01-NLP-basics/04_fake_news_classifier.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "TS3VUyUC6aij"
      },
      "source": [
        "---\n",
        "<h1 align='center'><strong>4️⃣\"Fake News\" Classifier</strong></h1>\n",
        "\n",
        "---\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PoSpaoNj6WeF",
        "outputId": "7cf5774e-865b-46c5-ee49-4bb5b78e9f60"
      },
      "source": [
        "!git clone https://github.com/mohd-faizy/Natural_Language_Processing_in_Python.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Natural_Language_Processing_in_Python'...\n",
            "remote: Enumerating objects: 170, done.\u001b[K\n",
            "remote: Counting objects: 100% (54/54), done.\u001b[K\n",
            "remote: Compressing objects: 100% (38/38), done.\u001b[K\n",
            "remote: Total 170 (delta 29), reused 39 (delta 16), pack-reused 116\u001b[K\n",
            "Receiving objects: 100% (170/170), 33.13 MiB | 37.08 MiB/s, done.\n",
            "Resolving deltas: 100% (62/62), done.\n"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "Aw05dwIb9A9U"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import os\n",
        "\n",
        "# plt.style.use('fivethirtyeight')\n",
        "# plt.style.use('ggplot')\n",
        "# sns.set_theme()\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "LZeI_v3_aDvw"
      },
      "outputs": [],
      "source": [
        "from pprint import pprint"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "\n",
        "# Download a specific NLTK dataset, e.g., the 'punkt' tokenizer models.\n",
        "nltk.download('punkt', quiet=True)\n",
        "\n",
        "# Download the NLTK stopwords dataset, which contains common stopwords for various languages.\n",
        "nltk.download('stopwords', quiet=True)\n",
        "\n",
        "# Download the WordNet lexical database, which is used for various NLP tasks like synonym and antonym lookup.\n",
        "nltk.download('wordnet', quiet=True)\n",
        "\n",
        "# Download the NLTK averaged perceptron tagger, which is used for part-of-speech tagging.\n",
        "nltk.download('averaged_perceptron_tagger', quiet=True)\n",
        "\n",
        "# Download the NLTK names dataset, which contains a list of common first names and last names.\n",
        "# nltk.download('names', quit=True)\n",
        "\n",
        "# Download the NLTK movie_reviews dataset, which contains movie reviews categorized as positive and negative.\n",
        "# nltk.download('movie_reviews', quit=True)\n",
        "\n",
        "# Download the NLTK reuters dataset, which is a collection of news documents categorized into topics.\n",
        "# nltk.download('reuters', quit=True)\n",
        "\n",
        "# Download the NLTK brown corpus, which is a collection of text from various genres of written American English.\n",
        "# nltk.download('brown', quit=True)\n",
        "\n",
        "# Download the 'maxent_ne_chunker' dataset, which is used for Named Entity Recognition.\n",
        "nltk.download('maxent_ne_chunker', quiet=True)\n",
        "\n",
        "# Download the 'words' dataset, which contains a list of common English words.\n",
        "nltk.download('words', quiet=True)"
      ],
      "metadata": {
        "id": "Wgf8tcBR4ic9",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "877a8800-de97-4c32-cb43-27ee0912f382"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
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          "metadata": {},
          "execution_count": 4
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    {
      "cell_type": "code",
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        "id": "q_QJ6k0e9bI2",
        "outputId": "ffeb0ad6-a1ac-43c4-8bb6-5adab9dce428"
      },
      "source": [
        "os.chdir('/content/Natural_Language_Processing_in_Python/01-NLP-basics/_datasets')\n",
        "cwd = os.getcwd()\n",
        "print('Curent working directory is ', cwd)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Curent working directory is  /content/Natural_Language_Processing_in_Python/01-NLP-basics/_datasets\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9H7VtZ_99qrY",
        "outputId": "7a4edbb8-a54f-40e2-a0ca-c5e34b0424d8"
      },
      "source": [
        "ls"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " articles.csv            grail.txt            \u001b[0m\u001b[01;34mwikipedia_articles\u001b[0m/\n",
            " english_stopwords.txt   \u001b[01;34mnews_articles\u001b[0m/      'Wikipedia articles.zip'\n",
            " fake_or_real_news.csv  'News articles.zip'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qQEPrKg8GDIF"
      },
      "source": [
        "## **Supervised Learning and NLP for Fake News Classification**\n",
        "\n",
        "### Introduction\n",
        "\n",
        "- Utilizing supervised learning with NLP for fake news classification.\n",
        "- Focus on linguistic features over geometric ones, using `Bag-of-Words` or `TF-IDF` models.\n",
        "\n",
        "### **Supervised Learning Steps**\n",
        "\n",
        "1. **Data Preparation**:\n",
        "   - Gather and preprocess news article data.\n",
        "   - Ensure data quality and consistency.\n",
        "\n",
        "2. **Label Assignment**:\n",
        "   - Categorize articles as \"`fake`\" or \"`authentic`\" based on analysis and expertise.\n",
        "\n",
        "3. **Data Splitting**:\n",
        "   - Divide the dataset into `training` and `test` sets.\n",
        "\n",
        "4. **Feature Extraction**:\n",
        "   - Convert text into numerical representations (`BoW` or `TF-IDF`) to aid in prediction.\n",
        "\n",
        "5. **Model Evaluation**:\n",
        "   - Assess model performance using metrics like `accuracy`, `precision`, `recall`, and `F1-score`.\n",
        "\n",
        "By following these steps, we can effectively classify fake news, contributing to misinformation mitigation.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "llPis5UXGDIF"
      },
      "source": [
        "## **Building word count vectors with scikit-learn**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "0uzBbuBPGDIG",
        "outputId": "833ab4bd-16e1-4868-ba77-cbf554525d7e"
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            "(6335, 4)\n"
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              "   Unnamed: 0                                              title  \\\n",
              "0        8476                       You Can Smell Hillary’s Fear   \n",
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              "2        3608        Kerry to go to Paris in gesture of sympathy   \n",
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              "4         875   The Battle of New York: Why This Primary Matters   \n",
              "\n",
              "                                                text label  \n",
              "0  Daniel Greenfield, a Shillman Journalism Fello...  FAKE  \n",
              "1  Google Pinterest Digg Linkedin Reddit Stumbleu...  FAKE  \n",
              "2  U.S. Secretary of State John F. Kerry said Mon...  REAL  \n",
              "3  — Kaydee King (@KaydeeKing) November 9, 2016 T...  FAKE  \n",
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          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "url = \"/content/Natural_Language_Processing_in_Python/01-NLP-basics/_datasets/fake_or_real_news.csv\"\n",
        "df = pd.read_csv(url)\n",
        "print(df.shape)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "jVxtgkc9GDIH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b30b24c7-bfbf-4097-db18-2444543a5163"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['00' '000' '0000' '00000031' '000035' '00006' '0001' '0001pt' '000ft'\n",
            " '000km']\n"
          ]
        }
      ],
      "source": [
        "# Import necessary libraries\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Prepare the data for text classification\n",
        "X = df['text']\n",
        "y = df.label\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "# - test_size is set to 0.33, meaning 33% of the data will be used for testing\n",
        "# - random_state is set to 53 for reproducibility\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "                                                    test_size=0.33,\n",
        "                                                    random_state=53)\n",
        "\n",
        "# Create an instance of the CountVectorizer with specified settings\n",
        "# - 'stop_words' is set to 'english' to remove common English stopwords (e.g., \"the\", \"and\")\n",
        "count_vectorizer = CountVectorizer(stop_words='english')\n",
        "\n",
        "# Use the CountVectorizer to transform the training data (X_train)\n",
        "# This converts the text data into a 'bag-of-words' representation\n",
        "count_train = count_vectorizer.fit_transform(X_train)\n",
        "\n",
        "# Use the same CountVectorizer to transform the test data (X_test)\n",
        "# Note that we use 'transform' on the test data to ensure consistent vocabulary\n",
        "count_test = count_vectorizer.transform(X_test)\n",
        "\n",
        "# Print the first 10 feature names (words) in the bag-of-words representation\n",
        "print(count_vectorizer.get_feature_names_out()[:10])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ">The reason we use `fit_transform` on `X_train` and transform on `X_test` is to avoid data leakage. Data leakage occurs when information from the test set is used to train the model, which can lead to overfitting and unrealistic performance estimates. To prevent this, we need to ensure that the model only learns from the training set and does not see the test set until the evaluation stage.\n",
        "\n",
        "- **The `fit_transform` method does two things:** it fits the `CountVectorizer` to the training data, which means it **learns the vocabulary** and **the frequency of each word** in the training data, and it transforms the training data into a sparse matrix of word counts.\n",
        "\n",
        "- **The `transform` method only does the second thing:** it transforms the test data into a sparse matrix of word counts using the vocabulary and frequency learned from the training data. This way, the test data is not used to influence the model training, and we can get a fair evaluation of the model’s performance on unseen data."
      ],
      "metadata": {
        "id": "77rpuo0KSnDP"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7K7JocR9GDII"
      },
      "source": [
        "### **TfidfVectorizer for text classification**\n",
        "\n",
        "Similar to the sparse `CountVectorizer` created in the previous exercise, you'll work on creating tf-idf vectors for your documents. You'll set up a `TfidfVectorizer` and investigate some of its features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "x_3jkDv0GDII",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "0b952513-3c36-4ba0-b0cc-7363abf606e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['00' '000' '0000' '00000031' '000035' '00006' '0001' '0001pt' '000ft'\n",
            " '000km']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.]])"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Import the TfidfVectorizer class from the scikit-learn library\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "# Create an instance of the TfidfVectorizer with specified settings\n",
        "# - 'stop_words' is set to 'english' to remove common English stopwords (e.g., \"the\", \"and\")\n",
        "# - 'max_df' is set to 0.7 to ignore terms that have a document frequency higher than 70%\n",
        "tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_df=0.7)\n",
        "\n",
        "# Use the TfidfVectorizer to transform the training data (X_train)\n",
        "# This converts the text data into a TF-IDF matrix\n",
        "tfidf_train = tfidf_vectorizer.fit_transform(X_train)\n",
        "\n",
        "# Use the same TfidfVectorizer to transform the test data (X_test)\n",
        "# Note that we use 'transform' on the test data to ensure consistent vocabulary and scaling\n",
        "tfidf_test = tfidf_vectorizer.transform(X_test)\n",
        "\n",
        "# Print the first 10 feature names (terms) in the TF-IDF matrix\n",
        "print(tfidf_vectorizer.get_feature_names_out()[:10])\n",
        "\n",
        "# Print the TF-IDF values of the first 5 training examples as a dense array\n",
        "display(tfidf_train.A[:5])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### `CountVectorizer` vs `TfidfVectorizer`\n",
        "\n",
        "- `CountVectorizer` simply counts the occurrences of words.\n",
        "- `TfidfVectorizer` considers both the frequency of a word in a document and its rarity across the entire corpus, giving more weight to words that are important within a document but less common in the corpus. This makes it a better choice when you want to capture the relative importance of words in text data.\n",
        "\n",
        "- **`count_train.A`** represents the output of the `CountVectorizer` for the training data. The `.A` attribute is used to convert the **`Sparse Matrix`** generated by the **CountVectorizer** into a **`Dense Matrix`**. **This dense matrix contains the counts of each term (word) in each document in your training dataset.**\n",
        "\n",
        "- **`tfidf_train.A`**  is used to access the **Dense Matrix** representation of the `TF-IDF` (Term Frequency-Inverse Document Frequency) matrices for the training and test data."
      ],
      "metadata": {
        "id": "zvvEVR-UoE6p"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RH2adjp_GDII"
      },
      "source": [
        "### **Inspecting the vectors**\n",
        "\n",
        "To get a better idea of how the `vectors` work, we will investigate them by converting them into **pandas DataFrames**."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Create DataFrames `count_df` and `tfidf_df` using `pd.DataFrame()`.\n",
        "- Specify values as the first argument and columns (features) as the second argument.\n",
        "- Access values using the `.A` attribute of `count_train` and `tfidf_train`.\n",
        "- Access columns using the `.get_feature_names_out()` methods of `count_vectorizer` and `tfidf_vectorizer`.\n",
        "- Print the head of each DataFrame to inspect their structure.\n",
        "- Test if column names are the same by creating an object called `difference`.\n",
        "- Find the difference between columns in `count_df` and `tfidf_df` using `.columns`.\n",
        "- Check if the two DataFrames are equivalent using the `.equals()` method with `count_df` and `tfidf_df` as arguments.\n"
      ],
      "metadata": {
        "id": "boGHD-lQzP3v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(count_train.A)\n",
        "print(tfidf_train.A)"
      ],
      "metadata": {
        "id": "7C4rHRnckI9V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a23e6a31-f428-4b66-be60-56b1550f0ab4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]]\n",
            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the CountVectorizer DataFrame: count_df\n",
        "count_df = pd.DataFrame(count_train.A, columns=count_vectorizer.get_feature_names_out())\n",
        "\n",
        "# Print the head of count_df\n",
        "print(count_df.head())\n",
        "\n",
        "# Create the TfidfVectorizer DataFrame: tfidf_df\n",
        "tfidf_df = pd.DataFrame(tfidf_train.A, columns=tfidf_vectorizer.get_feature_names_out())\n",
        "\n",
        "# Print the head of tfidf_df\n",
        "print(tfidf_df.head())\n",
        "\n",
        "\n",
        "# Calculate the difference in columns: difference\n",
        "difference = set(count_df.columns) - set(tfidf_df.columns)\n",
        "print(difference)\n",
        "\n",
        "# Check whether the DataFrame are equal\n",
        "print(count_df.equals(tfidf_df))"
      ],
      "metadata": {
        "id": "ccxAejISv6lM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f10bf4b-8d19-4115-918e-0f88940927ed"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   00  000  0000  00000031  000035  00006  0001  0001pt  000ft  000km  ...  \\\n",
            "0   0    0     0         0       0      0     0       0      0      0  ...   \n",
            "1   0    0     0         0       0      0     0       0      0      0  ...   \n",
            "2   0    0     0         0       0      0     0       0      0      0  ...   \n",
            "3   0    0     0         0       0      0     0       0      0      0  ...   \n",
            "4   0    0     0         0       0      0     0       0      0      0  ...   \n",
            "\n",
            "   حلب  عربي  عن  لم  ما  محاولات  من  هذا  والمرضى  ยงade  \n",
            "0    0     0   0   0   0        0   0    0        0      0  \n",
            "1    0     0   0   0   0        0   0    0        0      0  \n",
            "2    0     0   0   0   0        0   0    0        0      0  \n",
            "3    0     0   0   0   0        0   0    0        0      0  \n",
            "4    0     0   0   0   0        0   0    0        0      0  \n",
            "\n",
            "[5 rows x 56922 columns]\n",
            "    00  000  0000  00000031  000035  00006  0001  0001pt  000ft  000km  ...  \\\n",
            "0  0.0  0.0   0.0       0.0     0.0    0.0   0.0     0.0    0.0    0.0  ...   \n",
            "1  0.0  0.0   0.0       0.0     0.0    0.0   0.0     0.0    0.0    0.0  ...   \n",
            "2  0.0  0.0   0.0       0.0     0.0    0.0   0.0     0.0    0.0    0.0  ...   \n",
            "3  0.0  0.0   0.0       0.0     0.0    0.0   0.0     0.0    0.0    0.0  ...   \n",
            "4  0.0  0.0   0.0       0.0     0.0    0.0   0.0     0.0    0.0    0.0  ...   \n",
            "\n",
            "   حلب  عربي   عن   لم   ما  محاولات   من  هذا  والمرضى  ยงade  \n",
            "0  0.0   0.0  0.0  0.0  0.0      0.0  0.0  0.0      0.0    0.0  \n",
            "1  0.0   0.0  0.0  0.0  0.0      0.0  0.0  0.0      0.0    0.0  \n",
            "2  0.0   0.0  0.0  0.0  0.0      0.0  0.0  0.0      0.0    0.0  \n",
            "3  0.0   0.0  0.0  0.0  0.0      0.0  0.0  0.0      0.0    0.0  \n",
            "4  0.0   0.0  0.0  0.0  0.0      0.0  0.0  0.0      0.0    0.0  \n",
            "\n",
            "[5 rows x 56922 columns]\n",
            "set()\n",
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C-lTi5L9GDIJ"
      },
      "source": [
        "## **Training and testing a classification model with scikit-learn**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zEWmoM8HGDIJ"
      },
      "source": [
        "### **Training and testing the \"fake news\" model with CountVectorizer**\n",
        "\n",
        "\n",
        "> **Which of the below is the most reasonable model to use when training a new supervised model using text vector data?** $\\Rightarrow$ `Naive Bayes`\n",
        "\n",
        "- `Feature Independence`: Naive Bayes assumes all features are independent, which simplifies calculations but may not hold in reality.\n",
        "- `Handling High Dimensionality`: It efficiently handles high-dimensional text data by treating features independently.\n",
        "- `Probabilistic Model`: Provides intuitive, probabilistic predictions based on - conditional probabilities.\n",
        "- `Multinomial and Bernoulli Variants`: Multinomial for discrete features, - Bernoulli for binary features.\n",
        "- `Scalability`: Highly scalable and works well with large datasets.\n",
        "- `Performance`: Surprisingly accurate, can outperform sophisticated methods.\n",
        "- `Model Selection`: Choose wisely based on data and task requirements.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training and testing a **Naive Bayes** model using the **CountVectorizer** data."
      ],
      "metadata": {
        "id": "nbzVmZCGz7R-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, roc_auc_score\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Instantiate a Multinomial Naive Bayes classifier: nb_classifier\n",
        "nb_classifier = MultinomialNB()\n",
        "\n",
        "# Fit the classifier to the training data\n",
        "nb_classifier.fit(count_train, y_train)\n",
        "\n",
        "# Create the predicted tags: pred\n",
        "pred = nb_classifier.predict(count_test)\n",
        "\n",
        "# Calculate the accuracy score: score\n",
        "accuracy = accuracy_score(y_test, pred)\n",
        "print(f\"Accuracy: {accuracy:.2f}\")\n",
        "\n",
        "# Calculate the confusion matrix: cm\n",
        "cm = confusion_matrix(y_test, pred, labels=['FAKE', 'REAL'])\n",
        "print(\"Confusion Matrix and ROC with CountVectorizer\")\n",
        "\n",
        "# Calculate the ROC curve\n",
        "y_pred_prob = nb_classifier.predict_proba(count_test)[:, 1]\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label='REAL')\n",
        "auc = roc_auc_score(y_test, y_pred_prob)\n",
        "\n",
        "# Plot the ROC curve and the confusion matrix\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {auc:.2f})')\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve')\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "# Plot confusion matrix as a heatmap\n",
        "plt.subplot(1, 2, 2)\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['FAKE', 'REAL'], yticklabels=['FAKE', 'REAL'])\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "QZHGGBUvBfIL",
        "outputId": "5a71122d-3cf3-4cf5-e79b-3c2000b9259e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.89\n",
            "Confusion Matrix and ROC with CountVectorizer\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lcbebDqUGDIK"
      },
      "source": [
        "### Training and testing the \"fake news\" model with TfidfVectorizer\n",
        "Now that you have evaluated the model using the `CountVectorizer`, you'll do the same using the `TfidfVectorizer` with a Naive Bayes model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "uFEY8qzoGDIK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "c3637961-8e8a-4b05-fe45-97a100089826"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.86\n",
            "Confusion Matrix and ROC with TfidfVectorizer\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, roc_auc_score\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Create a Multinomial Naive Bayes classifier: nb_classifier\n",
        "nb_classifier = MultinomialNB()\n",
        "\n",
        "# Fit the classifier to the training data\n",
        "nb_classifier.fit(tfidf_train, y_train)\n",
        "\n",
        "# Create the predicted tags: pred\n",
        "pred = nb_classifier.predict(tfidf_test)\n",
        "\n",
        "# Calculate the accuracy score: score\n",
        "accuracy = accuracy_score(y_test, pred)\n",
        "print(f\"Accuracy: {accuracy:.2f}\")\n",
        "\n",
        "# Calculate the confusion matrix: cm\n",
        "cm = confusion_matrix(y_test, pred, labels=['FAKE', 'REAL'])\n",
        "print(\"Confusion Matrix and ROC with TfidfVectorizer\")\n",
        "\n",
        "# Calculate the ROC curve\n",
        "y_pred_prob = nb_classifier.predict_proba(tfidf_test)[:, 1]\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label='REAL')\n",
        "auc = roc_auc_score(y_test, y_pred_prob)\n",
        "\n",
        "# Plot the ROC curve and the confusion matrix\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {auc:.2f})')\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve')\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "# Plot confusion matrix as a heatmap\n",
        "plt.subplot(1, 2, 2)\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['FAKE', 'REAL'], yticklabels=['FAKE', 'REAL'])\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kKcx1NgYGDIK"
      },
      "source": [
        "## **Simple NLP, complex problems**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N8YATSDSGDIK"
      },
      "source": [
        "### Improving your model\n",
        "\n",
        "\n",
        "What are possible next steps you could take to improve the model?\n",
        "\n",
        "- *Tweaking alpha levels.*\n",
        "- *Trying a new classification model.*\n",
        "- *Training on a larger dataset.*\n",
        "- *Improving text preprocessing.*\n",
        "\n",
        "For the larger models:\n",
        "\n",
        "**Tweaking Alpha Levels:**\n",
        "\n",
        "- Alpha levels control the trade-off between model-generated responses and user-provided instructions. Fine-tuning these levels can help make the model's output more aligned with the desired context or style.\n",
        "- Increasing alpha makes the output more focused on the given instruction, while decreasing it makes the output more creative and free-form.\n",
        "- Experimenting with different alpha values can help you strike the right balance for your specific use case.\n",
        "\n",
        "**Trying a New Classification Model:**\n",
        "\n",
        "- GPT-3.5 is a language model, and while it can perform some classification tasks, its primary strength lies in generating text. If your task is primarily classification-oriented, you might consider using a specialized classification model.\n",
        "- Models like BERT, RoBERTa, or more recent transformer-based architectures designed for classification tasks might be more suitable depending on your specific requirements.\n",
        "\n",
        "**Training on a Larger Dataset:**\n",
        "\n",
        "- Training on a larger and more diverse dataset can potentially improve the model's performance, as it can learn from a broader range of language patterns and contexts.\n",
        "- However, increasing the dataset size also comes with computational and infrastructure challenges, including higher costs and longer training times.\n",
        "\n",
        "**Improving Text Preprocessing:**\n",
        "\n",
        "- Proper text preprocessing is crucial for improving the model's understanding and performance. This can include tasks such as tokenization, text cleaning, and data augmentation.\n",
        "- Fine-tuning the preprocessing steps to handle domain-specific language or text types can lead to better model performance.\n",
        "\n",
        "**Additional Steps to Consider:**\n",
        "\n",
        "- **Fine-Tuning:**\n",
        "  - Fine-tuning the model on domain-specific or task-specific data can significantly improve its performance for specific applications.\n",
        "  - It allows you to adapt the model's behavior to better suit your needs.\n",
        "\n",
        "- **Diversity in Data:**\n",
        "  - Ensuring diversity in the training data, including different languages, cultures, and perspectives, can help reduce biases and improve the model's ability to understand and generate content for a global audience.\n",
        "\n",
        "- **Ethical and Safety Measures:**\n",
        "  - Implementing ethical guidelines and safety measures to prevent the model from generating harmful or inappropriate content is essential, especially in applications where user-generated text is involved.\n",
        "\n",
        "- **Feedback Loops:**\n",
        "  - Collecting user feedback and iteratively improving the model based on real-world usage can be invaluable for refining its capabilities.\n",
        "\n",
        "- **Research and Development:**\n",
        "  - Stay updated with the latest research in the field of natural language processing and AI. Incorporate state-of-the-art techniques and insights into your model development process.\n",
        "\n",
        "The choice of which steps to take depends on your specific objectives, available resources, and the nature of your application. Often, a combination of these approaches is necessary to achieve the best results. Regular testing, evaluation, and adaptation based on real-world usage are essential for model improvement.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "import warnings\n",
        "\n",
        "\n",
        "# Suppress FutureWarnings and UserWarnings related to MultinomialNB\n",
        "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
        "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
        "\n",
        "# Create the list of alphas: alphas\n",
        "alphas = np.arange(0, 1, 0.1)\n",
        "\n",
        "# Define train_and_predict()\n",
        "def train_and_predict(alpha):\n",
        "    # Instantiate the classifier: nb_classifier\n",
        "    nb_classifier = MultinomialNB(alpha=alpha)\n",
        "\n",
        "    # Fit to the training data\n",
        "    nb_classifier.fit(tfidf_train, y_train)\n",
        "\n",
        "    # Predict the labels: pred\n",
        "    pred = nb_classifier.predict(tfidf_test)\n",
        "\n",
        "    # Compute accuracy: score\n",
        "    score = accuracy_score(y_test, pred)\n",
        "    return score\n",
        "\n",
        "# Initialize an empty list to store the accuracy scores\n",
        "scores = []\n",
        "\n",
        "# Iterate over the alphas and compute the corresponding scores\n",
        "for alpha in alphas:\n",
        "    score = train_and_predict(alpha)\n",
        "    scores.append(score)\n",
        "\n",
        "# Plot the accuracy scores against the alpha values\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(alphas, scores, marker='o', linestyle='-')\n",
        "plt.title('Accuracy vs. Alpha for Naive Bayes')\n",
        "plt.xlabel('Alpha')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "mGNCuU75CVmC",
        "outputId": "dd988254-07a8-4c58-8f0a-ceb9e9eed28f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DMV30diEGDIL"
      },
      "source": [
        "### **Inspecting Model**\n",
        "\n",
        "Now that we have built a \"fake news\" classifier, we will investigate what it has learned. we can map the important vector weights back to actual words using some simple inspection techniques."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the class labels: class_labels\n",
        "class_labels = nb_classifier.classes_\n",
        "\n",
        "# Extract the features: feature_names\n",
        "feature_names = tfidf_vectorizer.get_feature_names_out()\n",
        "\n",
        "# Zip the feature names together with the coefficient array\n",
        "# and sort by weights: feat_with_weights\n",
        "feat_with_weights = sorted(zip(nb_classifier.feature_log_prob_[0], feature_names))\n",
        "\n",
        "# Print the first class label and the top 20 feat_with_weights entries\n",
        "print(class_labels[0], feat_with_weights[:20])\n",
        "\n",
        "# Print the second class label and the bottom 20 feat_with_weights entries\n",
        "print(class_labels[1], feat_with_weights[-20:])"
      ],
      "metadata": {
        "id": "aY3eq5Tw4FJZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f5d90e5-ec2b-45a9-d389-69ba1d5da17b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FAKE [(-11.280753302177917, '00000031'), (-11.280753302177917, '00006'), (-11.280753302177917, '000ft'), (-11.280753302177917, '001'), (-11.280753302177917, '002'), (-11.280753302177917, '003'), (-11.280753302177917, '006'), (-11.280753302177917, '008'), (-11.280753302177917, '010'), (-11.280753302177917, '013'), (-11.280753302177917, '025'), (-11.280753302177917, '027'), (-11.280753302177917, '035'), (-11.280753302177917, '037'), (-11.280753302177917, '040'), (-11.280753302177917, '044'), (-11.280753302177917, '048'), (-11.280753302177917, '066'), (-11.280753302177917, '068'), (-11.280753302177917, '075')]\n",
            "REAL [(-8.036772745824807, 'president'), (-8.022187159522364, 'american'), (-8.013319806154513, 'media'), (-8.007761560290644, 'donald'), (-8.006632122322646, 'october'), (-7.989623223030759, 'government'), (-7.929695447721539, 'like'), (-7.922750601304927, 'war'), (-7.915731838943572, 'new'), (-7.908889774759155, 'world'), (-7.885018054191407, 'just'), (-7.758145325115569, 'said'), (-7.7498037548099585, 'russia'), (-7.697669509488481, 'fbi'), (-7.604825769578616, '2016'), (-7.554879292243166, 'election'), (-7.541640806988918, 'people'), (-7.235945549755579, 'hillary'), (-6.923220068888362, 'clinton'), (-6.867377223688766, 'trump')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Get the class labels: class_labels\n",
        "class_labels = nb_classifier.classes_\n",
        "\n",
        "# Extract the features: feature_names\n",
        "feature_names = tfidf_vectorizer.get_feature_names_out()\n",
        "\n",
        "# Zip the feature names together with the coefficient array\n",
        "# and sort by weights: feat_with_weights\n",
        "feat_with_weights = sorted(zip(nb_classifier.feature_log_prob_[0], feature_names))\n",
        "\n",
        "# Extract the top and bottom 20 features with their weights\n",
        "top_features = feat_with_weights[-20:]\n",
        "bottom_features = feat_with_weights[:20]\n",
        "\n",
        "# Unzip the features and weights\n",
        "top_weights, top_feature_names = zip(*top_features)\n",
        "bottom_weights, bottom_feature_names = zip(*bottom_features)\n",
        "\n",
        "# Create a figure with two subplots\n",
        "fig, axes = plt.subplots(2, 1, figsize=(10, 8))\n",
        "\n",
        "# Plot the top 20 features and their weights\n",
        "axes[0].barh(top_feature_names, top_weights, color='blue')\n",
        "axes[0].set_xlabel('Weight')\n",
        "axes[0].set_title('Top 20 Features and Weights')\n",
        "axes[0].invert_yaxis()\n",
        "axes[0].tick_params(axis='y', rotation=0)  # Rotate labels\n",
        "\n",
        "# Plot the bottom 20 features and their weights\n",
        "axes[1].barh(bottom_feature_names, bottom_weights, color='red')\n",
        "axes[1].set_xlabel('Weight')\n",
        "axes[1].set_title('Bottom 20 Features and Weights')\n",
        "axes[1].invert_yaxis()\n",
        "axes[1].tick_params(axis='y', rotation=0)  # Rotate labels\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "D3vmDokqDG7N",
        "outputId": "39e29bca-f9be-45ee-878e-d64d7256f0d1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#### $\\color{skyblue}{\\textbf{Connect with me:}}$\n",
        "\n",
        "\n",
        "[<img align=\"left\" src=\"https://cdn4.iconfinder.com/data/icons/social-media-icons-the-circle-set/48/twitter_circle-512.png\" width=\"32px\"/>][twitter]\n",
        "[<img align=\"left\" src=\"https://cdn-icons-png.flaticon.com/512/145/145807.png\" width=\"32px\"/>][linkedin]\n",
        "[<img align=\"left\" src=\"https://cdn2.iconfinder.com/data/icons/whcompare-blue-green-web-hosting-1/425/cdn-512.png\" width=\"32px\"/>][Portfolio]\n",
        "\n",
        "[twitter]: https://twitter.com/F4izy\n",
        "[linkedin]: https://www.linkedin.com/in/mohd-faizy/\n",
        "[Portfolio]: https://github.com/mohd-faizy"
      ],
      "metadata": {
        "id": "EupklH23D8hq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Oth.**"
      ],
      "metadata": {
        "id": "qcNrYWzosjbh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ROC-AUC**"
      ],
      "metadata": {
        "id": "PyYgLmWssdYD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn.datasets\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import roc_curve, roc_auc_score\n",
        "import plotly.express as px\n",
        "import pandas as pd\n",
        "\n",
        "# Random Classification dataset\n",
        "X, y = make_classification(n_samples=800, n_classes=2, random_state=1)\n",
        "model = LogisticRegression()\n",
        "model.fit(X, y)\n",
        "\n",
        "# Predict probabilities\n",
        "preds = model.predict_proba(X)[:, 1]\n",
        "\n",
        "fpr, tpr, thresh = roc_curve(y, preds)\n",
        "\n",
        "# Create a DataFrame\n",
        "roc_df = pd.DataFrame(zip(fpr, tpr, thresh), columns=[\"FPR\", \"TPR\", \"Thres_V\"])\n",
        "\n",
        "print(roc_df)\n",
        "\n",
        "fig = px.area(roc_df, x=\"FPR\", y=\"TPR\", hover_data=[\"Thres_V\"])\n",
        "\n",
        "for i in range(len(roc_df)):\n",
        "    if i % 20 == 0:\n",
        "        fig.add_annotation(x=roc_df.iloc[i][0],\n",
        "                           y=roc_df.iloc[i][1],\n",
        "                           text=f\"Thres_V: {roc_df.iloc[i][2]:.2f}\",\n",
        "                           showarrow=True,\n",
        "                           arrowhead=1)\n",
        "\n",
        "fig.add_annotation(x=0.5, y=0.5, text=f\"AUC={roc_auc_score(y, preds):.2f}\",showarrow=True)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "9WlQeY-yp-l7",
        "outputId": "73e15065-886d-4d9e-8a6c-dd356068b770"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          FPR       TPR   Thres_V\n",
            "0    0.000000  0.000000  1.999992\n",
            "1    0.000000  0.002481  0.999992\n",
            "2    0.000000  0.243176  0.988012\n",
            "3    0.002519  0.243176  0.987978\n",
            "4    0.002519  0.560794  0.896033\n",
            "..        ...       ...       ...\n",
            "139  0.929471  0.995037  0.012190\n",
            "140  0.929471  0.997519  0.011615\n",
            "141  0.944584  0.997519  0.009709\n",
            "142  0.944584  1.000000  0.009671\n",
            "143  1.000000  1.000000  0.000476\n",
            "\n",
            "[144 rows x 3 columns]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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