Information_Quality_Text.ipynb

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            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c5c5bb6a733c4e82a8fe3025c0580d00": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ludwigwittgenstein2/Research/blob/master/Information_Quality_Text.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Information Quality of Text\n",
        "\n",
        "Dimensions:\n",
        "\n",
        "1. Relevance\n",
        "2. Consistency\n",
        "3. Accuracy\n",
        "\n"
      ],
      "metadata": {
        "id": "2wWoTcKE-gsF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Gpt-2 Text Generator\n"
      ],
      "metadata": {
        "id": "-6voorBx-r7I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q gpt-2-simple\n",
        "import gpt_2_simple as gpt2\n",
        "from datetime import datetime\n",
        "from google.colab import files\n"
      ],
      "metadata": {
        "id": "deIZHpnJ-tt8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80f0573a-8d4d-4629-8eef-c11aaedb90bf"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for gpt-2-simple (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_y36pAWw-vQH",
        "outputId": "13314a82-92d2-4767-a243-f4c801786a2f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Oct 28 21:31:08 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   42C    P8    11W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ">\n",
        "There are three released sizes of GPT-2:\n",
        "\n",
        "124M (default): the \"small\" model, 500MB on disk.\n",
        "\n",
        "355M: the \"medium\" model, 1.5GB on disk.\n",
        "\n",
        "774M: the \"large\" model, cannot currently be finetuned with Colaboratory but can be used to generate text from the pretrained model (see later in Notebook)\n",
        "\n",
        "1558M: the \"extra large\", true model. Will not work if a K80 GPU is attached to the notebook. (like 774M, it cannot be finetuned).\n"
      ],
      "metadata": {
        "id": "rintaMol-4Dt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9CrvpVc-aN4",
        "outputId": "8dc5aa8b-94d0-48cc-9941-850c99712e1f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Fetching checkpoint: 1.05Mit [00:00, 3.84Git/s]                                                     \n",
            "Fetching encoder.json: 1.05Mit [00:00, 4.45Mit/s]\n",
            "Fetching hparams.json: 1.05Mit [00:00, 4.61Git/s]                                                   \n",
            "Fetching model.ckpt.data-00000-of-00001: 498Mit [00:07, 65.6Mit/s]                                  \n",
            "Fetching model.ckpt.index: 1.05Mit [00:00, 2.47Git/s]                                               \n",
            "Fetching model.ckpt.meta: 1.05Mit [00:00, 5.89Mit/s]\n",
            "Fetching vocab.bpe: 1.05Mit [00:00, 6.68Mit/s]\n"
          ]
        }
      ],
      "source": [
        "gpt2.download_gpt2(model_name=\"124M\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gpt2.mount_gdrive()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q0fxnmdj_CQC",
        "outputId": "eb60bf06-b89f-46a3-fcdf-16945b331dd2"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file_name = \"CleanedDatasetEntire1.txt\""
      ],
      "metadata": {
        "id": "WGlOcknf_EBT"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gpt2.copy_file_from_gdrive(file_name)"
      ],
      "metadata": {
        "id": "jk1DXkXC_IsR"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sess = gpt2.start_tf_sess()\n",
        "\n",
        "gpt2.finetune(sess,\n",
        "              dataset=file_name,\n",
        "              model_name='124M',\n",
        "              steps=50,\n",
        "              restore_from='fresh',\n",
        "              run_name='run1',\n",
        "              print_every=10,\n",
        "              sample_every=200,\n",
        "              save_every=500,\n",
        "              reuse=True,\n",
        "              )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8MejE25A_KJg",
        "outputId": "d1add084-6a9f-48f0-9ccf-8aaee4c8a99e"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading checkpoint models/124M/model.ckpt\n",
            "Loading dataset...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1/1 [00:00<00:00, 678.03it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dataset has 17233 tokens\n",
            "Training...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10 | 26.92] loss=2.46 avg=2.46\n",
            "[20 | 50.09] loss=1.91 avg=2.18\n",
            "[30 | 73.66] loss=1.59 avg=1.98\n",
            "[40 | 96.76] loss=1.48 avg=1.86\n",
            "[50 | 119.71] loss=1.36 avg=1.76\n",
            "Saving checkpoint/run1/model-50\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gpt2.copy_checkpoint_to_gdrive(run_name='run1')"
      ],
      "metadata": {
        "id": "3NKKgBjZ_LhQ"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wlSMsN8I_M99"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "tf.compat.v1.reset_default_graph()"
      ],
      "metadata": {
        "id": "2vIS-N2N_NBH"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sess = gpt2.start_tf_sess()\n",
        "gpt2.load_gpt2(sess, run_name='run1')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0X0QKsWB_PxZ",
        "outputId": "16102eb0-e343-4659-ffca-be56f768990f"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading checkpoint checkpoint/run1/model-50\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gpt2.generate(sess, run_name='run1', destination_path='generated_text.txt')"
      ],
      "metadata": {
        "id": "W2LkUKXF_RBF"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gen_file = 'gpt2_gentext_{:%Y%m%d_%H%M%S}.txt'.format(datetime.utcnow())\n",
        "\n",
        "gpt2.generate_to_file(sess,\n",
        "                      destination_path=gen_file,\n",
        "                      length=500,\n",
        "                      temperature=0.7,\n",
        "                      nsamples=100,\n",
        "                      batch_size=20\n",
        "                      )"
      ],
      "metadata": {
        "id": "SXqUNVEQ_Ue9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "single_text = gpt2.generate(sess, return_as_list=False,destination_path='synthetic_GPT_2.txt' )\n",
        "print((single_text))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GWIC7Myq_V2_",
        "outputId": "e4f49bd0-ec3c-4648-c04e-fc15ec63349d"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "\n",
        "with open('single_text.txt', 'w') as f:\n",
        "\t# using csv.writer method from CSV package\n",
        "\twrite = csv.writer(f)\n",
        "\twrite.writerow(single_text)\n",
        "\n"
      ],
      "metadata": {
        "id": "aC7N4MoZAvXu"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bDpaqfbDDMAJ",
        "outputId": "dbed52f7-ba58-4d4f-ee71-10d7b755b0fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "\n",
        "# Open a CSV file for writing and write the text to it\n",
        "with open('data.txt', 'w', newline='') as file:\n",
        "    writer = csv.writer(file)\n",
        "    writer.writerow(single_text)\n",
        "\n",
        "print(\"Data exported to data.txt successfully!\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BTrFwAUEAv87",
        "outputId": "6ca1de49-35e1-4e60-8230-415b4b6071a3"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data exported to data.txt successfully!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PqcwF4qFAy1j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Evaluate Quality of Text:\n"
      ],
      "metadata": {
        "id": "gXYfluAiBlU2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "KL-Divergence"
      ],
      "metadata": {
        "id": "yScjUjn8MWd-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from scipy.stats import entropy\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Read the data from the first CSV file\n",
        "with open('/content/CleanedDatasetEntire1.txt', 'r') as file1:\n",
        "    reader1 = csv.reader(file1)\n",
        "    data1 = [row[0] for row in reader1]\n",
        "\n",
        "# Read the data from the second CSV file\n",
        "with open('/content/single_text.txt', 'r') as file2:\n",
        "    reader2 = csv.reader(file2)\n",
        "    data2 = [row[0] for row in reader2]\n",
        "\n",
        "# Convert the strings into a vector representation\n",
        "vectorizer = CountVectorizer()\n",
        "data1_vector = vectorizer.fit_transform(data1)\n",
        "data2_vector = vectorizer.transform(data2)\n",
        "\n",
        "# Convert the vector representation into a numpy array\n",
        "data1_array = data1_vector.toarray().sum(axis=0)\n",
        "data2_array = data2_vector.toarray().sum(axis=0)\n",
        "\n",
        "# Normalize the arrays\n",
        "data1_array = data1_array / data1_array.sum()\n",
        "data2_array = data2_array / data2_array.sum()\n",
        "\n",
        "# Add a small constant to all elements\n",
        "data1_array = data1_array + 1e-10\n",
        "data2_array = data2_array + 1e-10\n",
        "\n",
        "# Normalize the arrays again\n",
        "data1_array = data1_array / data1_array.sum()\n",
        "data2_array = data2_array / data2_array.sum()\n",
        "\n",
        "# Calculate the KL divergence between the two arrays\n",
        "kl_divergence = entropy(data1_array, data2_array)\n",
        "\n",
        "print(\"The KL divergence between the two arrays is:\", kl_divergence)\n",
        "\n",
        "# Plot the arrays using seaborn\n",
        "sns.distplot(data1_array, hist=False, label='GPT-2 generated')\n",
        "sns.distplot(data2_array, hist=False, label='US-Census Entire Dataset')\n",
        "plt.xlabel('Value')\n",
        "plt.ylabel('Probability')\n",
        "plt.title('Distributions of Synthetic  dataset and Original Name Address Dataset')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 883
        },
        "id": "V_ncG1amMY2l",
        "outputId": "490757a3-7b1e-426c-8f5d-4d0a951ddb9a"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The KL divergence between the two arrays is: 11.704128455529556\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-40-ad68590a5a68>:45: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(data1_array, hist=False, label='GPT-2 generated')\n",
            "<ipython-input-40-ad68590a5a68>:46: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(data2_array, hist=False, label='US-Census Entire Dataset')\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": [],
      "metadata": {
        "id": "4zbzPzKFk_63"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "S7ApLvC4k__n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "Wz5niGL3OIzd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Cosine"
      ],
      "metadata": {
        "id": "MIcuE2VXMhGL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "import string\n",
        "from collections import Counter\n",
        "\n",
        "def read_file(file_name):\n",
        "    with open(file_name, 'r') as file:\n",
        "        return file.read()\n",
        "\n",
        "def clean_text(text):\n",
        "    text = text.lower()\n",
        "    text = text.translate(str.maketrans('', '', string.punctuation))\n",
        "    return text\n",
        "\n",
        "def get_cosine_similarity(text1, text2):\n",
        "    text1 = clean_text(text1)\n",
        "    text2 = clean_text(text2)\n",
        "\n",
        "    words1 = text1.split()\n",
        "    words2 = text2.split()\n",
        "\n",
        "    word_counts1 = Counter(words1)\n",
        "    word_counts2 = Counter(words2)\n",
        "\n",
        "    common_words = set(word_counts1.keys()) & set(word_counts2.keys())\n",
        "\n",
        "    dot_product = sum(word_counts1[word] * word_counts2[word] for word in common_words)\n",
        "    magnitude1 = math.sqrt(sum(word_counts1[word] ** 2 for word in word_counts1.keys()))\n",
        "    magnitude2 = math.sqrt(sum(word_counts2[word] ** 2 for word in word_counts2.keys()))\n",
        "\n",
        "    return dot_product / (magnitude1 * magnitude2)\n",
        "\n",
        "text1 = read_file('CleanedDatasetEntire1.txt')\n",
        "text2 = read_file('single_text.txt')\n",
        "\n",
        "cosine_similarity = get_cosine_similarity(text1, text2)\n",
        "\n",
        "print('Cosine Similarity between Synthetic dataset and original dataset:', cosine_similarity)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2_0TsIzBnWy",
        "outputId": "88a08423-e753-45a6-f74a-291b9d7ce80e"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cosine Similarity between Synthetic dataset and original dataset: 0.758613854863566\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Levenshtein Distance"
      ],
      "metadata": {
        "id": "0AEw5x5GOZtu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install python-Levenshtein\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LLKya2y8OJ3u",
        "outputId": "082adfce-378b-46b3-ed60-d72bdeb21091"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting python-Levenshtein\n",
            "  Downloading python_Levenshtein-0.23.0-py3-none-any.whl (9.4 kB)\n",
            "Collecting Levenshtein==0.23.0 (from python-Levenshtein)\n",
            "  Downloading Levenshtein-0.23.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (169 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m169.4/169.4 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting rapidfuzz<4.0.0,>=3.1.0 (from Levenshtein==0.23.0->python-Levenshtein)\n",
            "  Downloading rapidfuzz-3.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: rapidfuzz, Levenshtein, python-Levenshtein\n",
            "Successfully installed Levenshtein-0.23.0 python-Levenshtein-0.23.0 rapidfuzz-3.4.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import Levenshtein\n",
        "\n",
        "# Function to read the contents of a text file\n",
        "def read_file(filename):\n",
        "    with open(filename, 'r', encoding='utf-8') as file:\n",
        "        return file.read()\n",
        "\n",
        "# Read the contents of the two files\n",
        "file1_contents = read_file('CleanedDatasetEntire1.txt')\n",
        "file2_contents = read_file('single_text.txt')\n",
        "\n",
        "# Calculate the Levenshtein distance\n",
        "distance = Levenshtein.distance(file1_contents, file2_contents)\n",
        "\n",
        "# Print the result\n",
        "print(f\"Levenshtein distance between the two files: {distance}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PHZfrS9wOLA1",
        "outputId": "919a5f53-b18c-410f-dfc7-3c30e05d4bec"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Levenshtein distance between the two files: 36229\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Kolmogorov-Smirnov"
      ],
      "metadata": {
        "id": "-bsxdKVmOwCp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "kBnm0sHhOcoM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import ks_2samp\n",
        "\n",
        "# Function to read the contents of a text file and calculate the word lengths\n",
        "def calculate_word_lengths(filename):\n",
        "    with open(filename, 'r', encoding='utf-8') as file:\n",
        "        text = file.read()\n",
        "        # Split the text into words and calculate word lengths\n",
        "        word_lengths = [len(word) for word in text.split()]\n",
        "        return word_lengths\n",
        "\n",
        "# Read the contents of the two files and calculate word lengths\n",
        "word_lengths1 = calculate_word_lengths('CleanedDatasetEntire1.txt')\n",
        "word_lengths2 = calculate_word_lengths('single_text.txt')\n",
        "\n",
        "# Perform the Kolmogorov-Smirnov test\n",
        "ks_statistic, p_value = ks_2samp(word_lengths1, word_lengths2)\n",
        "\n",
        "# Print the result\n",
        "print(f\"KS Statistic: {ks_statistic}\")\n",
        "print(f\"P-Value: {p_value}\")\n",
        "\n",
        "# Determine the result based on the p-value\n",
        "if p_value < 0.05:\n",
        "    print(\"The distributions are significantly different.\")\n",
        "else:\n",
        "    print(\"The distributions are not significantly different.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cv-0XgJ3M4-D",
        "outputId": "57e18133-fe02-40b4-8356-bd5816a4c365"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KS Statistic: 0.05013419410510688\n",
            "P-Value: 0.26023676964783\n",
            "The distributions are not significantly different.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Pearson Correlation"
      ],
      "metadata": {
        "id": "v5IKHXQBPI3v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import collections\n",
        "import math\n",
        "\n",
        "# Function to read the contents of a text file and calculate word frequencies\n",
        "def calculate_word_frequencies(filename):\n",
        "    with open(filename, 'r', encoding='utf-8') as file:\n",
        "        text = file.read()\n",
        "        words = text.split()\n",
        "        word_counts = collections.Counter(words)\n",
        "        return word_counts\n",
        "\n",
        "# Read the contents of the two files and calculate word frequencies\n",
        "word_frequencies1 = calculate_word_frequencies('CleanedDatasetEntire1.txt')\n",
        "word_frequencies2 = calculate_word_frequencies('single_text.txt')\n",
        "\n",
        "# Create sets of unique words from both texts\n",
        "unique_words = set(word_frequencies1.keys()).union(word_frequencies2.keys())\n",
        "\n",
        "# Convert word frequencies to a common format, considering words that may be missing\n",
        "word_counts1 = [word_frequencies1[word] if word in word_frequencies1 else 0 for word in unique_words]\n",
        "word_counts2 = [word_frequencies2[word] if word in word_frequencies2 else 0 for word in unique_words]\n",
        "\n",
        "# Calculate the Pearson correlation\n",
        "def pearson_correlation(x, y):\n",
        "    n = len(x)\n",
        "    sum_x = sum(x)\n",
        "    sum_y = sum(y)\n",
        "    sum_x_squared = sum(xi ** 2 for xi in x)\n",
        "    sum_y_squared = sum(yi ** 2 for yi in y)\n",
        "    product_sum = sum(xi * yi for xi, yi in zip(x, y))\n",
        "    numerator = product_sum - (sum_x * sum_y / n)\n",
        "    denominator = math.sqrt((sum_x_squared - (sum_x ** 2 / n)) * (sum_y_squared - (sum_y ** 2 / n)))\n",
        "    if denominator == 0:\n",
        "        return 0  # To handle the case where the denominator is zero\n",
        "    else:\n",
        "        return numerator / denominator\n",
        "\n",
        "correlation = pearson_correlation(word_counts1, word_counts2)\n",
        "\n",
        "# Print the Pearson correlation coefficient\n",
        "print(f\"Pearson Correlation: {correlation}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TU4MBfC4O0pK",
        "outputId": "1bbc0e90-d248-45d1-a6f8-6f27b62a9e07"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pearson Correlation: 0.8289836512421701\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Jaccard Distance"
      ],
      "metadata": {
        "id": "WgxvkGlZPNAk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to read the contents of a text file and tokenize it\n",
        "def tokenize_text(filename):\n",
        "    with open(filename, 'r', encoding='utf-8') as file:\n",
        "        text = file.read()\n",
        "        # Tokenize the text by splitting it into words (you can use more advanced tokenization methods if needed)\n",
        "        tokens = set(text.split())\n",
        "        return tokens\n",
        "\n",
        "# Read the contents of the two files and tokenize them\n",
        "tokens1 = tokenize_text('CleanedDatasetEntire1.txt')\n",
        "tokens2 = tokenize_text('single_text.txt')\n",
        "\n",
        "# Calculate the Jaccard similarity\n",
        "intersection = len(tokens1.intersection(tokens2))\n",
        "union = len(tokens1.union(tokens2))\n",
        "\n",
        "jaccard_similarity = intersection / union\n",
        "\n",
        "# Print the Jaccard similarity\n",
        "print(f\"Jaccard Similarity: {jaccard_similarity:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bBnWUE3sPWk2",
        "outputId": "902ac50a-d9bb-41cb-fecf-3c50e810fe75"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jaccard Similarity: 0.04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Jaccard Similiarity"
      ],
      "metadata": {
        "id": "z_L5DazBPrKz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to read the contents of a text file and tokenize it\n",
        "def tokenize_text(filename):\n",
        "    with open(filename, 'r', encoding='utf-8') as file:\n",
        "        text = file.read()\n",
        "        # Tokenize the text by splitting it into words (you can use more advanced tokenization methods if needed)\n",
        "        tokens = set(text.split())\n",
        "        return tokens\n",
        "\n",
        "# Read the contents of the two files and tokenize them\n",
        "tokens1 = tokenize_text('CleanedDatasetEntire1.txt')\n",
        "tokens2 = tokenize_text('single_text.txt')\n",
        "\n",
        "# Calculate the Jaccard distance\n",
        "intersection = len(tokens1.intersection(tokens2))\n",
        "union = len(tokens1.union(tokens2))\n",
        "\n",
        "jaccard_similarity = intersection / union\n",
        "jaccard_distance = 1 - jaccard_similarity\n",
        "\n",
        "# Print the Jaccard distance\n",
        "print(f\"Jaccard Distance: {jaccard_distance:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fpWYGfURPhW5",
        "outputId": "35c2956c-47b1-4972-f7a5-0b2e4b9d2242"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jaccard Distance: 0.96\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gensim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YVAQGYNKQsa-",
        "outputId": "f019859d-eb04-4424-f21b-7445dbaa9608"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: gensim in /usr/local/lib/python3.10/dist-packages (4.3.2)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.11.3)\n",
            "Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from gensim) (6.4.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vfBNSUK6ROhm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Word Embedding Similarity"
      ],
      "metadata": {
        "id": "MXQLdnDwUd7m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m spacy download en_core_web_lg\n",
        "!python -m spacy download en_core_web_sm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KaNMARsSUC8N",
        "outputId": "914d6542-25b4-484c-f12e-d9e14ce0e84e"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-10-28 22:01:08.156731: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2023-10-28 22:01:08.156795: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2023-10-28 22:01:08.156834: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2023-10-28 22:01:11.220407: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "Collecting en-core-web-lg==3.6.0\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-3.6.0/en_core_web_lg-3.6.0-py3-none-any.whl (587.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m587.7/587.7 MB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: spacy<3.7.0,>=3.6.0 in /usr/local/lib/python3.10/dist-packages (from en-core-web-lg==3.6.0) (3.6.1)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (3.0.12)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (1.0.5)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (1.0.10)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (2.0.8)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (3.0.9)\n",
            "Requirement already satisfied: thinc<8.2.0,>=8.1.8 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (8.1.12)\n",
            "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (1.1.2)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (2.4.8)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (2.0.10)\n",
            "Requirement already satisfied: typer<0.10.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (0.9.0)\n",
            "Requirement already satisfied: pathy>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (0.10.3)\n",
            "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (6.4.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (4.66.1)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (1.23.5)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (2.31.0)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (1.10.13)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (3.1.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (67.7.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (23.2)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (3.3.0)\n",
            "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (3.3.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (2023.7.22)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (0.7.11)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (0.1.3)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.10.0,>=0.3.0->spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (8.1.7)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (2.1.3)\n",
            "Installing collected packages: en-core-web-lg\n",
            "Successfully installed en-core-web-lg-3.6.0\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('en_core_web_lg')\n",
            "2023-10-28 22:02:06.742160: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2023-10-28 22:02:06.742229: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2023-10-28 22:02:06.742267: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2023-10-28 22:02:08.267244: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "Collecting en-core-web-sm==3.6.0\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.6.0/en_core_web_sm-3.6.0-py3-none-any.whl (12.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.8/12.8 MB\u001b[0m \u001b[31m74.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: spacy<3.7.0,>=3.6.0 in /usr/local/lib/python3.10/dist-packages (from en-core-web-sm==3.6.0) (3.6.1)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.0.12)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.0.5)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.0.10)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.0.8)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.0.9)\n",
            "Requirement already satisfied: thinc<8.2.0,>=8.1.8 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (8.1.12)\n",
            "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.1.2)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.4.8)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.0.10)\n",
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            "Requirement already satisfied: pathy>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (0.10.3)\n",
            "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (6.4.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (4.66.1)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.23.5)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.31.0)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.10.13)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.1.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (67.7.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (23.2)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.3.0)\n",
            "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.3.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2023.7.22)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (0.7.11)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (0.1.3)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.10.0,>=0.3.0->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (8.1.7)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.1.3)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('en_core_web_sm')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy\n",
        "\n",
        "# Load the spaCy model with word vectors (you can use other models as well)\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "\n",
        "# Read the contents of \"CleanedDatasetEntire1.txt\" and \"single_text.txt\"\n",
        "with open(\"CleanedDatasetEntire1.txt\", \"r\", encoding=\"utf-8\") as file:\n",
        "    text1 = file.read()\n",
        "\n",
        "with open(\"single_text.txt\", \"r\", encoding=\"utf-8\") as file:\n",
        "    text2 = file.read()\n",
        "\n",
        "# Process the text with spaCy\n",
        "doc1 = nlp(text1)\n",
        "doc2 = nlp(text2)\n",
        "\n",
        "# Calculate the similarity between the two documents (cosine similarity)\n",
        "similarity = doc1.similarity(doc2)\n",
        "\n",
        "print(f\"Word Embedding Similarity: {similarity:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OZh3kSdVQsu0",
        "outputId": "834d6c54-8edf-4b2b-c93f-1b298375f856"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Word Embedding Similarity: 0.99\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-50-6c2ad36c846d>:18: UserWarning: [W007] The model you're using has no word vectors loaded, so the result of the Doc.similarity method will be based on the tagger, parser and NER, which may not give useful similarity judgements. This may happen if you're using one of the small models, e.g. `en_core_web_sm`, which don't ship with word vectors and only use context-sensitive tensors. You can always add your own word vectors, or use one of the larger models instead if available.\n",
            "  similarity = doc1.similarity(doc2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#BLEU SCORE"
      ],
      "metadata": {
        "id": "GXJJwFz8UwOP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install nltk\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wv3Ubu-oUx1m",
        "outputId": "910652ef-5329-408f-ab01-2a219ca9ac9c"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (3.8.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk) (1.3.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk) (2023.6.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk) (4.66.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from nltk.translate.bleu_score import sentence_bleu\n",
        "\n",
        "# Read the contents of \"CleanedDatasetEntire1.txt\" and \"single_text.txt\"\n",
        "with open(\"CleanedDatasetEntire1.txt\", \"r\", encoding=\"utf-8\") as file:\n",
        "    reference_text = file.read()\n",
        "\n",
        "with open(\"single_text.txt\", \"r\", encoding=\"utf-8\") as file:\n",
        "    candidate_text = file.read()\n",
        "\n",
        "# Tokenize the reference and candidate texts\n",
        "reference_tokens = nltk.word_tokenize(reference_text)\n",
        "candidate_tokens = nltk.word_tokenize(candidate_text)\n",
        "\n",
        "# Calculate the BLEU Score\n",
        "bleu_score = sentence_bleu([reference_tokens], candidate_tokens)\n",
        "\n",
        "print(f\"BLEU Score: {bleu_score:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kvUoIUE6Uy6G",
        "outputId": "b23d6597-ba11-43f0-e1fb-243b20c4e1b7"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BLEU Score: 0.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Precision, Recall, F-1"
      ],
      "metadata": {
        "id": "wSZgTXYKf5ML"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "# Read the contents of \"CleanedDatasetEntire1.txt\" and \"single_text.txt\"\n",
        "with open(\"CleanedDatasetEntire1.txt\", \"r\", encoding=\"utf-8\") as file:\n",
        "    text1 = file.read()\n",
        "\n",
        "with open(\"single_text.txt\", \"r\", encoding=\"utf-8\") as file:\n",
        "    text2 = file.read()\n",
        "\n",
        "# Assuming you have binary labels (0 for incorrect, 1 for correct) for both texts\n",
        "labels1 = [1, 0]  # Replace with the actual labels for \"CleanedDatasetEntire1.txt\"\n",
        "labels2 = [1, 1]  # Replace with the actual labels for \"single_text.txt\"\n",
        "\n",
        "# Calculate the F1 score, accuracy, precision, and recall\n",
        "f1 = f1_score(labels1, labels2)\n",
        "accuracy = accuracy_score(labels1, labels2)\n",
        "precision = precision_score(labels1, labels2)\n",
        "recall = recall_score(labels1, labels2)\n",
        "\n",
        "print(f\"F1 Score: {f1:.2f}\")\n",
        "print(f\"Accuracy: {accuracy:.2f}\")\n",
        "print(f\"Precision: {precision:.2f}\")\n",
        "print(f\"Recall: {recall:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "--AdVg44f7Q-",
        "outputId": "50d9f438-b402-4971-908d-40b31d2e1a7f"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F1 Score: 0.67\n",
            "Accuracy: 0.50\n",
            "Precision: 0.50\n",
            "Recall: 1.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#"
      ],
      "metadata": {
        "id": "YwW1AS14fhet"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wQ6Lonn-UCKd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#LSTM Synthetic dataset"
      ],
      "metadata": {
        "id": "IiO8qbSElAzk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM, Dense, Embedding\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "# 1. Preprocess the data\n",
        "with open(\"CleanedDatasetEntire1.txt\", \"r\") as file:\n",
        "    text = file.read()\n",
        "\n",
        "# Tokenize the text\n",
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts([text])\n",
        "vocab_size = len(tokenizer.word_index) + 1\n",
        "\n",
        "# Convert text to sequences of integers\n",
        "sequences = []\n",
        "for line in text.split(\"\\n\"):\n",
        "    encoded = tokenizer.texts_to_sequences([line])[0]\n",
        "    for i in range(1, len(encoded)):\n",
        "        sequences.append(encoded[:i+1])\n",
        "\n",
        "# Pad sequences for consistent length\n",
        "max_length = max([len(seq) for seq in sequences])\n",
        "sequences = pad_sequences(sequences, maxlen=max_length, padding='pre')\n",
        "\n",
        "# Split sequences into input and output elements\n",
        "X, y = sequences[:,:-1], sequences[:,-1]\n",
        "y = to_categorical(y, num_classes=vocab_size)\n",
        "\n",
        "# 2. Create an LSTM model\n",
        "model = Sequential()\n",
        "model.add(Embedding(vocab_size, 50, input_length=max_length-1))\n",
        "model.add(LSTM(100))\n",
        "model.add(Dense(vocab_size, activation='softmax'))\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "# 3. Train the model\n",
        "model.fit(X, y, epochs=20, verbose=2)\n",
        "\n",
        "# 4. Generate synthetic data\n",
        "def generate_text(seed_text, model, max_length, num_words):\n",
        "    output_text = seed_text\n",
        "    for _ in range(num_words):\n",
        "        encoded = tokenizer.texts_to_sequences([seed_text])[0]\n",
        "        encoded = pad_sequences([encoded], maxlen=max_length-1, padding='pre')\n",
        "        prediction = np.argmax(model.predict(encoded), axis=-1)\n",
        "        predicted_word = \"\"\n",
        "        for word, index in tokenizer.word_index.items():\n",
        "            if index == prediction:\n",
        "                predicted_word = word\n",
        "                break\n",
        "        seed_text += \" \" + predicted_word\n",
        "        output_text += \" \" + predicted_word\n",
        "    return output_text\n",
        "\n",
        "# Generate new text based on a seed\n",
        "seed = \"This is a\"\n",
        "synthetic_data = generate_text(seed, model, max_length, 500)  # Generating 500 words\n",
        "\n",
        "# Save synthetic data to file\n",
        "with open(\"LSTMtraining.txt\", \"w\") as output_file:\n",
        "    output_file.write(synthetic_data)\n",
        "\n",
        "print(\"Synthetic data saved to LSTMtraining.txt!\")\n"
      ],
      "metadata": {
        "id": "jhoyJi73lDGL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#GPT-NEO"
      ],
      "metadata": {
        "id": "GouwIyDvr0Sz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import GPTJForCausalLM, GPT2Tokenizer\n",
        "\n",
        "# Load the GPT-J model and tokenizer\n",
        "model = GPTJForCausalLM.from_pretrained(\"EleutherAI/gpt-j-6B\")\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(\"EleutherAI/gpt-j-6B\")\n",
        "\n",
        "# Open the CleanedDatasetEntire1.txt file and read its contents\n",
        "with open(\"CleanedDatasetEntire1.txt\", \"r\") as f:\n",
        "    text = f.read()\n",
        "\n",
        "# Generate text using the GPT-J model\n",
        "input_ids = tokenizer.encode(text, return_tensors=\"pt\")\n",
        "output = model.generate(input_ids, max_length=2048, do_sample=True, pad_token_id=model.config.eos_token_id, attention_mask=input_ids.ne(model.config.pad_token_id))\n",
        "\n",
        "# Decode the generated text and save it to a file\n",
        "generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "with open(\"GPT-JSynthetic.txt\", \"w\") as f:\n",
        "    f.write(generated_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "bd065c4f6a9a4056b4fb3b729d53b9c2",
            "09cf9c09fc9a437bb94651006e2ca787",
            "9d6a1cdec3e842e1840c0a4f23187368",
            "99f6bb32d9874a8ba272b8c0906120fd",
            "cfda8138da284cf39f863e46f8635eba",
            "b787bfbbf15f45febc5de95e00647f54",
            "12844b368e2b47ffb5772997c0e1f357",
            "5a2da8d24c1f41c797342c0cdc9e1b94",
            "918755a629634318979b7c8790531f9a",
            "53ef7e3bc1ef4f2aadac05979644c87e",
            "918b10b3f65a43d9a04d613b750f4523",
            "8be5cefdd8bc4d1eb47b2c43a6377a0a",
            "72472f7c9f734b1885da7f7da24645b0",
            "788a70692a744548bdff30418e984a0a",
            "45ffc4e75ea74d5982b8e590d19c7470",
            "a596741c292b415f9d9002230f3774a6",
            "afc292591b02440483049f06f347beed",
            "709d5cb8cef14c7997dc2bfdeeea0835",
            "820d27f518294d04b310b138e49db1d6",
            "653c7d32d97e4a1db1dd65857d2a18d2",
            "ba13735214484537974d9695b234fa0a",
            "c5c5bb6a733c4e82a8fe3025c0580d00"
          ]
        },
        "id": "5QR6jILokSXX",
        "outputId": "92d9e6b4-8a1e-40bc-bd19-b75c5ca25b85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/930 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bd065c4f6a9a4056b4fb3b729d53b9c2"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/24.2G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "PWCx01gwUjco"
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    },
    {
      "cell_type": "markdown",
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      "metadata": {
        "id": "ciZ9G5DGMwu9"
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    }
  ]
}