Giovanni_&_Oscar_Milestone_3.ipynb

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        "# 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"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "WPFnCCd7O6fN"
      },
      "source": [
        "### 0. Setup Dependencies and Milestone 2 Code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QiwopORsO-yC",
        "outputId": "fa56efa0-7ab1-4f76-9319-137ec3d158f8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pn1OWO_1PRrO",
        "outputId": "f480ba71-6926-41c5-bd42-0fbbf008eb6d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        }
      ],
      "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": 3,
      "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)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "UJCd5JzUNNuk"
      },
      "source": [
        "#### Load the Data in a Colab Notebook"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
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      "outputs": [
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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>id</th>\n",
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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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              "      <th>2</th>\n",
              "      <td>5</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())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "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": [
        "#### Split the training data into 90% training and 10% for validation (your experiments)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "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": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Text\n",
            "[7366 'obliterate' 'United Kingdom'\n",
            " 'WWE 2K15: Universe Mode - Part 149 - OBLITERATE!!: http://t.co/0oms8rI3l1 via @YouTube']\n",
            "\n",
            "Is Natural Disaster?\n",
            "0 No\n",
            "======================\n",
            "\n",
            "Text\n",
            "[9788 'trapped' 'å_å_Los Mina City\\x89ã¢'\n",
            " \"Hollywood Movie About Trapped Miners Released in Chile: 'The 33' Hollywood movie about trapped miners starring... http://t.co/x8moYeVjsJ\"]\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": [
        "#### Testing and Submitting a Baseline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "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": 10,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input:\n",
            "[7366 'obliterate' 'United Kingdom'\n",
            " 'WWE 2K15: Universe Mode - Part 149 - OBLITERATE!!: http://t.co/0oms8rI3l1 via @YouTube']\n",
            "\n",
            "Output\n",
            "0\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[9788 'trapped' 'å_å_Los Mina City\\x89ã¢'\n",
            " \"Hollywood Movie About Trapped Miners Released in Chile: 'The 33' Hollywood movie about trapped miners starring... http://t.co/x8moYeVjsJ\"]\n",
            "\n",
            "Output\n",
            "0\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[3956 'devastation' nan\n",
            " '70 Years After Atomic Bombs Japan Still Struggles With War Past: The anniversary of the devastation wrought b... http://t.co/iTBJ6DKRZI']\n",
            "\n",
            "Output\n",
            "1\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[6278 'hijacking' 'Zimbabwe'\n",
            " '\\x89Û÷Good Samaritans\\x89Ûª shot in horror hijacking http://t.co/V5yUUALoqw #263Chat #Twimbos ZimpapersViews']\n",
            "\n",
            "Output\n",
            "1\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[10194 'violent%20storm' 'United Kingdom'\n",
            " 'POV video captures violent landing at Amsterdam Airport Schiphol during a storm ... http://t.co/fkv5qXDcy3']\n",
            "\n",
            "Output\n",
            "1\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": 11,
      "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": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Loss: 7.872682859625455\n",
            "Testing Loss: 7.941198556940306\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": 13,
      "metadata": {},
      "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": 14,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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": 15,
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      "outputs": [
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              "    }\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('submission.csv').head()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 1. Visualize and Learn about the Data we are working with"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "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": 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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>word</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>&lt;LINK&gt;</th>\n",
              "      <td>2510</td>\n",
              "      <td>2196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>-</th>\n",
              "      <td>419</td>\n",
              "      <td>396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>be</th>\n",
              "      <td>113</td>\n",
              "      <td>287</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>as</th>\n",
              "      <td>171</td>\n",
              "      <td>160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>by</th>\n",
              "      <td>279</td>\n",
              "      <td>242</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>after</th>\n",
              "      <td>181</td>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>my</th>\n",
              "      <td>134</td>\n",
              "      <td>566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>this</th>\n",
              "      <td>179</td>\n",
              "      <td>287</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>to</th>\n",
              "      <td>761</td>\n",
              "      <td>1195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>in</th>\n",
              "      <td>1186</td>\n",
              "      <td>854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>for</th>\n",
              "      <td>399</td>\n",
              "      <td>489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>new</th>\n",
              "      <td>132</td>\n",
              "      <td>292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>from</th>\n",
              "      <td>247</td>\n",
              "      <td>183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>of</th>\n",
              "      <td>957</td>\n",
              "      <td>935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>it</th>\n",
              "      <td>164</td>\n",
              "      <td>342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i</th>\n",
              "      <td>292</td>\n",
              "      <td>1061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>and</th>\n",
              "      <td>512</td>\n",
              "      <td>927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>at</th>\n",
              "      <td>305</td>\n",
              "      <td>237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>with</th>\n",
              "      <td>188</td>\n",
              "      <td>382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>on</th>\n",
              "      <td>418</td>\n",
              "      <td>444</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           pos count  neg count\n",
              "word                           \n",
              "<LINK>          2510       2196\n",
              "-                419        396\n",
              "be               113        287\n",
              "that             181        357\n",
              "you              125        629\n",
              "as               171        160\n",
              "by               279        242\n",
              "is               348        590\n",
              "nan             1117       1479\n",
              "after            181         75\n",
              "my               134        566\n",
              "this             179        287\n",
              "<ARTICLE>       2426       3372\n",
              "to               761       1195\n",
              "in              1186        854\n",
              "for              399        489\n",
              "new              132        292\n",
              "from             247        183\n",
              "of               957        935\n",
              "it               164        342\n",
              "i                292       1061\n",
              "and              512        927\n",
              "at               305        237\n",
              "with             188        382\n",
              "on               418        444"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DEBUG: Total Words Len: 33580\n"
          ]
        },
        {
          "data": {
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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)"
      ]
    },
    {
      "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",
        "    }\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": 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",
              "    </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>"
            ],
            "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"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        },
        {
          "data": {
            "image/png": 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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 = ['0-10', '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"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}