-9488-177660cdfae9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-86370dd6-554c-439e-9488-177660cdfae9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "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())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uHyK_HA4esfE",
        "outputId": "9642b401-d675-4304-a937-758b76af61f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V3z56Q3ResfF"
      },
      "source": [
        "#### Split the training data into 90% training and 10% for validation (your experiments)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZSy4RfHdesfG"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the data into train and test\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X_data, Y_data, train_size=0.90)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5oc7Lz7VesfH",
        "outputId": "d71147e4-c2aa-49ac-8aa3-c937f410b065"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Text\n",
            "[5531 'flattened' nan\n",
            " \"Conklin being flattened. Shuffled feet instead of kick slide. Didn't get hands up into chest. http://t.co/67TjN9EgyK\"]\n",
            "\n",
            "Is Natural Disaster?\n",
            "0 No\n",
            "======================\n",
            "\n",
            "Text\n",
            "[5076 'famine' nan\n",
            " \"New article: Russian 'food crematoria' provoke outrage amid crisis famine memories read more at here http://t.co/9HiEiFHSmC\"]\n",
            "\n",
            "Is Natural Disaster?\n",
            "1 Yes\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()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q5h4hWDtesfH"
      },
      "source": [
        "#### Testing and Submitting a Baseline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KIl32eKQesfH"
      },
      "outputs": [],
      "source": [
        "# Returns a positive result, regardless of the input\n",
        "def baseline_model(text_inputs):\n",
        "  return 1\n",
        "\n",
        "baseline_model_np = np.vectorize(baseline_model, signature='(n) -> ()')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "STZxFRh6esfI",
        "outputId": "8fcbf670-b983-40c5-d3ed-a6a726fc165f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input:\n",
            "[5531 'flattened' nan\n",
            " \"Conklin being flattened. Shuffled feet instead of kick slide. Didn't get hands up into chest. http://t.co/67TjN9EgyK\"]\n",
            "\n",
            "Output\n",
            "0\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[5076 'famine' nan\n",
            " \"New article: Russian 'food crematoria' provoke outrage amid crisis famine memories read more at here http://t.co/9HiEiFHSmC\"]\n",
            "\n",
            "Output\n",
            "1\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[3833 'detonate' 'back in japan ??????????'\n",
            " 'Detonate (feat. M?.?O?.?P?.?)\\nfrom Grandeur by Apollo Brown http://t.co/GFDhFMPCEl']\n",
            "\n",
            "Output\n",
            "0\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[932 'blaze' nan\n",
            " 'Life is amazin same time its crazy niggas dey wanna blaze me hate it because i made it all it took was dedication n some motivation']\n",
            "\n",
            "Output\n",
            "0\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[1043 'bleeding' 'Baltimore, MD'\n",
            " '@DarrylB1979 yea heard about that..not coming out until 2017 and 2019 ?????? Vampiro is bleeding']\n",
            "\n",
            "Output\n",
            "0\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n"
          ]
        }
      ],
      "source": [
        "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6lt18RwvesfI"
      },
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-QXmrJW_esfJ",
        "outputId": "8762e9d9-efce-438c-fdcc-662b07191cfb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Loss: 7.9089810946339725\n",
            "Testing Loss: 7.614847890216753\n"
          ]
        }
      ],
      "source": [
        "training_loss = calculate_loss(Y_train, baseline_predictions_train)\n",
        "print('Training Loss:', training_loss)\n",
        "\n",
        "# Run on the validation data\n",
        "baseline_predictions_test = baseline_model_np(X_test)\n",
        "testing_loss = calculate_loss(Y_test, baseline_predictions_test)\n",
        "print('Testing Loss:', testing_loss)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-iug-2ZUesfJ"
      },
      "outputs": [],
      "source": [
        "# Create the submission CSV file for our Kaggle submission\n",
        "def save_to_submissions_csv(text_inputs, prediction_labels):\n",
        "  print('Generating \"submission.csv\" 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",
        "    'submission.csv',\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('Successfully created \"submission.csv\"')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J2SGTUScesfK",
        "outputId": "9f1615ba-bcbc-4bcd-89c9-fc4d14b668a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generating \"submission.csv\" file...\n",
            "Successfully created \"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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7JsiVarTesfK",
        "outputId": "0ab83ef1-331b-4f1b-9bd5-b29ac97d56e4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  target\n",
              "0   0       1\n",
              "1   2       1\n",
              "2   3       1\n",
              "3   9       1\n",
              "4  11       1"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# Look at the first few predictions to ensure things went smoothly\n",
        "pd.read_csv('submission.csv').head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g16z4c7sesfK"
      },
      "source": [
        "### 1. Visualize and Learn about the Data we are working with"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZxuZPug7esfL"
      },
      "outputs": [],
      "source": [
        "from collections import Counter\n",
        "\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",
        "  # 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",
        "      else:\n",
        "        neg_counter[word] += 1  # Negative entry\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",
        "  }\n",
        "  word_count_df = pd.DataFrame(data=result, columns=['word', 'pos count', 'neg count'])\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)}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "eL9ThtNoesfL",
        "outputId": "cf4561b2-6b35-4c8f-c251-7e6902aeb665"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "           pos count  neg count\n",
              "word                           \n",
              "and              512        927\n",
              "after            181         75\n",
              "i                292       1061\n",
              "new              132        292\n",
              "<ARTICLE>       2426       3372\n",
              "this             179        287\n",
              "at               305        237\n",
              "by               279        242\n",
              "from             247        183\n",
              "to               761       1195\n",
              "<LINK>          2510       2196\n",
              "as               171        160\n",
              "of               957        935\n",
              "on               418        444\n",
              "be               113        287\n",
              "in              1186        854\n",
              "is               348        590\n",
              "nan             1117       1479\n",
              "it               164        342\n",
              "for              399        489\n",
              "-                419        396\n",
              "with             188        382\n",
              "my               134        566\n",
              "that             181        357\n",
              "you              125        629"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7e91f35d-f28f-4f64-b2e0-af475ed1a509\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>word</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>and</th>\n",
              "      <td>512</td>\n",
              "      <td>927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>after</th>\n",
              "      <td>181</td>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i</th>\n",
              "      <td>292</td>\n",
              "      <td>1061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>new</th>\n",
              "      <td>132</td>\n",
              "      <td>292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>&lt;ARTICLE&gt;</th>\n",
              "      <td>2426</td>\n",
              "      <td>3372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>this</th>\n",
              "      <td>179</td>\n",
              "      <td>287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>at</th>\n",
              "      <td>305</td>\n",
              "      <td>237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>by</th>\n",
              "      <td>279</td>\n",
              "      <td>242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>from</th>\n",
              "      <td>247</td>\n",
              "      <td>183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>to</th>\n",
              "      <td>761</td>\n",
              "      <td>1195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>&lt;LINK&gt;</th>\n",
              "      <td>2510</td>\n",
              "      <td>2196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>as</th>\n",
              "      <td>171</td>\n",
              "      <td>160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>of</th>\n",
              "      <td>957</td>\n",
              "      <td>935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>on</th>\n",
              "      <td>418</td>\n",
              "      <td>444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>be</th>\n",
              "      <td>113</td>\n",
              "      <td>287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>in</th>\n",
              "      <td>1186</td>\n",
              "      <td>854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is</th>\n",
              "      <td>348</td>\n",
              "      <td>590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nan</th>\n",
              "      <td>1117</td>\n",
              "      <td>1479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>it</th>\n",
              "      <td>164</td>\n",
              "      <td>342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>for</th>\n",
              "      <td>399</td>\n",
              "      <td>489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>-</th>\n",
              "      <td>419</td>\n",
              "      <td>396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>with</th>\n",
              "      <td>188</td>\n",
              "      <td>382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>my</th>\n",
              "      <td>134</td>\n",
              "      <td>566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>that</th>\n",
              "      <td>181</td>\n",
              "      <td>357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>you</th>\n",
              "      <td>125</td>\n",
              "      <td>629</td>\n",
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              "  </tbody>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DEBUG: Total Words Len: 33580\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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IJIXFDREREUkKp4ITEZEolbZCsSmVFq8iKxRT6RIS9sHKyhrduvWolPOxuCEiItGRy2VwdrKFXKGwyPm1Gg0eZOSwwDGRhIR9qFmzJosbIiKqvuRyGeQKBdL3LEXuvVuVem5lrQao0z8GcrmMxU0VxeKGiIhEK/feLd3mu2JXuHFmTMy7WL58MW7evAEPD09Mnvye3q7bgiDgyy+3YO/e3UhLu43atesgIuJlvPLKa3rxfvzxB6xY8SnS0lLRpElTTJ48De+8MwaRkUMQFTW6zFy+++5bbN++Fdev/wNbW1s0a9YCU6dOh6trXQDAtWtXsWLFUpw/fw4KhQJt27bD+PGTULduwf23b/+LyMi+mDt3AcLCuuriLlv2CX766Ufs2LEPQEGPTGzsHKxfvwWrV3+G338/g9q1XTF8eBR69uwNABg/fhTOnTsDAAgJCQQAjBgR/cznYAwWN0RERCZy7949LFu2CK+99gbs7e2xenUcpk+fgu3bv4GVVcGf3GXLFmHfvj0YNmwkmjdviQsXzmPlyuWwsbFB//4RAIDLly/h/fen4oUXOmLChElITU3FBx+8h9zcvGfmsHXr5/jss0/Ru3c/jBo1Fvn5+Th9+hQyMjLg6loXaWmpGDcuGvXrN8DMmXORm/sEa9asxNtvj8KmTV+iZk07g5/3hx/ORJ8+/TF48KvYu3cPYmPnoFmzFnB398DkydMwd+5M2NjUwLhxMQCAOnXqGHwOQ7C4ISIiMhG1OhPLl6+Gp6cXAKBGjRqYMGEM/vzzAlq39kNKyi3s3LkdU6a8h379BgIA2rZth8ePH2PDhrXo23cg5HI5Nm/eiHr1nsNHHy2EXF4w6LlmzZqYO3dWmefPzs7G+vVr0LfvAPzf/72vO96xYyfdvlfbt2+FRpOPJUvioFI5AgCaNvXB669HIiFhHyIiBhv8vAcOfBkDB0YCAFq2bI0TJ47hxx8P44033oSHhydq1rRDzZo10bJlK4NjVwSnghMREZlI7dquusIGADw8PAEAd+6kAQBOnvwVANCpU2fk5+frfgIDg3Dv3j2kpxe0u3TpIkJCOuoKGwDo2PHFZ57/woXzePz4MXr37ldqm99/P4c2bQJ1hQ0ANG7sjiZNnsf5878b8Gz/Jyiove7/bW1tUbduPdy5k16hWKbAnhsiIiITcXBw0LttZWUNAMjNzQUAZGZmQBAE9OrVtdhjASAtLQ1169bDvXt34eTkrHdfzZp2UCptyjy/Wp0JoKDIKk1WlhrPP9+02HFn51q6xxvK3r748y58zpbA4oaIiKiSqFSOkMlk+OyzdbC2ti52f6NGjQEAtWrVRkbGA737Hj16iNzcJ8+MDwB3795BnTpupbRR4cGD+8WOP3hwDw0bFpxfqVQCAPLy8vXaZGWpyzy/WPCyFBERUSUJCGgLAMjMzISPT/NiP4WDeX18muPYsZ+g1Wp1j01M/PGZ8Vu29EWNGjWQkLCv1Da+vn44ffok1Or/FSo3bvyDa9euwte3NQDA2dkFVlZWuH79fzPV8vLydLOeDGVtbY0nTyqvJ4c9N0RERJWkUaPGGDgwEvPmzcKQIUPRvHlL5Ofn4+bNGzh79hTmz/8EADB06BuIjh6O99//P/TtOwBpabfx5ZdboFTa6I3DeZq9vT1GjIjGypXLodVq0bHji9BqBZw5cwo9evTE88/74OWXX8X+/fswadJ4DBs2Erm5T7B27Uq4udVFeHgfAIBcLseLL4Zh587taNCgIRwdnbBz53YIggCZTFbq+UvTuLEHDhz4FseOJaJ27dqoXdu1zEtnxmJxQ0REoqWs1UBy54yJeReNGjXGN9/swsaN62BrWxONGjVGWFgXXZumTX0wb94CfPbZcrz//rvw8PDC++/Pxttvj4adnX2Z8V97bTicnJyxfftWfPfdt6hZsyZatPCFs3PBGB43t7qIi1uDFSuW4sMPZ0AuV6Bt2yC8/fYkvWngMTH/h4ULP8LSpf9BzZp2GDJkKBo1aoyffvrR4Of82mvDkJJyE/PmfYDs7Cyzr3MjEwShWi6/qNFocf/+wzLbWFnJ4exshwcPHiI/X1tm2/IwZTwx5wYANjZWUKlscSt+im4BLmVdDzSIWlShc4j9+Van15bxxBFLKvHy8nJx795t1KpVD9bWSt3xqrr9QuF0a1N4OtapU78hJmYsli9fDX//AKPjmTo/U8TLyXlc4vuhkIuLXbn2F2PPDRERiY5WK+BBRk6JG2dqNKb7g1paPDFsnLlw4XzdlO3k5CRs2hSPpk290bq1v0XzqgoMKm6OHj2KtWvX4urVq8jOzoabmxu6du2K8ePH601/++GHH7B06VIkJyfjueeew6hRozBo0CC9WLm5uViyZAn27t2Lhw8fwt/fHzNnzoSnp6deu2vXrmHevHk4e/Ys7Ozs0K9fP8TExOhGchMRkTSVVmCYsrfAHPFMJStLjSVL/oPMzAzY29ujXbsOGDcupswxN1TAoOImIyMDvr6+GDp0KJycnHDlyhUsX74cV65cwfr16wEAp06dwvjx4xEREYHp06fjl19+wfvvvw87Ozv06PG/3UDnzZuHhIQETJs2DW5ubli1ahXeeOMN7N+/X1coZWZmYvjw4XB3d8fy5cuRlpaGBQsW4PHjx5g1q+xVGomIiKqyuXPni7bwEjuDipt+/fRXPGzXrh2USiVmzpyJtLQ0uLm5YeXKlfD19cWHH34IAGjfvj1u3ryJTz/9VFfcpKamYseOHfjggw8QEVGwj0arVq0QFhaGr776CtHR0QCAr776Cg8fPkRcXBycnJwAABqNBnPmzMHo0aPh5lbyHH4iIiKqvozu2yosOvLy8pCbm4tff/1Vr4cGAMLDw3Ht2jXculWwbf2xY8eg1Wr12jk5OSE4OBiJiYm6Y4mJiejQoYPuHADQs2dPaLVaHD9+3NjUiYiISIIqVNxoNBo8efIEf/75J1asWIHOnTujQYMGuHHjBvLy8oqNm/HyKthnIykpSfffWrVqwdHRsVi7wjaF7Z6OpVKp4OrqqteOiIiqtmo6cZeeYqr3QYVmS4WFhSEtrWBzr44dO+KTTwoWHcrMLNiTQqVS6bUvvF14v1qtLrb/RmG7wjaF7Z6OBQCOjo567SrKyqrs2q5wull5pp2VhynjiTk3AMVmOJR0LkOI/flWp9eW8cQRSyrx5PLCvZeePHPfpMK142QywBR/A8UcT8y5mTNeXl7B9hI2NtZGDZyuUHGzZs0a5OTk4OrVq1i5ciXGjBmDDRs2VDgJS5DLZXB2tnt2QwAqla1Jz23KeGLOzRznEPvzrU6vLeOJI5YU4j154oIHDzKgUMhgY2MDoPQvRxqNSVMTdTwx52b6eAIePXqChw8zUauWC2rVKt6xYYgKFTc+Pj4AAH9/f7Rq1Qr9+vXDf//7XzRp0gQAkJWVpde+cP+KwstQKpUK2dnZxeKq1Wq9S1UqlapYLKCgB+jpS1qG0moFqNWPymyjUMihUtlCrc4xyboKpown5twAwNpaAXv7GiXeV5FziP35VqfXlvHEEUtK8WrUUMHWNg+ZmcU3c9Qng1wu+//Tw01x+ULM8cScm3niKRQy1Kxpjxo1VHjwoORFdlUq28pZxM/b2xvW1ta4ceMGOnfuDGtrayQlJaFjx466NoXjYwrHz3h6euLu3bvFipSnx9h4enoWG1uTlZWFO3fuFBuLUxHlnWKn0WhNOh3PlPHEmltZbz5jziHW52uOeGLOjfHEE0sq8RwcXGBn5wSNJr/UNgqFDI6ONZGZ+QgajfF/UMUcT8y5mSeeHLVrq6BWP/7/7xXjYhpd3Pz+++/Iy8tDgwYNoFQq0a5dOxw8eBDDhw/XtUlISICXlxcaNCjYryMkJARyuRyHDh1CZGQkgILemGPHjmHs2LG6x4WGhmLVqlV6Y28OHDgAuVyO4OBgY1MnIiIRkcvlkMtLX6DVykqOGjVqICdHY7LtIcQaT8y5mSuewoRbbRhU3IwfPx4tW7aEt7c3atSogUuXLiE+Ph7e3t7o2rUrAOCtt97CsGHDMHv2bPTs2RO//vorvv32WyxZskQXp27duoiIiMDChQshl8vh5uaG1atXw8HBAYMHD9a1Gzx4MDZv3oxx48Zh9OjRSEtLw8KFCzF48GCucUNEREQlMqi48fX1RUJCAtasWQNBEFC/fn1ERkYiKipKtx1CYGAgli9fjqVLl2LHjh147rnnMG/ePPTs2VMv1owZM2BnZ4dPPvkEDx8+RJs2bbBhwwa9WVSOjo7YtGkT5s6di3HjxsHOzg4RERGYOHGiCZ46ERERSZFBxc2oUaMwatSoZ7br0qULunTpUmYbpVKJqVOnYurUqWW28/LywsaNGw1Jk4iIiKox7r5FREREksLihoiIiCSFxQ0RERFJCosbIiIikhQWN0RERCQpLG6IiIhIUljcEBERkaSwuCEiIiJJYXFDREREksLihoiIiCSFxQ0RERFJCosbIiIikhQWN0RERCQpLG6IiIhIUljcEBERkaSwuCEiIiJJYXFDREREksLihoiIiCSFxQ0RERFJCosbIiIikhQWN0RERCQpLG6IiIhIUljcEBERkaSwuCEiIiJJYXFDREREksLihoiIiCSFxQ0RERFJCosbIiIikhQWN0RERCQpLG6IiIhIUljcEBERkaSwuCEiIiJJYXFDREREksLihoiIiCSFxQ0RERFJCosbIiIikhQWN0RERCQpLG6IiIhIUljcEBERkaQYVNx89913eOuttxAaGgo/Pz/069cPO3bsgCAIujZDhw6Ft7d3sZ9r167pxcrKysL06dMRFBQEf39/TJgwAenp6cXOeebMGbzyyivw9fVFWFgY1qxZo3c+IiIioqKsDGm8ceNG1K9fH9OmTYOzszN+/vlnzJw5E6mpqRg/fryuXZs2bTB16lS9xzZo0EDvdkxMDK5evYrZs2fDxsYGS5cuRXR0NHbu3Akrq4K0rl+/jqioKAQHByMmJgZ///03Fi1aBIVCgaioqIo+ZyIiIpIwg4qblStXwsXFRXe7Q4cOyMjIwIYNGzB27FjI5QUdQSqVCn5+fqXGOXv2LI4dO4b4+HiEhIQAADw8PBAeHo5Dhw4hPDwcABAfHw9nZ2csXrwYSqUSHTp0wP3797Fq1SoMHToUSqXS0OdLREREEmfQZamihU2hZs2aITs7G48ePSp3nMTERKhUKgQHB+uOeXp6olmzZkhMTNRr16VLF70iJjw8HGq1GmfPnjUkdSIiIqomjB5QfPr0abi5ucHe3l537LfffoOfnx9atWqF119/HSdPntR7TFJSEjw8PCCTyfSOe3p6IikpCQDw6NEj3L59G56ensXayGQyXTsiMi+5XAYrK7nuR6HgPAQiEjeDLks97dSpU0hISNAbX9O2bVv069cP7u7uSE9PR3x8PEaMGIHNmzfD398fAKBWq+Hg4FAsnqOjIy5cuACgYMAxUHCJqyilUglbW1tkZmYakzoAwMqq7A/pwg9xU32YmzKemHMDCv4gPutchhD785XqayuTyeDgUKPYY7VaAVZWCovnV9XiiTk3xhNXPDHnVhXiVbi4SU1NxcSJE9GuXTsMGzZMd3zChAl67Tp16oTevXvjs88+w9q1ayueqYnJ5TI4O9uVq61KZWvSc5synphzM8c5xP58pfraLvriNG6lFXzhaODmgCmvBcDBoYapUgMgrudr7nhizo3xxBVPzLmJOV6Fihu1Wo3o6Gg4OTlh+fLluoHEJalZsyZefPFFHDx4UHdMpVIhNTW1WNvMzEw4OjoCgK5np7AHp1Bubi5ycnJ07SpKqxWgVpc9TkihkEOlsoVanQONRmvU+UwdT8y5AYC1tQL29iX/8avIOcT+fKX62hY+9lZaFq6l6PeWZmc/Rl6exqL5VbV4Ys6N8cQVT8y5WTKeSmVbrt4dg4ubx48fY/To0cjKysK2bdtKvLz0LJ6enjhx4gQEQdAbd5OcnIymTZsCKCiK6tWrV2xsTXJyMgRBKDYWpyLy88v3gmg02nK3rex4Ys2trDefMecQ6/M1Rzwx5wYUfEEQc35ijifm3BhPXPHEnJuY4xl0cSs/Px8xMTFISkrCunXr4Obm9szHPHr0CD/++CNatWqlOxYaGorMzEycOHFCdyw5ORkXL15EaGioXrvDhw8jLy9PdywhIQEqlUo3foeIiIioKIN6bubMmYMjR45g2rRpyM7Oxrlz53T3NW/eHOfPn8e6devw0ksvoX79+khPT8eGDRtw584dLFu2TNfW398fISEhmD59OqZOnQobGxssWbIE3t7e6Natm65dVFQU9u3bh8mTJ2PIkCG4fPky4uPjMXHiRK5xQ0RERCUyqLg5fvw4AGDBggXF7jt8+DBcXV2Rl5eHJUuWICMjA7a2tvD398ecOXPg6+ur137p0qWYP38+Zs2ahfz8fISEhGDGjBm61YkBoHHjxoiPj8eCBQswatQouLi4YMKECRg5cmRFnisRERFVAwYVNz/88MMz28THx5crloODA2JjYxEbG1tmuzZt2mD79u3liklERERk1Do3RIXkcpne2jZlrXNDRERkTixuyGhyuQxOTjW5ci0REYkCixsymlwug0Ih11vorY1PHQwLb27hzIiIqDpicUMmU3ShtwZ17J/RmoiIyDx4HYGIiIgkhcUNERERSQqLGyIiIpIUFjdEREQkKSxuiIiISFJY3BAREZGksLghIiIiSWFxQ0RERJLC4oaIiIgkhcUNERERSQqLGyIiIpIUFjdEREQkKSxuiIiISFJY3BAREZGksLghIiIiSWFxQ0RERJLC4oaIiIgkhcUNERERSQqLGyIiIpIUFjdEREQkKVaWToCIqia5XAYrq/99P9JqBWi1ggUzIiIqwOKGiAzi5GADQauFvX0NveNajQYPMnJY4BCRxbG4ISKD2NtaQyaXI33PUuTeuwUAUNZqgDr9YyCXy1jcEJHFsbghogrJvXcLuanJlk6DiKgYDigmIiIiSWFxQ0RERJLC4oaIiIgkhcUNERERSQqLGyIiIpIUFjdEREQkKSxuiIiISFJY3BAREZGksLghIiIiSWFxQ0RERJLC4oaIiIgkhcUNERERSYpBxc13332Ht956C6GhofDz80O/fv2wY8cOCIL+LsBff/01unfvjlatWqFv3744cuRIsVhZWVmYPn06goKC4O/vjwkTJiA9Pb1YuzNnzuCVV16Br68vwsLCsGbNmmLnIyIiIipkUHGzceNG2NraYtq0aVi5ciVCQ0Mxc+ZMrFixQtdm//79mDlzJnr27Im1a9fCz88P48ePx7lz5/RixcTE4Pjx45g9ezYWLVqE5ORkREdHIz8/X9fm+vXriIqKgqurK1avXo3hw4fj008/xfr164171kRERCRZVoY0XrlyJVxcXHS3O3TogIyMDGzYsAFjx46FXC7Hp59+il69eiEmJgYA0L59e1y+fBkrVqzA2rVrAQBnz57FsWPHEB8fj5CQEACAh4cHwsPDcejQIYSHhwMA4uPj4ezsjMWLF0OpVKJDhw64f/8+Vq1ahaFDh0KpVJri34CIiIgkxKCem6KFTaFmzZohOzsbjx49ws2bN/HPP/+gZ8+eem3Cw8Nx4sQJ5ObmAgASExOhUqkQHBysa+Pp6YlmzZohMTFRdywxMRFdunTRK2LCw8OhVqtx9uxZQ1InIiKiasKgnpuSnD59Gm5ubrC3t8fp06cBFPTCFOXl5YW8vDzcvHkTXl5eSEpKgoeHB2QymV47T09PJCUlAQAePXqE27dvw9PTs1gbmUyGpKQktGvXzqjcrazKru0UCrnef41lynhiys3QxxhzDjE8X3PHE1NufG2l+9oynrjjiTm3qhDPqOLm1KlTSEhIwNSpUwEAmZmZAACVSqXXrvB24f1qtRoODg7F4jk6OuLChQsACgYclxRLqVTC1tZWF6ui5HIZnJ3tytVWpbI16lzmjCfm3MxxDrE/X762fG0rOxbjSTuemHMTc7wKFzepqamYOHEi2rVrh2HDhpkkmcqk1QpQqx+V2UahkEOlsoVanQONRmv0OU0ZT0y5FT62vIw5hxier7njiSk3vrbSfW0ZT9zxxJybJeOpVLbl6t2pUHGjVqsRHR0NJycnLF++HHJ5wYkcHR0BFPS6uLq66rUver9KpUJqamqxuJmZmbo2hT07hT04hXJzc5GTk6NrZ4z8/PK9IBqNttxtKzuemHMzxznE/nz52vK1rexYjCfteGLOTczxDL649fjxY4wePRpZWVlYt26d3uWlwvExheNmCiUlJcHa2hoNGzbUtUtOTi62Xk1ycrIuRs2aNVGvXr1isQof9/RYHCIiIiLAwOImPz8fMTExSEpKwrp16+Dm5qZ3f8OGDeHu7o4DBw7oHU9ISECHDh10s55CQ0ORmZmJEydO6NokJyfj4sWLCA0N1R0LDQ3F4cOHkZeXpxdLpVLB39/fkNSJiIiomjDostScOXNw5MgRTJs2DdnZ2XoL8zVv3hxKpRJvv/02pkyZgkaNGqFdu3ZISEjA+fPnsWXLFl1bf39/hISEYPr06Zg6dSpsbGywZMkSeHt7o1u3brp2UVFR2LdvHyZPnowhQ4bg8uXLiI+Px8SJE7nGDREREZXIoOLm+PHjAIAFCxYUu+/w4cNo0KABevfujZycHKxduxZr1qyBh4cH4uLiivW0LF26FPPnz8esWbOQn5+PkJAQzJgxA1ZW/0upcePGiI+Px4IFCzBq1Ci4uLhgwoQJGDlyZEWeKxEREVUDBhU3P/zwQ7naRUZGIjIyssw2Dg4OiI2NRWxsbJnt2rRpg+3bt5c7RyIiIqreuCs4ERERSQqLGyIiIpIUFjdEREQkKSxuiIiISFJY3BAREZGksLghIiIiSWFxQ0RERJLC4oaIiIgkhcUNERERSQqLGyIiIpIUFjdEREQkKSxuiIiISFJY3BAREZGksLghIiIiSWFxQ0RERJLC4oaIiIgkhcUNERERSQqLGyIiIpIUFjdEREQkKSxuiIiISFJY3BAREZGksLghIiIiSWFxQ0RERJLC4oaIiIgkhcUNERERSQqLGyIiIpIUFjdEREQkKSxuiIiISFJY3BAREZGksLghIiIiSWFxQ0RERJLC4oaIiIgkhcUNERERSQqLGyIiIpIUFjdEREQkKSxuiIiISFJY3BAREZGksLghIiIiSWFxQ0RERJJicHFz/fp1zJo1C/369UPz5s3Ru3fvYm2GDh0Kb2/vYj/Xrl3Ta5eVlYXp06cjKCgI/v7+mDBhAtLT04vFO3PmDF555RX4+voiLCwMa9asgSAIhqZORERE1YCVoQ+4cuUKjh49itatW0Or1ZZaZLRp0wZTp07VO9agQQO92zExMbh69Spmz54NGxsbLF26FNHR0di5cyesrApSu379OqKiohAcHIyYmBj8/fffWLRoERQKBaKiogxNn4iIiCTO4OKmc+fO6Nq1KwBg2rRpuHDhQontVCoV/Pz8So1z9uxZHDt2DPHx8QgJCQEAeHh4IDw8HIcOHUJ4eDgAID4+Hs7Ozli8eDGUSiU6dOiA+/fvY9WqVRg6dCiUSqWhT4GIiIgkzODLUnK5aYbpJCYmQqVSITg4WHfM09MTzZo1Q2Jiol67Ll266BUx4eHhUKvVOHv2rElyISIiIukw24Di3377DX5+fmjVqhVef/11nDx5Uu/+pKQkeHh4QCaT6R339PREUlISAODRo0e4ffs2PD09i7WRyWS6dkRERESFDL4sVR5t27ZFv3794O7ujvT0dMTHx2PEiBHYvHkz/P39AQBqtRoODg7FHuvo6Ki71JWVlQWg4BJXUUqlEra2tsjMzDQqTyursms7hUKu919jmTKemHIz9DHGnEMMz9fc8cSUG19b6b62jCfueGLOrSrEM0txM2HCBL3bnTp1Qu/evfHZZ59h7dq15jilweRyGZyd7crVVqWyNem5TRlPzLmZ4xxif758bfnaVnYsxpN2PDHnJuZ4ZilunlazZk28+OKLOHjwoO6YSqVCampqsbaZmZlwdHQEAF3PTmEPTqHc3Fzk5OTo2lWEVitArX5UZhuFQg6VyhZqdQ40Gm2Fz2WOeGLKrfCx5WXMOcTwfM0dT0y58bWV7mtbmfGysx9Dq/3fzFqtVqjQch5V5fnyvWK+eCqVbbl6dyqluCmJp6cnTpw4AUEQ9MbdJCcno2nTpgAKiqJ69eoVG1uTnJwMQRCKjcUxVH5++V4QjUZb7raVHU/MuZnjHGJ/vnxt+dpWdiwxx5PJZBC0Wtjb19A7rtVo8CAjR6/gsUR+VSGemHMTc7xKWaH40aNH+PHHH9GqVSvdsdDQUGRmZuLEiRO6Y8nJybh48SJCQ0P12h0+fBh5eXm6YwkJCVCpVLrxO0REJD5yuQwyuRzpe5biVvwU3IqfgvQ9SyFXKCCXy54dgKiCDO65ycnJwdGjRwEAKSkpyM7OxoEDBwAAQUFBSEpKwrp16/DSSy+hfv36SE9Px4YNG3Dnzh0sW7ZMF8ff3x8hISGYPn06pk6dChsbGyxZsgTe3t7o1q2brl1UVBT27duHyZMnY8iQIbh8+TLi4+MxceJErnFDRFQF5N67hdzUZEunQdWIwcXNvXv38M477+gdK7z9+eefo27dusjLy8OSJUuQkZEBW1tb+Pv7Y86cOfD19dV73NKlSzF//nzMmjUL+fn5CAkJwYwZM3SrEwNA48aNER8fjwULFmDUqFFwcXHBhAkTMHLkyIo8XyIiIpI4g4ubBg0a4O+//y6zTXx8fLliOTg4IDY2FrGxsWW2a9OmDbZv317uHImIiKj64q7gREREJCksboiIiEhSWNwQERGRpLC4ISIiIklhcUNERESSwuKGiIiIJIXFDREREUkKixsiIiKSFBY3REREJCksboiIiEhSWNwQERGRpLC4ISIiIklhcUNERESSwuKGiIiIJIXFDREREUkKixsiIiKSFBY3REREJCksboiIiEhSWNwQERGRpLC4ISIiIklhcUNERESSwuKGiIiIJIXFDREREUkKixsiIiKSFCtLJ0BElUOh0P8uo9UK0GoFC2VDFSGXyyCXy3S3n35NiagAixsiiZPJZBC0WqhUtnrHtRoNHmTksMCpIuRyGZycapZYpMpkslIeRVQ9sbghkji5XAaZXI70PUuRe+8WAEBZqwHq9I+BXC5jcVNFyOUyKBRyLPriNG6lZQEAGrg5YMprAXq9OUTE4oao2si9dwu5qcmWToOMdCstC9dSMi2dBpGo8YItERERSQqLGyIiIpIUFjdEREQkKSxuiIiISFI4oJiIiEzi6XV4OIuLLIXFDRERGa20dXiILIHFDRERGa2kdXja+NTBsPDmFs6MqiMWN0REZDJF1+FpUMfewtlQdcX+QyIiIpIU9txUcdwMkYiISB+LmyqKmyESERGVzODLUtevX8esWbPQr18/NG/eHL179y6x3ddff43u3bujVatW6Nu3L44cOVKsTVZWFqZPn46goCD4+/tjwoQJSE9PL9buzJkzeOWVV+Dr64uwsDCsWbMGglC9/3gX3QzxVvwU3IqfgvQ9SyFXKDj9koiIqjWDi5srV67g6NGjaNy4Mby8vEpss3//fsycORM9e/bE2rVr4efnh/Hjx+PcuXN67WJiYnD8+HHMnj0bixYtQnJyMqKjo5Gfn69rc/36dURFRcHV1RWrV6/G8OHD8emnn2L9+vWGpi5JhZsh5qYm63Z8JiIiqs4MvizVuXNndO3aFQAwbdo0XLhwoVibTz/9FL169UJMTAwAoH379rh8+TJWrFiBtWvXAgDOnj2LY8eOIT4+HiEhIQAADw8PhIeH49ChQwgPDwcAxMfHw9nZGYsXL4ZSqUSHDh1w//59rFq1CkOHDoVSqazQEyciIiJpMrjnRi4v+yE3b97EP//8g549e+odDw8Px4kTJ5CbmwsASExMhEqlQnBwsK6Np6cnmjVrhsTERN2xxMREdOnSRa+ICQ8Ph1qtxtmzZw1Nn4iIiCTO5FPBk5KSABT0whTl5eWFvLw83Lx5U9fOw8MDMpn++BBPT09djEePHuH27dvw9PQs1kYmk+naERERERUy+WypzMyCxZtUKpXe8cLbhfer1Wo4ODgUe7yjo6PuUldWVlaJsZRKJWxtbXWxKsrKquzarnCatamWEzdlvLIGDVckvjG5GfqYys6vqsUzdW7GvFf42orntS3rMXK57JmfZ4acg58Dlo8n5tyqQrxqOxVcLpfB2dmuXG2fnm5tLFPHM2V8c+dm7DnE/lqYMp7YXwtTxxNTLuaOZ+rc7O1rmDSe2N97Yn5tTR1PzLmJOZ7JixtHR0cABb0urq6uuuNqtVrvfpVKhdTU1GKPz8zM1LUp7Nkp7MEplJubi5ycHF27itBqBajVj8pso1DIoVLZQq3OgUajrfC5zBHP2lpR6gdaReIbk1vhY8ursvOravFMnZsx7xW+tuJ5bct6LbKzHyMvT2NUbubMryR8r1Td3CwZT6WyLVfvjsmLm8LxMUlJSXpjZZKSkmBtbY2GDRvq2p04cQKCIOiNu0lOTkbTpk0BADVr1kS9evWKja1JTk6GIAjFxuIYKj+/fC+IRqMtd9vKilfWi2tMfFM/V1OfQ4yvhbnimSqWud4rpo4nplzMHc/UuWm1gmifqznOIebX1tTxxJybmOOZfEBxw4YN4e7ujgMHDugdT0hIQIcOHXSznkJDQ5GZmYkTJ07o2iQnJ+PixYsIDQ3VHQsNDcXhw4eRl5enF0ulUsHf39/U6RNRFVM43qTwx1TX7Imo6jK45yYnJwdHjx4FAKSkpCA7O1tXyAQFBcHFxQVvv/02pkyZgkaNGqFdu3ZISEjA+fPnsWXLFl0cf39/hISEYPr06Zg6dSpsbGywZMkSeHt7o1u3brp2UVFR2LdvHyZPnowhQ4bg8uXLiI+Px8SJE7nGDVE1J5fL4ORUs8Q91p6eiUlE1YfBxc29e/fwzjvv6B0rvP3555+jXbt26N27N3JycrB27VqsWbMGHh4eiIuLK9bTsnTpUsyfPx+zZs1Cfn4+QkJCMGPGDFhZ/S+txo0bIz4+HgsWLMCoUaPg4uKCCRMmYOTIkRV5vkQkIXK5DAqFHIu+OI1baQVj8xq4OWDKawHchoSoGjO4uGnQoAH+/vvvZ7aLjIxEZGRkmW0cHBwQGxuL2NjYMtu1adMG27dvNyhPU+LO20TidistC9dS9JeGeHp6NH9viaqPajsVvDy48zZR1ePkYANBqy02Q4y/t0TVB4ubMhTdebtwU0plrQao0z8GcrmMH5JEImRva83fW6JqjsVNORTuvE1EVQd/b4mqL86ZJCIiIklhcUNERESSwuKGiIiIJIXFDREREUkKBxQTEVVxXNOHSB+LGyKiKopr+hCVjMUNEVEVxTV9iErG4oaIqIrjmj5E+jigmIiIiCSFxQ0RERFJCosbIiIikhQWN0RERCQpLG6IiIhIUljcEBERkaRwKjiRxMjlMsjlMr3bRETVCYsbIgmRy2VwcqoJhYKdskRUfbG4IZIQuVwGhUKORV+cxq20LABAG586GBbe3MKZERFVHhY3RBJ0Ky0L11IyAQAN6thbOBsiosrFvmsiIiKSFBY3REREJCksboiIiEhSWNwQERGRpLC4ISIiIklhcUNERESSwuKGiIiIJIXFDREREUkKixsiIiKSFBY3REREJCncfoGIiIhE4elNf7VaAVqtYHAcFjdERERkUTKZDIJWC5XKVu+4VqPBg4wcgwscFjdERERkUXK5DDK5HOl7liL33i0AgLJWA9TpHwO5XMbihoiIiKqm3Hu3kJuabHQcFjdERKTHVOMeiCyFxQ0REQEw/bgHIkthcUNERABMP+6BxKe69MqxuCEiIj2mGvdA4lHdeuXMsojfrl274O3tXexn0aJFeu2+/vprdO/eHa1atULfvn1x5MiRYrGysrIwffp0BAUFwd/fHxMmTEB6ero50iYiIpKkor1yt+Kn4Fb8FKTvWQq5QgG5XGbp9EzOrD0369atg4ODg+62m5ub7v/379+PmTNnYsyYMWjfvj0SEhIwfvx4fPHFF/Dz89O1i4mJwdWrVzF79mzY2Nhg6dKliI6Oxs6dO2FlxY4nIiKi8qouvXJmrQ5atGgBFxeXEu/79NNP0atXL8TExAAA2rdvj8uXL2PFihVYu3YtAODs2bM4duwY4uPjERISAgDw8PBAeHg4Dh06hPDwcHOmT0RERFWQRfaWunnzJv755x/07NlT73h4eDhOnDiB3NxcAEBiYiJUKhWCg4N1bTw9PdGsWTMkJiZWas5ERERUNZi1uOnduzeaNWuGLl26YPXq1dBoNACApKQkAAW9MEV5eXkhLy8PN2/e1LXz8PCATKZ/PdDT01MXw5TkchmsrOS6HylehyQiIpI6s1yWcnV1xdtvv43WrVtDJpPhhx9+wNKlS5GWloZZs2YhMzMTAKBSqfQeV3i78H61Wq03ZqeQo6MjLly4YHSeVlb/q+1kMhkcHGoUmyZXmvK2K+kxFXns08oqvCo7N0MfY+l/O7HHE9NrIfbXVuz5mTJWZTxXYz5XqtprIfZ4ps5NTH8zSmLq/MxS3HTs2BEdO3bU3Q4JCYGNjQ02bdqEMWPGmOOUBpPLZXB2tit2fNEXp3ErLQsA0ManDoaFNy/x8U9PpzOEMY81d3xz52bsOUydn5jjif21MHU8sT9fvrbiiSemXMwdT+zvFbHmV2nTjXr27In169fjr7/+gqOjI4CCad6urq66Nmq1GgB096tUKqSmphaLlZmZqWtTUVqtALX6ke62QiGHSmWLW2lZuJZS0HPUoI59qY9Xq3Og0WgNOmfhOSry2KdZWytgb19DFLkVPra8LP1vJ/Z4YnotxP7aij0/U8aqjOdqzOdKVXstAMDKSgF7OyVkcv2eAUGrQab6CQTBsLVfxPJeKYmY/mYYk59KZVuunhyLzKX29PQEUDCmpvD/C29bW1ujYcOGunYnTpyAIAh6426Sk5PRtGlTo/PIz6/4C6LRaCv8eGMeW6isF9fSuZnzHKbOT8zxxP5amDqe2J9vdXhtzfW5Yup4pspFoZCXuiKzIAgWz8+UscT+N8PU+VXabKmEhAQoFAo0b94cDRs2hLu7Ow4cOFCsTYcOHaBUKgEAoaGhyMzMxIkTJ3RtkpOTcfHiRYSGhlZW6kREJGGFa7/kpibrihyq2szScxMVFYV27drB29sbAHD48GFs374dw4YN012GevvttzFlyhQ0atQI7dq1Q0JCAs6fP48tW7bo4vj7+yMkJATTp0/H1KlTYWNjgyVLlsDb2xvdunUzR+pERERUxZmluPHw8MDOnTuRmpoKrVYLd3d3TJ8+HUOHDtW16d27N3JycrB27VqsWbMGHh4eiIuLg7+/v16spUuXYv78+Zg1axby8/MREhKCGTNmcHViIqpU1WXDQSIpMEuFMGPGjHK1i4yMRGRkZJltHBwcEBsbi9jYWFOkRkRkkOq24SCRFLD7g4ioDEU3HHx60KlcLmNxQyRCLG6IiIqQy2V6C4oV/n912XCQyu/p94qpFrQj47G4ISL6/+RyGZycavKPFD1Tae8VrVYotmUQVT4WN0RE/59cLoNCIS/3SuVUtZTWK1fRWE+/Vxq4OWDKawGwspLrFgDkwHPLYHFDRPSU8q5UTlWHuXrlir5XnBxsIGi1eivtGjvwnLP0KobFDRERSV5l9MrZ21rrDT43ZuA5Z+kZh8UNERFVG5XRK2eKweecpWccFjdEREQiJfZZemK9bMbihoiomjLlAFuqXsR+2YzFDRFRNVSVp72LtbegOhH7ZTMWN0RE1VBVnPYu9t6C6kisl81Y3BARVWNVadq72HsLSDxY3FC1wGXSiaRDrL0FJB4sbkjyuEw6EVH1wuKmiqisWQ1iH6hXkfzKWiads0PIEsT+e0ZU1bG4qQIqY1aD2AfqmSK/omMLjMXLXFQRYv89I5IKFjdVQGXMahD7QD0x5cfLXFRRYnofE0kZi5sqpKosG25OYsiPl7nIWGJ4HxNJGYsbogoy1WUuXuIiIjItFjdEJiSXy2Bl9b/i5FkDRXmJi4jI9FjcULVmaDFSGicHGwhaLezta+gdf9ZAUV7iIiIyPRY3VC1VtBgpjb2ttVEDRU05k4uIqLpjcUPVkrHFSGk4UJSIyPJY3FC1xmKEiEh6OC2DiIiIJIU9N2bEKb5kDFMNdiYi8ausLXYqSuz5PY3FjZlwiq9xqtovkimZerCz2LDoJ9JXGVvsGEPs+ZWExY2ZcIpvxVXFXyRTMtdgZzFg0U+GqC5fcipjix1jiD2/krC4MTNO8TVcVfxFMgcpDnYuq+i3spJDEP5XuPEyXPVWHb/kVMYWO8YQe35Fsbgh0apKv0hkmKKvrdQvw1HF8EsOGYPFTTVVXbp7SfykfBmOjMcvORVT3T/jWdxUQ9Wxu5fET4qX4YgsoSp8xpu7+GJxUw2xu5eo8hT9EK9u357JMsT+GV8ZxReLm2qM3b1E5lUVvkGTdIn1M74yii8WN0REZvL0h7iYvj0TWZo5iy9+nSAiMrPCD/H0+48snQpRtcCeGwvgsvpERETmw+KmEnE9DyIiIvNjcVOJuJ4HERGR+VWJMTfXrl3DiBEj4Ofnh+DgYCxcuBC5ubmWTqvCCtfzyE1N1hU5REREZBqi77nJzMzE8OHD4e7ujuXLlyMtLQ0LFizA48ePMWvWLEunR0RERCIj+uLmq6++wsOHDxEXFwcnJycAgEajwZw5czB69Gi4ublZNkEiIiISFdFflkpMTESHDh10hQ0A9OzZE1qtFsePH7dcYkRERCRKoi9ukpKS4OnpqXdMpVLB1dUVSUlJFsqKiIiIxEomCIKop+i0aNEC77zzDkaNGqV3vHfv3vD398fcuXMrFFcQ9NeWkckAuVyOjKwnyNdoAQA2SgUcaiqheZgJQZNf0E5hBYWdI7RaLcr6lzNlPDHnxnh8bRmv/PHEnBvjmT6emHOrqvHkchlksmfv0VZtixsiIiKSJtFfllKpVMjKyip2PDMzE46OjhbIiIiIiMRM9MWNp6dnsbE1WVlZuHPnTrGxOERERESiL25CQ0Px888/Q61W644dOHAAcrkcwcHBFsyMiIiIxEj0Y24yMzPRq1cveHh4YPTo0bpF/Pr06cNF/IiIiKgY0Rc3QMH2C3PnzsXZs2dhZ2eHfv36YeLEiVAqlZZOjYiIiESmShQ3REREROUl+jE3RERERIZgcUNERESSwuKGiIiIJIXFDREREUkKixsiIiKSFBY3REREJCksboiIiEhSWNwQUZWTnZ0NLtFFRKVhcWMmubm5OHToEG7evGnpVCQlNzfX0imQCLRt2xZ//PEHAGDYsGG4du2ahTMisRMEARkZGWb7DNFqtRg2bBj++ecfs8Qnw1hZOgFL27Nnj0Ht+/fvX652SqUSkydPxrp169CwYUPDE6vm9uzZg6ysLAwdOhQAcPnyZYwfPx63bt1CQEAAli5dilq1apU7XrNmzbBt2zb4+voWu+/ChQuIjIzEX3/9Ve54165dg5eXV7nbl8fff/+N1NRUPHnypNh93bp1MzjelStXcPr0aWRmZsLR0REBAQF4/vnnTZGqUbKzs5GbmwsXFxfdsb179+LatWvo0KED2rdv/8wYNjY2un+n3377DQ8fPjRbviQNeXl5eOGFF/DZZ5+hU6dOJo8vCIJJ34v37t0r8bPgueeeK3eMxMREdOzYETKZzOh83nvvPYwdO7bEv2cpKSmIi4vD/PnzjT6PqVT74mbatGl6twvfBEW7vIu+Mcpb3ACAp6cnbt++bVR+ffr0wSeffIKmTZuiT58+ZbaVyWTYu3evQbEDAwPRtm1bBAYGok6dOkblakrx8fEYPHiw7vbcuXNhbW2N6dOnY/PmzVi8eDE++uijcscr6xKGRqOBQqEwKL9evXrBz88PERERCA8PR82aNQ16fFGXL19GTEwMkpOTS8xTJpMZVHjl5ubi3XffxaFDhyAIApRKJXJzcyGTydC9e3csXLjQqH3Zzp49C2tra7Rs2bJCj3/33XdRp04dzJkzBwAQFxeHuLg4ODo6Yu3atVi0aBHCw8PLjOHt7Y2FCxciNDQUAPD1118jMTGxxLYymQzjxo0zKMdjx47h4MGDJRabMpkMmzZtMigeULAJ8JUrV3D79m2EhobC0dERT548gbW1NeRywzrRk5KScOjQoVLzi42NNTi/ok6fPo2kpCQEBATA09PT4MdrNBr8/vvvSE1NLbGnxJDP0S5dumDFihXw8fEpdt/ly5fx1ltv4fDhw8+Mo1QqUbduXWg0mnKfu7I9ePAA8+bNw6FDh5Cfn693nyAIBn8WjBo1CnXr1sWAAQMwcOBAo75o7969G0OGDCkxxoMHD7Bnzx6Dipv33nvvmW2MKZaqfXFz8uRJ3f9fv34d77zzDvr164fu3bujdu3auHv3Lg4cOIC9e/di6dKlBsWeNGkSYmNj4eXlhVatWlUov5YtW8LW1hYA0KJFC5NU4IU6deqEkydP4uuvv4ZGo0HDhg0REBCAtm3bom3btkb9Ihw4cAA1atSo8DeklJQUXc/I/fv3cfr0aaxatQqhoaFwcXHBxx9//MwYd+7cQXp6uu52UlJSsSLmyZMn2Llzp0HfhgBg9erV2LVrF+bMmYPY2Fj06NEDERERaNOmjUFxAOD999+HQqHAypUr4e7uDmtra4NjFLV48WIcPXoUc+bMQXh4OOzt7ZGdnY2EhATMnz8fS5YswdSpUysUOycnB1FRUbC3t8ePP/5o8B9lAPjjjz/wwQcfACj4wN66dStGjx6NiRMnYv78+YiPj39mcfPBBx9g3rx5+OKLLyCTybB///5SC1RDi5t169Zh0aJFqF+/Pry8vODg4FD+J1cCQRCwZMkSbN68GTk5OZDJZNixYwccHR0xfvx4tG7dGuPHjy93vD179mD69OmwsbHBc889V+z9YuhnxOTJk6FUKnV/SL788ktd4alUKrF69Wp06NCh3PH+/PNPvP3227h9+3apxbohxU1KSkqpl5IeP36M1NTUcsd69dVXsXHjRoSEhMDGxqbcj6ssM2bMwMmTJzF69Gh4eXkZ/Vmwf/9+7NixA19//TVWr16NwMBAREZGonv37ibdePr69etwcnIy6DElFWlqtRq3b9+Gs7Mz3NzcjEtKIJ033nhDWL16dYn3rVq1Shg2bJhB8Xr37i0EBQUJPj4+Qvv27YXevXvr/fTp08cUaRvt8ePHws8//ywsX75cGDZsmODn5yf4+PgIoaGhFYqXkZEhtGrVSggKChKePHlSoRgBAQFCYmKiIAiCkJCQILRq1UoX67fffhN8fX2fGWP58uWCt7e34OPjU+qPt7e30Lx5c2Hbtm0VyvPBgwfCpk2bhP79+wve3t5C9+7dhbVr1wp37twpdww/Pz/h6NGjFTp/SUJCQoRNmzaVeN/GjRuFkJCQCsfetWuX4O/vL7Rs2VL44YcfKhSjZcuWwsmTJwVBEITz588LPj4+wo0bNwRBEIRffvlF8Pf3Nyiet7e38Pvvv1col5KEhYUJc+fONVm8xYsXC/7+/sKXX34p/PPPP4K3t7dw4cIFQRAEYevWrcKAAQMMitetWzchJiZGePTokUnye/HFF4X9+/frbnfq1EmYMWOGkJ2dLUyZMkV49dVXDYoXEREh9OnTR/j111+Fe/fuCWq1utjPszx+/Fh48OCBcP/+fcHb21s4fvy48ODBA72f1NRU4ZNPPhE6depU7tw+/PBDITg4WGjfvr0wYcIE4cMPPxTmzp2r92OMX3/9VXj48GGFHx8QECDs3r3bqBxKkp+fL3z//ffCW2+9JbRo0UIIDAwUZs+eLfzxxx9lPu6LL77Q/b3y8fERXnrppWJ/x7p16ya0aNFCmDRpkklyvXr1qtC3b1/hxIkTRsWp9j03RZ09exZvvvlmife1aNECK1euNCheixYtKtx1X5lsbGzg7+8PAMjPz0dOTg7Onz+PrKysCsX79ttvUbNmTTx58gT//e9/0atXL4Nj+Pj4YOvWrahbty42b96M9u3b675p/Pvvv+UabzNgwAAEBQVBEAQMHz4cs2bNQpMmTfTaWFtbw93dHc7OzgbnCABOTk4YNmwYhg0bhosXL2L+/Pn45JNPsHTpUnTs2BHDhw9/5hgSHx8f3L9/v0LnL0lmZmaplxI8PT2RmZlZ4di7du1C9+7dkZ2djV27diEsLMzgGLVr18bVq1cRGBiIo0ePon79+rpewpycHFhZGfax9Pnnn8PLy0tvjJGTkxMCAgKKvd7lkZGRgS5duhj8uNLs3r0bkyZNwuDBg4tdEmnUqJHBkw7S09Mxe/ZsXY+use7fv6+7JF142WzYsGGws7PDgAED8M477xgU7+rVq1i6dCmCgoIqnNPatWuxYsUKAAU9PVFRUaW2NaTX68iRI7rPkcIB6UXJZDLMmDHDwGz/x5jnDAAqlarCn0VlUSgU6NKlC7p06YK0tDRMmTIFX375Jb766is8//zzGD58OAYNGlTscXXq1NH9Dbty5Qo8PDz0xsoBBZ+hnp6eiIiIMEmuXl5eiI6Oxvz58/HNN99UOA6LmyJcXFyQkJCA4ODgYvft37+/2Iv6LAsWLDBVamZx9OhRnDp1CidPnsSFCxdQs2ZNtGnTBj169MCMGTPQokWLCsXduXMnevfujaysLOzcubNCxc3EiRMxZswY9O3bF3Z2dtiwYYPuvu+//75cl/nq16+P+vXrAyj4A9iiRQvY2dkZnMuzqNVq7N27F7t27cLFixfRqlUrvPTSS0hMTMSIESMwbty4Mj+AZ82ahalTp8LV1RXt2rUz+I/70zw9PfHNN98gJCSk2H179+6t0BgKALhx4wZOnTqFd955BxkZGZg4cSLu379v8O9Fjx498J///Ac///wzEhMT9b5QXLx4EY0bNzYonp+fn0nHGIWFheH06dMGXYopS0ZGRqmDzzUaTbGxFc8SGBiIy5cvmyw/JycnpKSkIDAwED/99BNcXV11A881Gg20Wq1B8dzd3Y0eVNu1a1fUr18fgiBg+vTpeOutt9CoUSO9NtbW1vDy8kKzZs3KHfeHH34wKi9zi4qKwubNmxEcHGz058DTrl27hh07dmDv3r1Qq9Xo0aMHXnrpJRw9ehQffPABfv/9d3z44Yd6j+natSu6du2qu13agGJTc3BwwI0bN4yKweKmiDFjxmDWrFm4ceMGunbtilq1auHevXv4/vvvcfLkyWIvvCEEQUB6ejpq1apl8jdtRY0ePRo1atRAREQE5syZg6ZNmxo9pufSpUv466+/MG/ePGRmZiIqKgq3b99GvXr1DIoTEBCAI0eO4J9//kGjRo2gUql090VERBT7oHuWoKAg7NmzB9u2bcM///xT4iyEM2fOGBTz+PHj2LlzJw4fPgwbGxv06dMHsbGxuoGPo0aNwsaNG/HZZ5+VWdx4eXnBz88Pb775JuRyebGxADKZDKdPny53XmPHjsU777yDlJQUdOvWDbVr18a9e/dw8OBBnDt3DsuWLTPoeRbatWsXGjRogMDAQOTn58PBwQF79+7FG2+8YVCcyZMnw87ODhcuXMDIkSMxatQo3X1//vknevToYVA8U48xGjRoEGbPno0nT57ghRde0HvvFTKk8Hd3d8fx48dLLEZ+++03g2ewTZo0Ce+++y5sbGwQHBxc4pggQ8Y/hIaGYtGiRbh06RJ2796Nfv366e67cuUKGjRoYFB+7733Hj766CN4e3tXeEahj4+P7vdIJpOhU6dOZunREIN58+bp3b527RpeeukltG3btsT3niE9Sw8fPsT+/fuxc+dOnD9/Hu7u7oiKikL//v11X0p69eqFF154AXPmzCnzb5ypZ0JlZGQUO5aXl4dr165h8eLFRs/slAkCV8Iq6siRI1i1ahX+/PNP5Ofnw8rKCs2bN8eYMWPQuXNng+P99NNPWL58OS5evAiNRoMdO3agRYsWmDlzJtq2bYu+ffua4VmUz9y5c3Hy5ElcuXIFrq6uullTQUFBFf5Qio2NxS+//IK9e/dCEAR07twZERERBs9WMbVvvvkGM2bMwIABA7B9+3YMGjQIWq0WP/zwA1QqFfr162dQ93ZYWBhSU1PRpk0bREZGomfPniUOULxw4QIiIiJw6dKlUmO99957+PbbbxEWFgYPD48SBxEakhsAHD58GCtWrMBff/2lm2XRrFkzjB8/vkLvY0EQEBYWhoiICF0uH330EX755Rfs27fP4Him1LFjR0RHR2PYsGHF7tu0aRPWrVuHn376qdzxSpqVAxT8kRUqMGNl165dmDlzJqKjo9GjRw/0798fK1asQGpqKhYuXIj58+c/cwB1afmV9mXEkPyysrIQGxuLCxcuoHnz5pg5cybs7e0BAEOGDEGbNm3w7rvvljtenz59cOfOHajVatSpU6dY8VWeWZ2GzBIFYNB7MC0tDRs3bsSZM2eQkZGhu4Q5fPhw4wexVoAhv48ymaxcM8MK+fn5AShYSuLll19GYGBgie0uX76MMWPGPLNnS61W4+DBg0hOTi5xkLchhZePj0+J719BEFCvXj2sWLECzZs3L3e8p4mjC0FEwsLCEBYWBq1Wq+tyr8iMEKBg7Mm7776Lnj17IjIyEjNnztTd17BhQ+zatcuixU1hPmq1GidPnsSpU6ewa9cufPTRR1CpVAgMDMSnn35a7nh5eXnYt2+f7pu4TCZD3759sXv37nIVNxs2bECfPn1Qu3ZtvctQJZHJZAb1GGzYsAFjx47FqFGjsH37drz66qto0aIFsrOzERUVZfDlqsLZUc8qAlu2bFlmYQMABw8exLRp0/Daa68ZlENpCtej2LVrFx49eoSsrCw4ODigZs2aSElJwXvvvWfwt7Djx48jPT0dAwYM0B0bMGAANm/ejD/++MPg2YBl9aIZ2lNl6jFGn3/+uUHtn2XgwIHIzMzE8uXLsXr1agDAuHHjYGtri5iYGIMKG6DgC4QpZ006ODiU+n748ssvDY5nilmd5polevnyZbz++uvIy8tDcHAwfHx8cO/ePXz11VfYuXMntmzZUulrQZnzUtmUKVPQt2/fEnuAimratOkz8/jnn38wePBg5ObmIicnBy4uLsjMzER+fj4cHR1hb29vUHFT0vvYxsYGbm5uaN26tdFXONhzY0bh4eEIDQ3FtGnToNFo0KJFC+zcuRMtWrTAkSNHMGPGDBw/ftzSaQIoGEj8xx9/4OTJk/jxxx9x5swZg7+hHjp0CJMmTUJiYqKuyzM5ORk9e/bEpk2b0K5duzIf7+Pjg+3bt8PX17fUb8+FDM3N398fq1atQrt27dCiRQusX79el8/333+P2NhYgz9k7t+/j02bNuH333/HnTt34OrqitatW2P48OEGjUPp1KkTPvzwQ92aLcYq+u/4tIosWAgUXAq5e/dusT/8ffv2RZs2bTB79uxyxyqrF83BwQH9+/c3qKeqf//+eP755/Gf//yn2H3vvvsurly5YvBinYDpF0F8+PAhzp49iwcPHsDR0RH+/v5GTzOvKHOunyVmUVFRyMjIwPr16+Ho6Kg7npmZiZEjR8LZ2Rnr1q2zWH579uzBiy++WOIluIyMDPz4448GTaMHgNu3b+P777/H7du3jeptGTNmDARBwLJly+Dn54edO3fCx8cHCQkJWLJkCT799FNRTaBhz81TTLl4182bN/Hiiy+WeJ+trW2FZyOZyi+//KIbUHz+/Hk8fvwYrq6uCAwMxKxZswwe+b9z506EhITo/WH38PDQ/SI8q7gp2sPxrN4OQ9nb2+t+sd3c3HD16lVdPhqNBg8ePDAo3u+//44333wTWq0WL7zwAtzd3XHv3j1s2bIFW7Zswfr169G6detyxRoxYgS2bNmCF154wezjsSqyHoVarcb3339f4vX4/v37Y9WqVZg+fXq5B+2auhfN1GOMzLEIorGFsKmLEXOunyVmZ86cwX/+8x+9wgYAHB0d8dZbbxl0+c0c3nvvPWzbtq3E4ubWrVt47733DCpuEhIS8H//938QBAEuLi4lrolU3uLm/Pnz+Oijj3Tv/by8PCgUCvTp00e3+OBXX31V7twKmWsldRY3RZh68S5XV1ckJSWVOJDw77//NnjhOFN74403UL9+fbRt2xZ9+vRBUFCQwQN1C6Wnp+PYsWNYsmRJsfv69++PBQsWYNasWbpr+ZWtZcuW+Pvvv9GxY0d07twZK1asgCAIsLKywpo1a3TXpstrzpw5aNKkCdauXav3nLKyshAdHY0PP/wQO3fuLFesGzdu4PLly0YNIty6davuEoJMJsOUKVOKjQHKzc1FSkoKunfvXq68ilq3bl2Jxdorr7yCli1bIi8vr9x/8K9fv442bdpAoVBAoVAgOzsbQEEBGh0djdjYWIwYMaLcuXXr1g1xcXFYsWIFPv74Y70xRnFxcQaPMTL1AGVTFMKmLkaKXoYyx6xOU47NMCWFQlHqgoC5ubkGr1RuamVdSFGr1QYX/kuWLEHXrl0xd+5co/+e5ebmwt7eHnK5HI6OjnoLpD7//PMGfyE190rqLG6K2Lp1K15//XWT/eL17t0by5cvh6enp64XRCaT4fLly1i3bh2GDBlikvNU1I8//oi6deuaJJajoyMOHTpU4qyoQYMGoWPHjgavCGrKJdxHjx6Nf//9FwAwYcIEpKSkIDY2FlqtFq1atTJ4JtzVq1exbNmyYsWag4MDoqOjMXHixHLHOnLkiG5c16lTp4rdX55vV+Zcj0KlUpXai2dnZ4egoCA8ePCg3B+8pu5FA6Bbw+PpMUYVsX//fkyaNAkvv/yyXs4vv/wycnJysG7dOoOKG1MUwuYuRkzJ1GMzTOmFF17A0qVL0axZM3h4eOjlvGzZMrzwwguVntPRo0f1BryvX78etWvX1mvz5MkT/PLLLwZNewcKegxfeeUVk1z+dHd3R0pKCtq2bYvmzZtj69atut7mbdu2Gbx9jzlXUgdY3Ogx9eJd48ePx5UrVzBixAjdpYDo6Gjcv38fnTp10psCawlFC5vbt2/j9u3b8PHxqdAfBRsbG92aMk+ztrYu9b7SmHoJdz8/P13vjEqlwsqVK5Gbm6v7NmKoxo0bQ61Wl3hfVlaWQWtBmGJAoTnWo4iLiyvX2JfU1FSMHDkSCQkJ5Ypr6l60omrWrGnUPl+A6Qcom7IQrgoWLFiA1q1b68ZmrFmzRm9sRkWXIjCFadOm4fXXX0evXr3w/PPP6y5hXr58GfXq1SvXfkem9s8//+g+A2QyGU6dOlWsx8La2hrPP/88Jk2aZFDsjh074ty5cyZZE6lXr1663pl33nkHUVFRCAoK0s0iNLToNvWXiKexuCnC1It3KZVKrFy5Er/88gt+/vln3UDCF154wSLfEEqybds2xMXF4c6dO7o9b1q0aIFx48YhKCgIw4cPL/Px9+7dg5OT0zO7cx8+fIi///673HsvzZ49G/b29ti0aROaNGli9B4rJVEqlRXu9nz33Xfx4Ycfol69enq9Gr/++ivi4uL0ZsZVNlOtRxEXFweNRlPmCrXJyckYOXIk8vLyyh3X1L1opmbqRRBNWQhXBeYam1FR48ePx7vvvovGjRvjt99+w9atW3Ho0CGcPn0aarUa7u7uGDRoEAYOHGiWRT6fZfjw4brP2c6dO+Ozzz575oSK8pozZw4mTpyIx48fo3379kat2VT0UrGfnx++/fZbJCYm4smTJ2jfvj2aNm1qUG7mXEkdAPeWKur48ePCSy+9JCxatEj4+eefhQsXLhT7MURKSoqQm5tb4n15eXlCSkqKKdKusA0bNggtWrQQFi1aJBw/flxvz5uNGzcKgwcPfmYMHx8fvX19NBqN0K1bN+HKlSt67c6dOyf4+PiUOzc/Pz/hxx9/LHf7yvD0niqF+4a1bdtW6Natm9C2bVvBx8dHCAoKEnr37m1Q7NTUVGHBggXCyy+/LHTr1k14+eWXhY8//lhITU0107N5tk2bNgne3t7C4sWLS7z/jz/+EDp06CB07txZ+Oeff4w615MnT4SsrCyjYpjKwYMHBR8fH2HIkCHChg0bhH379gkbN24UhgwZIjRr1kw4dOiQQfGOHj0qdOnSRfj111/1jv/yyy9Cly5dRPc+N1ZAQIDw22+/CYIgCEFBQcLBgwd19/38889C69atKzWfZs2aCefOnRMEofjnldSlpKQIgwcPLnGPvcJjltKvXz9hypQpJd43ZcoUoV+/fkbFZ89NESNHjgRQsK/J2rVr9QbtCRVYvKtLly7Ytm1biVNyL126VKEpuaa0ZcsWjB07FmPHji22542HhweSk5OfGUN46pKRIAi4fv16iSsAG8IUS7ibmrlmlYhx7Q0AGDZsGBQKBebNm4f8/Hy9mSS//PILxo4di/r16yM+Pt7g6+1PM6YXzdRMMUD56RlNWVlZGD58OBwcHODs7IwHDx4gKysLKpUKixYtKnVWZVVk6rEZxnJzc8MPP/yAWrVqQRAE3LlzR9dzWJLKnuhx6NAhXa/KoUOHntm+W7du5Y49depU3L59GzNnzoS7u7vRPeCmHAdprpXUC7G4KcLUi3c9/Ye/qNzcXIt/mKelpek2zHyatbU1Hj16VMkZ/Y8plnA3NXMN5Pz444/RsGHDUtfe+Pjjjy229sZrr70Ga2trfPDBB9BqtZg6dSoOHTqEyZMno0WLFlizZs0zFwiriowdoFydplc/zdRjM4w1fPhwfPzxx1izZg1kMlmp48gq8gXWFCZMmKBbl2rChAlltjU0v/Pnz+OTTz7RG49XUaYeB1n4JSIuLs4ksxyfxuKmCGN3dAUK9gW5du2a7vavv/6K1NRUvTZPnjzB/v37LX6t/bnnnsMff/xR4hij33//He7u7pWaz9Pfdu/cuYM+ffqUuIQ7YNiS62Im9rU3Xn75ZSgUCsycOROXL1/GL7/8gg4dOmD58uUm25larCo6QFnsM5rMqaSxGT/99JNu3IehYzOM9cYbbyAsLAxJSUl46623MGXKlEr/bCvL4cOH4erqqvt/U2rcuLHBG7OWxtTjIL/88kv06dPHZLMcn8bi5inGbq743XffIS4uDkBBJfvJJ5+U2E6lUpl8IzJDvfzyy4iLi4Ozs7OuqzM/Px8//vgj4uPjERMTU6n5VNdvu2Jde6PoxnZdunRBVlYWFixYgI4dO2L+/Pl48uSJ3u+IoYsDknQ9ffmi8LLjxYsXcfHiRYNX2TVW48aN0bhxYwwYMADdu3e3+BfLoorOJJ07dy7atm2Ltm3bokWLFkb/7r/33nv4+OOP8fzzzxvdA3716lUsXbrUJJ0AQMEXgI8//hjdunVDRESEyeIW4vYLRZhic8Xs7Gw8ePAAMpkMXbt2RVxcXLG1CaytreHq6iqKP+Tz5s3DF198AZlMBq1Wq1tv5dVXXy3XWhQ+Pj7o0aOHbl0GQRDwxRdfIDw8XG+dlbt37+LgwYPl7lIt3B+ppA+hlJQUxMXFWbw4NJUJEybg0qVLWL16dbG1N0aPHg1vb2+D9vgylZI2tiv8uCjpvWvJ8WMkHuW5fMH3SskmTZqEU6dOIT09Hba2tvDz80NAQADatm0Lf39/g4cymGIT00IDBgxAdHS0wXuhlUatVmPfvn3YuXMnLl68iIYNG2LQoEEYMGCASTYwZXFTRP/+/dG9e3eMGjVKbx+owmXhe/To8cyVU5s1a6YbRNy5c2esXLkS3t7elfQMKubmzZt6U9U7dOhQ7m5bQ6+LlndNl6L/jk+r6P5IYvXvv//i9ddfR2pqaolrb2zZsqXExRHNbdeuXQYV4EU31aTqKzIyEk+ePMGMGTNKvXxhqT21qorr16/j5MmTOH36NE6ePImUlBRYWVmhVatW2Lp1a7njTJs27Zm/w+X9kvjbb7/ho48+wuLFi00+DvLSpUvYuXMnvv32W6jVagQHB2PQoEHo3LlzhS9/8bJUEaZYFt7GxkbXVf/vv/8aPWvI3DQaDe7cuQMHBwfdCsLnzp3DuXPnADx79Lu5drQtq+auyP5IYvbcc8/pvsGIZe0NoGA3ayJDmfryRXVUeBktKCgIbdq0wb59+/Dbb7/h7NmzBsUxduxXZY2D9PHxwfvvv48333wTU6ZMQWJiIhITE+Hs7IwhQ4Zg1KhRqFGjhkExWdwUYYpl4b29vbFw4ULdDs9ff/01EhMTS2wrk8kwbtw4E2VvOFOPfi9LUlISDh06hDFjxpTaxtz7I4nRkydPdDNKhg0bhmHDhlk6JSKjiHEZh6ri2rVrOHnypO7n7t27aNKkCdq2bYshQ4agbdu2lZpPZYyDFAQBiYmJ2LlzJ44cOQKVSoWoqCi89NJLOHr0KLZs2YIrV65g+fLlBsVlcVOEKZaF/+CDD/TGsezfv7/UQWGWLm4qYxXgQleuXMGyZcvKLG7MuT+SWNnY2ODkyZN44403LJ1KMYYsRS+TyRAbG2vGbKiqEOMyDlVFr169UKNGDfTv3x+zZ89GQEBAsVmUlaloz095xkEa4saNG9i5cyf27NmD9PR0vPDCC1i0aBG6dOkCK6uC0sTPzw8tW7Y0eNsJgMWNHlMsC1+4aBVQ0NW2cePGEseNiIHYuo/NsT9SVRAcHIzjx4+jffv2lk5FT3nGNOXl5eHatWssbkhn7ty5ZV6+MGQQa3XTqVMnnDlzBjt27MDff/+Nc+fOoW3btmjTpo3FLk8X2rNnD4YMGVLiZ/KDBw+wZ88egyZ5dOvWDW5ubhg4cCAiIiJK3X/Q09OzQn9DWdwUYerNFT///HNRf3MRc/exVGZClcegQYMwa9YsPHz4EC+++CJq1apVrCu4vPu/mNKePXtKve/JkyfYtm0b1q1bB4VCgV69elVeYiRq1XVJB1NYtWoVBEHA33//rbs0tXPnTmRmZsLb2xtBQUFGbSZpDFOPg1y5ciVefPFF3Qzd0nh4eGDz5s0GxQY4W8rsjF03x5zMOfr9aQcPHkRMTIxkZjiZ0tOb5Bm77Yc5PX78GF9++SXi4+ORmZmJvn37YvTo0WjUqJGlUyOSnPT0dJw8eRJfffUVTp48WemfBUXHQV69ehUNGzYscxxkaeu6WQJ7bszom2++wcyZMzFgwACcPXu2xHVzLMkU3cdFF3ori1h7iCyl6E7F8+fPh62tbbHxRWLy6NEjbNmyBRs3bkRWVhYGDhyI0aNHV/o+PERSdvPmTZw6dQonT57EqVOncPPmTVhZWaF58+a6bSwqU1UeB8nixow2bNiAsWPHYtSoUdi+fTteffVVvXVzLH0N1RTdx+3bty9XjMIeCCrwww8/IDo6Go0bN8b06dNLXdPH0rKzs7F582Zs2rQJOTk5iIyMRHR0tEkW2SIifS+99BJsbGzg6+uLXr16ISgoCH5+fhbb5qQqj4NkcWNGplg3x5xMsf9NbGwsi5YKEPtOxQCwfPlybN68Gfn5+Rg8eDCioqJQq1atSs+DqLrYsmULfH19Lb6pckmq2jhIFjdmZIp1c8SOC71VjNh3KgaAFStWQCaTwdfXF0lJSXj//fdLbSuTybBy5cpKzI5IegIDAy2dgmSwuDEjU6ybI3aCIODIkSNo2LAhnn/++RLbXL58Gbdu3UJYWBh7ef4/se9UDEBvwTCOmSKiqoSzpczo3Llz+PfffxEeHg61Wo2pU6fi6NGjunVzFi9eXGWuX5Zmx44dWLBgAb799lvUrVu3xDapqano3bs3ZsyYUem7AVcFZS2ORUREhmNxU8mMWTdHjIYOHYrWrVtjypQpZbZbvHgxzp49W6H1CoiIiAzBy1KVTKlUinKwWEVdvHgRo0ePfma7oKAgg3azJcv7888/DWpviYUGiYhKwuKGjJKXl1euYk2pVOoGV1PVMGjQIIOm+YtloUEiIhY3ZJR69erh4sWLz1xc6s8//0S9evUqKSsyhc8//9zSKRARVQiLGzJKWFgY1q9fj169esHV1bXENnfu3MGGDRu4/1AVU97VUHNzcyWxrAERSUfZO1YRPcOoUaMgk8kwcOBAfPHFF7hx4wby8vKQn5+PmzdvYuvWrYiIiIBcLkd0dLSl0yUzOHLkCDp16mTpNIiIdNhzQ0ZxcXHB559/jilTpmDu3LnFxmgIggBfX18sWrRI1HsnERGRdLC4IaM1btwYX3/9tW7Dt7S0NAAFqzIHBQUhICDAwhkSEVF1wuKGTCYwMJDLhxMRkcVxzA0RERFJCntuyCj+/v7l3i9KJpPh9OnTZs6ITGXMmDHlanf37l0zZ0JEZBgWN2SUkSNHcjNMiSrvZpm2tra8HElEosK9pYiIiEhSOOaGjNKsWTOcP3/e0mmQGT169AiXLl1Cfn5+sftyc3Nx6dIlPH782AKZERGVjMUNGYUdf9L3+PFjREZG4tChQ8Xu+/rrr/Hqq6/yfUBEosLihojK5OLigtDQUOzatavYfbt370aPHj1ga2trgcyIiErGAcVktOzsbGRkZJSrrZOTk1lzIfMYOHAgJkyYgPT0dNSpUwcAcO3aNVy4cAHTpk2zcHZERPpY3JDRoqKiyt32r7/+MmMmZC6dOnWCs7Mz9u7dizfffBNAQa+Nu7s7Z0oRkeiwuCGjjRkzBo0aNbJ0GmRGCoUCffr0we7du/Hmm29Cq9Vi7969eP311y2dGhFRMSxuyGhhYWHw9fW1dBpkZhEREdiwYQPOnz8PtVqN+/fvY8CAAZZOi4ioGBY3RFQuXl5eaN26NXbv3o2srCwEBwfD1dXV0mkRERXD2VJUaW7evGnpFMhIgwYNwv79+3H48GEMGjTI0ukQEZWIxQ0ZZf78+WjYsGGZbS5duoTJkyejZ8+elZQVmUt4eDiePHkCW1tbdO7c2dLpEBGViJelyCgDBgxAQkICdu3ahdu3b6Nhw4YYNWoU2rRpg8uXL2PRokX46aefYGdnp5tlQ1WXvb094uLioFQqYWXFjw8iEifuLUVG+eabbzB16lQ4OjqicePGSE1NRWZmJqZPn4558+bBysoKw4cPx4gRI+Do6GjpdImIqBpgcUNGGThwIJydnREXFwdbW1sIgoD58+dj8+bNaNKkCdauXYu6detaOk0iIqpGOOaGjHLjxg0MGzZMt/y+TCZDdHQ0BEHAO++8w8KGiIgqHYsbMkp2djacnZ31jrm4uAAACxsiIrIIjggkoz29t5RGowEAPHz4sNieU9xbioiIzI1jbsgoPj4+kMlkxY4LglDice4tRURE5saeGzLK/PnzLZ0CERGRHvbcEBERkaRwQDGZ3ZUrV7BkyRKuaEtERJWCl6XILFJTU/Htt99i3759uHz5MhQKBTp16mTptIiIqBpgcUMmo1arceDAAezbtw+nT5+GVquFTCbDm2++iZEjRxabMk5ERGQOLG7IKLm5uTh8+DD27duHn376CXl5efD09MS4ceMQGhqKyMhIhIaGsrAhIqJKw+KGjNKhQwc8evQIbm5ueP3119GnTx80b94cAJCVlWXh7IiIqDrigGIyyuPHjyEIAuzt7eHk5MTNMYmIyOJY3JBRfvrpJ8ycORP29vZYsmQJunbtisGDB+OLL77A/fv3LZ0eERFVQ1znhkzm1q1b2LdvHxISEnDlyhUoFApotVq8/fbbGDFihG5zTSIiInNicUNmcenSJezduxffffcdbt++jZo1a6Jbt25YsGCBpVMjIiKJY3FDZnfy5Ens27cPBw8exK+//mrpdIiISOJY3JBJPHz4EDdv3kSTJk1gZaU/CS83NxdJSUlo0KAB7O3tLZQhERFVFxxQTCbx5MkTREZG4tChQ8Xu+/rrr/Hqq69CoVBYIDMiIqpuWNyQSbi4uCA0NBS7du0qdt/u3bvRo0cPDigmIqJKweKGTGbgwIE4ceIE0tPTdceuXbuGCxcuYODAgRbMjIiIqhMWN2QynTp1grOzM/bu3as7tnv3bri7uyMwMNCCmRERUXXC4oZMRqFQoE+fPti9ezcAQKvVYu/evey1ISKiSsXihkwqIiIC165dw/nz5/Hzzz/j/v37GDBggKXTIiKiaoQbZ5JJeXl5oXXr1ti9ezeysrIQHBwMV1dXS6dFRETVCHtuyOQGDRqE/fv34/Dhwxg0aJCl0yEiomqGi/iRyWVnZyM4OBh2dnZITEwstqgfERGRObG4IbP46aefoFQq0a5dO0unQkRE1QyLGyIiIpIUjrkhIiIiSWFxQ0RERJLC4oaIiIgkhcUNEdFTbt26BW9v7xI3giUi8WNxQ0RERJLC4oaIiIgkhcUNEVVLOTk5lk6BiMyExQ0RWdylS5fg7e2Nw4cP645duHAB3t7exTZeffPNNxEZGam7/cUXX6BXr15o2bIlQkJCMGfOHKjVar3HDB06FL1798aFCxfw2muvoXXr1li8eDEAQK1WY9q0aQgICEBgYCCmTp2KrKwsMz5bIjI3FjdEZHFNmzaFSqXCqVOndMdOnToFuVyOS5cuITs7GwCg1Wpx9uxZBAYGAgCWL1+ODz/8EHXq1MG0adPQvXt3bNu2DSNHjkReXp7eOTIyMhAdHY1mzZph+vTpaNeuHQRBwNixY/HNN9+gb9++iImJQWpqKqZOnVp5T56ITI6b/hCRxcnlcrRp00avuDl9+jS6du2Kw4cP48yZMwgNDdUVOoGBgbh//z5Wr16NkJAQrF27FnJ5wXc1T09PfPjhh9i7d6/exq137tzBnDlzMHjwYN2x77//HidPnsS7776LN998EwAwZMgQDBs2rJKeORGZA3tuiEgUAgICcPHiRTx69AhAQXETGhoKHx8fnD59GkBBb45MJkNAQAB+/vln5OXlYdiwYbrCBgAiIyNhb2+Po0eP6sVXKpUYOHCg3rHCjV2HDBmiO6ZQKPD666+b62kSUSVgzw0RiUJgYCDy8/Nx7tw51K1bF/fu3UNgYCCuXr2q69E5deoUmjRpAicnJ/z7778ACnpqilIqlWjYsCFSUlL0jru5uUGpVOodS0lJgaurK+zs7PSOe3h4mPrpEVElYs8NEYlCy5YtYWNjg5MnT+L06dOoVasWPDw8EBgYiPPnzyM3NxenT59GQEBAheLXqFHDxBkTkVixuCEiUVAqlfD19cWpU6dw6tQp3aDhgIAA5ObmYu/evbh79y7atm0LAHjuuecAAElJSXpxcnNzcevWLdSvX/+Z56xfvz7u3LmDhw8f6h1PTk42xVMiIgthcUNEohEQEIDz58/j119/1fXQuLi4wMvLC2vXrgUAXdHzwgsvwNraGps3b4YgCLoYO3bsQFZWFl588cVnni80NBT5+fn48ssvdcc0Gg22bNliyqdFRJWMY26ISDQCAwOxatUq3L59W1fEFB7ftm0b6tevj7p16wIoKHpGjx6NuLg4vPnmm+jcuTOSk5OxdetWtGrVCn379n3m+Tp37ow2bdrgk08+QUpKCpo0aYJDhw5xnRuiKo49N0QkGv7+/lAoFLCzs4OPj4/ueGGhU7TgAYC3334bs2bNwr///ov58+fju+++w8svv4z169fD2tr6meeTy+VYuXIl+vTpg71792LJkiVwc3PDxx9/bNonRkSVSiYU7c8lIiIiquLYc0NERESSwuKGiIiIJIXFDREREUkKixsiIiKSFBY3REREJCksboiIiEhSWNwQERGRpLC4ISIiIklhcUNERESSwuKGiIiIJIXFDREREUkKixsiIiKSFBY3REREJCn/D65KJN3heo+PAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Display and plot at most 40 words from the X_data set\n",
        "plot_frequency(X_data, Y_data, max_word_num=40)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gMXwUShKesfL"
      },
      "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",
        "  }\n",
        "  word_count_df = pd.DataFrame(data = result , columns=['interval', 'pos intervals', 'neg intervals'])\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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "id": "CJX2oTZTesfM",
        "outputId": "487392e8-f40f-43b9-f24e-11728ede5ae2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          pos intervals  neg intervals\n",
              "interval                              \n",
              "0-10                166            491\n",
              "11-20              1781           2182\n",
              "21-30              1290           1562\n",
              "31-40                34            107\n",
              "41-50                 0              0\n",
              "51-60                 0              0\n",
              "61-70                 0              0\n",
              "71+                   0              0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d03ae4e4-4dad-42d0-a841-c9f16e065c06\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>interval</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0-10</th>\n",
              "      <td>166</td>\n",
              "      <td>491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11-20</th>\n",
              "      <td>1781</td>\n",
              "      <td>2182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21-30</th>\n",
              "      <td>1290</td>\n",
              "      <td>1562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31-40</th>\n",
              "      <td>34</td>\n",
              "      <td>107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41-50</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51-60</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61-70</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71+</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d03ae4e4-4dad-42d0-a841-c9f16e065c06')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d03ae4e4-4dad-42d0-a841-c9f16e065c06 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d03ae4e4-4dad-42d0-a841-c9f16e065c06');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "defaultdict(<class 'int'>, {'0-10': 491, '11-20': 2182, '21-30': 1562, '31-40': 107, '41-50': 0, '51-60': 0, '61-70': 0, '71+': 0})\n",
            "defaultdict(<class 'int'>, {'11-20': 1781, '0-10': 166, '21-30': 1290, '31-40': 34, '41-50': 0, '51-60': 0, '61-70': 0, '71+': 0})\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plots the count of positive and negative classes of X_data based on the given intervals\n",
        "intervals = ['0-10', '11-20' , '21-30' , '31-40' , '41-50' , '51-60' , '61-70', '71+']\n",
        "length_plot(X_data, Y_data, intervals)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.9"
    },
    "vscode": {
      "interpreter": {
        "hash": "c4c6ce542ed86c85d22b49296a52397aea49807c5462a5cc5776ff93f0a8a801"
      }
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