{
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python",
      "version": "3.10.13",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kaggle": {
      "accelerator": "none",
      "dataSources": [],
      "dockerImageVersionId": 30746,
      "isInternetEnabled": false,
      "language": "python",
      "sourceType": "notebook",
      "isGpuEnabled": false
    }
  },
  "nbformat_minor": 0,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# --- INSTALLATION ---\n",
        "\n",
        "!pip install pandas matplotlib seaborn\n",
        "!kaggle datasets download -d shanegerami/ai-vs-human-text\n",
        "!unzip -n ai-vs-human-text.zip\n",
        "!rm ai-vs-human-text.zip\n",
        "\n",
        "# -------------------------"
      ],
      "metadata": {
        "id": "XKWBDF8lir6o",
        "execution": {
          "iopub.status.busy": "2024-08-14T18:13:18.903225Z",
          "iopub.execute_input": "2024-08-14T18:13:18.903635Z",
          "iopub.status.idle": "2024-08-14T18:14:34.119173Z",
          "shell.execute_reply.started": "2024-08-14T18:13:18.903599Z",
          "shell.execute_reply": "2024-08-14T18:14:34.117649Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# --- IMPORTS ---\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# -------------------------"
      ],
      "metadata": {
        "id": "q9TGKRUIiPMy"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# --- READING DATASET ---\n",
        "\n",
        "df = pd.read_csv('AI_Human.csv')\n",
        "\n",
        "# -----------------------"
      ],
      "metadata": {
        "id": "hbMRED5HyFhF"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "First let's check out the size and value counts of the original dataset."
      ],
      "metadata": {
        "id": "QUIKztclzdZg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# --- DATASET INFO ---\n",
        "\n",
        "print(f\"The dataframe has {df.shape[0]} rows and {df.shape[1]} columns.\")\n",
        "print(f\"The columns names are: {[column for column in df.columns]}\")\n",
        "print(df.head())\n",
        "print('\\n')\n",
        "print(df['generated'].value_counts())\n",
        "print('\\n')\n",
        "print(f\"There are {(df['generated'].value_counts()[0] / df['generated'].value_counts()[1]):.2f} times more human written essays than AI-generated ones.\\n\")\n",
        "\n",
        "# Plot\n",
        "labels = ['Human', 'AI']\n",
        "df['generated'].value_counts().plot(kind='bar', grid=True, title=\"Human/AI Counts\", xlabel=\"Human/AI\", ylabel=\"Count\")\n",
        "plt.xticks(range(len(labels)), labels, rotation=0)\n",
        "plt.show()\n",
        "\n",
        "# ---------------------"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 784
        },
        "id": "LDBBytcmx8p2",
        "outputId": "380f4fd5-d25b-47be-f63f-b98bb80b234f"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The dataframe has 487235 rows and 2 columns.\n",
            "The columns names are: ['text', 'generated']\n",
            "                                                text  generated\n",
            "0  Cars. Cars have been around since they became ...        0.0\n",
            "1  Transportation is a large necessity in most co...        0.0\n",
            "2  \"America's love affair with it's vehicles seem...        0.0\n",
            "3  How often do you ride in a car? Do you drive a...        0.0\n",
            "4  Cars are a wonderful thing. They are perhaps o...        0.0\n",
            "\n",
            "\n",
            "generated\n",
            "0.0    305797\n",
            "1.0    181438\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "There are 1.69 times more human written essays than AI-generated ones.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's check out how are these texts we have in the dataset."
      ],
      "metadata": {
        "id": "e63p04LN3c1H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# --- TEXT DATA ---\n",
        "\n",
        "# Calculate the word lengths for each essay\n",
        "df['word_length'] = df['text'].apply(lambda x: len(x.split()))\n",
        "\n",
        "# Separate the data into human-written and AI-generated essays\n",
        "human_lengths = df[df['generated'] == 0]['word_length']\n",
        "ai_lengths = df[df['generated'] == 1]['word_length']\n",
        "\n",
        "# Plot the distribution for human-written essays\n",
        "sns.histplot(human_lengths, kde=True, color='lightblue', label='Human-Written', stat='density')\n",
        "\n",
        "# Plot the distribution for AI-generated essays\n",
        "sns.histplot(ai_lengths, kde=True, color='lightgreen', label='AI-Generated', stat='density')\n",
        "\n",
        "# Adding titles and labels\n",
        "plt.title('Distribution of Word Lengths in Essays')\n",
        "plt.xlabel('Word Length')\n",
        "plt.ylabel('Density')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# -----------------"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "8W9UxpF_3cTJ",
        "outputId": "3fad4bb6-acb9-4f71-c19e-c0c49c7f6118"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see from the plot, both of the seem to have an average word length of about 350-400 words. What we can also see is that human written ones are more disperse in terms of word length and tend to be longer while the vast majority of AI-generated texts tend to pile in the 350-400 range we talked about before."
      ],
      "metadata": {
        "id": "ewBbrofY6lmx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's address the unbalance issue and solve it by undersampling the bigger set."
      ],
      "metadata": {
        "id": "tuTLI5t63Bk2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# --- ADDRESSING UNBALANCE ---\n",
        "\n",
        "# Separate human from ai\n",
        "df_human = df[df[\"generated\"] == 0]\n",
        "df_ai = df[df[\"generated\"] == 1]\n",
        "\n",
        "# Undersample human\n",
        "df_ai_len = df_ai[\"text\"].count()\n",
        "df_human_sample = df_human.sample(n=df_ai_len)\n",
        "\n",
        "# We combine the two dataframes\n",
        "df_unshuffled = pd.concat([df_human_sample, df_ai])\n",
        "print(df_unshuffled[\"generated\"].value_counts())\n",
        "\n",
        "# Plot\n",
        "labels = ['Human', 'AI']\n",
        "df_unshuffled['generated'].value_counts().plot(kind='bar', grid=True, title=\"Human/AI Counts\", xlabel=\"Human/AI\", ylabel=\"Count\")\n",
        "plt.xticks(range(len(labels)), labels, rotation=0)\n",
        "plt.show()\n",
        "\n",
        "# ----------------------------"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "2n4p7BW63b4b",
        "outputId": "ec675666-b06d-4bdb-c8b7-f4d9bda34ddb"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "generated\n",
            "0.0    181438\n",
            "1.0    181438\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since this dataset is too large to fine-tune the model with my current resources, I'll undersample this dataset randomly."
      ],
      "metadata": {
        "id": "9nHxNMy796Y7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# --- UNDERSAMPLING ---\n",
        "\n",
        "df_shuffled = df_unshuffled.sample(frac=0.01).reset_index(drop=True)\n",
        "print(df_shuffled[\"generated\"].value_counts())\n",
        "df_shuffled[\"generated\"].value_counts().plot(kind='bar', grid=True, title=\"Human/AI Counts\", xlabel=\"Human/AI\", ylabel=\"Count\")\n",
        "plt.xticks(range(len(labels)), labels)\n",
        "plt.show()\n",
        "\n",
        "# ---------------------"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "4539ZQro8adW",
        "outputId": "c9164533-9d65-4e95-c2e1-ee9481cd8fbf"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "generated\n",
            "1.0    1854\n",
            "0.0    1775\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see we have succesfully undersample the dataset and balance it for the training. Next would be the tokenization and the training. Check out the [training](https://github.com/diegovelilla/EssAI/blob/main/essai_training.ipynb) and [testing](https://github.com/diegovelilla/EssAI/blob/main/essai_testing.ipynb) notebooks if interested in this."
      ],
      "metadata": {
        "id": "iFKPOOwQ-mLK"
      }
    }
  ]
}