Summarization_using_BERT_and_K_Means.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aashitadutta/FinancialNarrativeSummarization/blob/main/Summarization_using_BERT_and_K_Means.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H5ZgD8wZyvaF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c44d974-ef8b-40e3-b0e5-a871631febcf"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_yTU6eJoxvd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d25b4e0-806d-47f5-c26e-984084ae15df"
      },
      "source": [
        "cd /content/drive/MyDrive/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ExPvPCGO0-Le",
        "outputId": "eb4deb9d-7614-45e9-b8ac-0705b86f3d00"
      },
      "source": [
        "!pip install transformers==2.6.0\n",
        "\n",
        "import transformers\n",
        "from transformers import GPT2Tokenizer, GPT2DoubleHeadsModel, DistilBertModel, DistilBertTokenizer, BertTokenizer, BertForTokenClassification\n",
        "import numpy as np\n",
        "\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from nltk import sent_tokenize\n",
        "from nltk import word_tokenize\n",
        "%tensorflow_version 1.x\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.neighbors import NearestNeighbors\n",
        "\n",
        "import json\n",
        "import matplotlib.pyplot as plt\n",
        "import timeit\n",
        "import torch\n",
        "import textwrap\n",
        "wrapper = textwrap.TextWrapper(width=70)\n",
        "SEED = 1234\n",
        "torch.manual_seed(SEED)"
      ],
      "execution_count": null,
      "outputs": [
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          "output_type": "stream",
          "text": [
            "Collecting transformers==2.6.0\n",
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            "\u001b[?25hCollecting boto3\n",
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            "\u001b[K     |████████████████████████████████| 133kB 33.7MB/s \n",
            "\u001b[?25hCollecting tokenizers==0.5.2\n",
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            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers==2.6.0) (3.0.12)\n",
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            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==2.6.0) (7.1.2)\n",
            "Collecting s3transfer<0.5.0,>=0.4.0\n",
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            "\u001b[?25hCollecting jmespath<1.0.0,>=0.7.1\n",
            "  Downloading https://files.pythonhosted.org/packages/07/cb/5f001272b6faeb23c1c9e0acc04d48eaaf5c862c17709d20e3469c6e0139/jmespath-0.10.0-py2.py3-none-any.whl\n",
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            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==2.6.0) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==2.6.0) (1.24.3)\n",
            "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.7/dist-packages (from botocore<1.21.0,>=1.20.101->boto3->transformers==2.6.0) (2.8.1)\n",
            "\u001b[31mERROR: botocore 1.20.101 has requirement urllib3<1.27,>=1.25.4, but you'll have urllib3 1.24.3 which is incompatible.\u001b[0m\n",
            "Installing collected packages: sacremoses, jmespath, botocore, s3transfer, boto3, tokenizers, sentencepiece, transformers\n",
            "Successfully installed boto3-1.17.101 botocore-1.20.101 jmespath-0.10.0 s3transfer-0.4.2 sacremoses-0.0.45 sentencepiece-0.1.96 tokenizers-0.5.2 transformers-2.6.0\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "TensorFlow is already loaded. Please restart the runtime to change versions.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7fa6eb460cf0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SW26HzzhORmH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "341f5dea-03f1-4997-bf9a-c3808675c68f"
      },
      "source": [
        "!git lfs install\n",
        "!git clone 'https://huggingface.co/distilbert-base-uncased'\n",
        "!git clone 'https://huggingface.co/bert-base-cased'\n",
        "!git clone 'https://huggingface.co/bert-base-uncased'\n",
        "!git clone 'https://huggingface.co/gpt2'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "git: 'lfs' is not a git command. See 'git --help'.\n",
            "\n",
            "The most similar command is\n",
            "\tlog\n",
            "Cloning into 'distilbert-base-uncased'...\n",
            "remote: Enumerating objects: 44, done.\u001b[K\n",
            "remote: Counting objects: 100% (44/44), done.\u001b[K\n",
            "remote: Compressing objects: 100% (42/42), done.\u001b[K\n",
            "remote: Total 44 (delta 16), reused 0 (delta 0)\u001b[K\n",
            "Unpacking objects: 100% (44/44), done.\n",
            "Cloning into 'bert-base-cased'...\n",
            "remote: Enumerating objects: 61, done.\u001b[K\n",
            "remote: Counting objects: 100% (61/61), done.\u001b[K\n",
            "remote: Compressing objects: 100% (59/59), done.\u001b[K\n",
            "remote: Total 61 (delta 23), reused 0 (delta 0)\u001b[K\n",
            "Unpacking objects: 100% (61/61), done.\n",
            "Cloning into 'bert-base-uncased'...\n",
            "remote: Enumerating objects: 52, done.\u001b[K\n",
            "remote: Counting objects: 100% (52/52), done.\u001b[K\n",
            "remote: Compressing objects: 100% (50/50), done.\u001b[K\n",
            "remote: Total 52 (delta 19), reused 0 (delta 0)\u001b[K\n",
            "Unpacking objects: 100% (52/52), done.\n",
            "Cloning into 'gpt2'...\n",
            "remote: Enumerating objects: 56, done.\u001b[K\n",
            "remote: Counting objects: 100% (56/56), done.\u001b[K\n",
            "remote: Compressing objects: 100% (55/55), done.\u001b[K\n",
            "remote: Total 56 (delta 20), reused 0 (delta 0)\u001b[K\n",
            "Unpacking objects: 100% (56/56), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qanyDVKk1Rs4",
        "outputId": "a93b2ce5-a762-4278-d9f2-fbdba87f5b60"
      },
      "source": [
        "\n",
        "BERT_pretrained_weights = 'distilbert-base-uncased' \n",
        "\n",
        "\n",
        "BERTforTokenClassification_config_directory = 'bert-base-cased' \n",
        "token_label_files = 'bert-base-cased/tokenizer.json' \n",
        "\n",
        "GPT2_config_directory = 'gpt2'\n",
        "\n",
        "print('which BERT pre-trained ? ',BERT_pretrained_weights)\n",
        "print('where is BERT token classifier dir ? ',BERTforTokenClassification_config_directory)\n",
        "print('where is GPT2 dir ? ',GPT2_config_directory)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "which BERT pre-trained ?  distilbert-base-uncased\n",
            "where is BERT token classifier dir ?  bert-base-cased\n",
            "where is GPT2 dir ?  gpt2\n"
          ],
          "name": "stdout"
        }
      ]
    },
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        "id": "C0J9HKfn1n48",
        "outputId": "ead951f8-ba30-4efe-9c25-04f96472c961"
      },
      "source": [
        "print('----loading pre-trained BERT----')\n",
        "BERT_pretrained = DistilBertModel.from_pretrained(BERT_pretrained_weights)\n",
        "tokenizer_pretrained = DistilBertTokenizer.from_pretrained(BERT_pretrained_weights)\n",
        "print('----loading token labels----')\n",
        "with open(token_label_files, 'r') as fp:\n",
        "    POS2idx = json.load(fp)\n",
        "\n",
        "POS_values = list(POS2idx.keys())\n",
        "print('----loading BERT token classifier----')\n",
        "BERT_token_classifier = BertForTokenClassification.from_pretrained(\n",
        "                      BERTforTokenClassification_config_directory)\n",
        "\n",
        "tokenizer_token_classifier = BertTokenizer.from_pretrained(\n",
        "                      BERTforTokenClassification_config_directory)\n",
        "\n",
        "print('----loading GPT2 summary generator----')\n",
        "tokenizer_GPT2 = GPT2Tokenizer.from_pretrained(\n",
        "                  GPT2_config_directory)\n",
        "special_tokens = {'bos_token':'<|startoftext|>','eos_token':'<|endoftext|>','pad_token':'<pad>','additional_special_tokens':['<|keyword|>','<|summarize|>']}\n",
        "tokenizer_GPT2.add_special_tokens(special_tokens)\n",
        "GPT2_generator = GPT2DoubleHeadsModel.from_pretrained(\n",
        "                  GPT2_config_directory)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----loading pre-trained BERT----\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=442.0, style=ProgressStyle(description_…"
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=267967963.0, style=ProgressStyle(descri…"
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3dfaaf78be0a4cc396a5b256ba19b736",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "----loading token labels----\n",
            "----loading BERT token classifier----\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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        {
          "output_type": "stream",
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            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "----loading GPT2 summary generator----\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          ],
          "name": "stdout"
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          ],
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            "\n"
          ],
          "name": "stdout"
        },
        {
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        {
          "output_type": "stream",
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            "\n"
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aff_t8gUlM03"
      },
      "source": [
        "import os"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fondu_aEjMKo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "outputId": "da3ae6bb-a52f-49ba-a50e-209d09968843"
      },
      "source": [
        "max_len =  512\n",
        "i=0\n",
        "path = \"/content/drive/MyDrive/lstm-input\"\n",
        "path1=\"/content/drive/MyDrive/lstm-summ\"\n",
        "files = os.listdir(path)\n",
        "files=sorted(files)\n",
        "files2=os.listdir(path1)\n",
        "files2=sorted(files2)\n",
        "print(files)\n",
        "print(files2)\n",
        "d=0;\n",
        "for (f1,f2) in zip(files,files2):\n",
        "  if d==10:\n",
        "    break\n",
        "  d=d+1;\n",
        "  f1_score1=[]\n",
        "  precision1=[]\n",
        "  recall1=[]\n",
        "  f1_score2=[]\n",
        "  precision2=[]\n",
        "  recall2=[]\n",
        "  f1_scoreL=[]\n",
        "  precisionL=[]\n",
        "  recallL=[]\n",
        "  if f1.endswith(\".txt\") and f2.endswith(\".txt\"):\n",
        "    file_path = f\"{path}/{f1}\"\n",
        "    name1=str(f1)\n",
        "    #with open(file_path,'r+') as file:\n",
        "    #  file1 = file.read().replace('\\n', '')\n",
        "    #file1=open(file_path,'r+').replace('\\n',' ')\n",
        "    file1 = open(file_path, \"r+\")\n",
        "    text=file1.read()\n",
        "    file1.close()\n",
        "    file1=open(file_path, \"w\")\n",
        "    text2=''\n",
        "    if '\\n' in text:\n",
        "      text2 = text.replace('\\n' , ' ')\n",
        "      #print(text2)\n",
        "      file1.write(text2)\n",
        "    file1.truncate(160000)\n",
        "    file1.seek(0, os.SEEK_END)\n",
        "    print(\"Size of file1 is :\", file1.tell(), \"bytes\")\n",
        "    print(file1)\n",
        "    file1.close()\n",
        "\n",
        "\n",
        "    ##file2##\n",
        "    file_path = f\"{path1}/{f2}\"\n",
        "    name1=str(f2)\n",
        "    #file2=open(file_path,'r+').replace('\\n',' ')\n",
        "    file2 = open(file_path, \"r+\")\n",
        "    text=file2.read()\n",
        "    file2.close()\n",
        "    file2=open(file_path, \"w\")\n",
        "    text2=''\n",
        "    if '\\n' in text:\n",
        "      text2 = text.replace('\\n' , ' ')\n",
        "      #print(text2)\n",
        "      file2.write(text2)\n",
        "    file2.truncate(160000)\n",
        "    file2.seek(0, os.SEEK_END)\n",
        "    #print(\"Size of file2 is :\", file2.tell(), \"bytes\")\n",
        "    #print(file2)\n",
        "    file2.close()\n",
        "    #for line in file2:\n",
        "    #  print(line)\n",
        "    #input_text = file1\n",
        "    #print(len(input_text))\n",
        "    #reference_text=file2.read().replace('\\n',' ')\n",
        "    file1 = open(file_path, \"r+\")\n",
        "    text=file1.read()\n",
        "    file2 = open(file_path, \"r+\")\n",
        "    reference_text=file2.read()\n",
        "    #print(text)\n",
        "    paragraph_split =sent_tokenize(input_text)\n",
        "\n",
        "    print('input text has',len(paragraph_split) ,'sentences.')\n",
        "    file1.close()\n",
        "    print('tokenizing sentences')\n",
        "   \n",
        "    path = \"/content/drive/MyDrive\"\n",
        "    os.chdir(path)\n",
        "    input_tokens = []\n",
        "    for i in paragraph_split:\n",
        "      input_tokens.append(tokenizer_pretrained.encode(i, \n",
        "                              add_special_tokens=True,truncation=True))\n",
        "    temp = []\n",
        "    for i in input_tokens:\n",
        "     temp.append(len(i))\n",
        "    input_ids = pad_sequences(input_tokens, \n",
        "                          maxlen=max_len, dtype=\"long\", \n",
        "                          value=0, \n",
        "                          truncating=\"post\", \n",
        "                          padding=\"post\")\n",
        "\n",
        "    print('creating attention masks')\n",
        "\n",
        "    attention_masks = []\n",
        "    for sent in input_ids:\n",
        "      att_mask = [int(token_id > 0) for token_id in sent]  \n",
        "      attention_masks.append(att_mask)  \n",
        "\n",
        "    input_ids = torch.tensor(input_ids)  \n",
        "    attention_mask = torch.tensor(attention_masks)\n",
        "    number_extract = 2 \n",
        "    \n",
        "    input_ids = input_ids.to('cpu')\n",
        "    BERT_pretrained = BERT_pretrained.to('cpu')\n",
        "    lattention_mask = attention_mask.to('cpu')\n",
        "\n",
        "    with torch.no_grad():\n",
        "     last_hidden_states = BERT_pretrained(input_ids, \n",
        "                             attention_mask=attention_mask)\n",
        "\n",
        "    sentence_features = last_hidden_states[0][:,0,:].detach().cpu().numpy()\n",
        "\n",
        "    print('performing k-medoid clustering with '\n",
        "        ,number_extract,' clusters')\n",
        "\n",
        "    kmeans = KMeans(n_clusters=number_extract,random_state=0).fit(sentence_features)\n",
        "    cluster_center = kmeans.cluster_centers_\n",
        "    nbrs = NearestNeighbors(n_neighbors= 1,algorithm='brute').fit(sentence_features)\n",
        "    distances, indices = nbrs.kneighbors(cluster_center.reshape(number_extract,-1))\n",
        "\n",
        "    rows, cols = (number_extract, len(indices[0]))\n",
        "    ind =np.array([[0]*cols]*rows)\n",
        "    dis=-1*distances\n",
        "    xord = dis.argsort()\n",
        "    j=0\n",
        "    i=0  \n",
        "    for i in range(rows):\n",
        "     for j in range(cols):\n",
        "      ind[i][j]=indices[i][xord[i][j]]\n",
        "    topic_answer = []\n",
        "    j=0\n",
        "    ind=ind[ind[:, 0].argsort()]\n",
        "    print(ind)\n",
        "    while j!=number_extract:\n",
        "     i=ind[j][0]\n",
        "     topic_answer.append(paragraph_split[i])\n",
        "     j=j+1\n",
        "    if j==number_extract:\n",
        "      break\n",
        "\n",
        "\n",
        "    print('result:')\n",
        "\n",
        "    print('the ',number_extract,' extracted sentences are')\n",
        "    for i in topic_answer:\n",
        "     print(i)\n",
        "\n",
        "    topic_answer_string = ''\n",
        "    for topic in topic_answer:\n",
        "      topic_answer_string = topic_answer_string + ' '+ topic\n",
        "    scores = rouge.get_scores(topic_answer_string, reference_text)\n",
        "    scores_rouge1 = [score['rouge-1']['r'] for score in scores]\n",
        "    scores_rouge2 = [score['rouge-2']['r'] for score in scores]\n",
        "    scores_rougeL = [score['rouge-l']['r'] for score in scores]                 \n",
        "    recall1.append(scores_rouge1[0])\n",
        "    recall2.append(scores_rouge2[0])\n",
        "    recallL.append(scores_rougeL[0])\n",
        "    scores_rouge1 = [score['rouge-1']['p'] for score in scores]\n",
        "    scores_rouge2 = [score['rouge-2']['p'] for score in scores]\n",
        "    scores_rougeL = [score['rouge-l']['p'] for score in scores]                 \n",
        "    precision1.append(scores_rouge1[0])\n",
        "    precision2.append(scores_rouge2[0])\n",
        "    precisionL.append(scores_rougeL[0])\n",
        "    scores_rouge1 = [score['rouge-1']['f'] for score in scores]\n",
        "    scores_rouge2 = [score['rouge-2']['f'] for score in scores]\n",
        "    scores_rougeL = [score['rouge-l']['f'] for score in scores]                 \n",
        "    f1_score1.append(scores_rouge1[0])\n",
        "    f1_score2.append(scores_rouge2[0])\n",
        "    f1_scoreL.append(scores_rougeL[0])\n",
        "    completeName=os.path.join(\"/content/drive/MyDrive/output1\", str(name1))\n",
        "    file1 = open(completeName, \"w\")\n",
        "    file1.write(topic_answer_string)\n",
        "    file1.close()\n",
        "   \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        " "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['102.txt', '104.txt', '105.txt', '106.txt', '108.txt', '109.txt', '110.txt', '111.txt', '118.txt', '128.txt', '129.txt', '130.txt', '131.txt', '132.txt', '148.txt', '149.txt', '150.txt', '151.txt', '152.txt', '153.txt', '155.txt', '156.txt', '159.txt', '160.txt', '161.txt', '163.txt', '168.txt', '169.txt', '17.txt', '170.txt', '173.txt', '174.txt', '181.txt', '19.txt', '205.txt', '206.txt', '214.txt', '225.txt', '227.txt', '228.txt', '229.txt', '230.txt', '64.txt', '65.txt', '66.txt', '67.txt', '72.txt', '73.txt', '92.txt', '93.txt']\n",
            "['102.txt', '104.txt', '105.txt', '106.txt', '108.txt', '109.txt', '110.txt', '111.txt', '118.txt', '128.txt', '129.txt', '130.txt', '131.txt', '132.txt', '148.txt', '149.txt', '150.txt', '151.txt', '152.txt', '153.txt', '155.txt', '156.txt', '159.txt', '160.txt', '161.txt', '163.txt', '168.txt', '169.txt', '17.txt', '170.txt', '173.txt', '174.txt', '181.txt', '19.txt', '205.txt', '206.txt', '214.txt', '225.txt', '227.txt', '228.txt', '229.txt', '230.txt', '64.txt', '65.txt', '66.txt', '67.txt', '72.txt', '73.txt', '92.txt', '93.txt']\n",
            "Size of file1 is : 160000 bytes\n",
            "<_io.TextIOWrapper name='/content/drive/MyDrive/lstm-input/102.txt' mode='w' encoding='UTF-8'>\n",
            "input text has 1 sentences.\n",
            "tokenizing sentences\n",
            "creating attention masks\n",
            "performing k-medoid clustering with  2  clusters\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-43-271267e024de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    118\u001b[0m         ,number_extract,' clusters')\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m     \u001b[0mkmeans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKMeans\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumber_extract\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msentence_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m     \u001b[0mcluster_center\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkmeans\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcluster_centers_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m     \u001b[0mnbrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNearestNeighbors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_neighbors\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0malgorithm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'brute'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msentence_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/cluster/_kmeans.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    861\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_num_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    862\u001b[0m             raise ValueError(\"n_samples=%d should be >= n_clusters=%d\" % (\n\u001b[0;32m--> 863\u001b[0;31m                 _num_samples(X), self.n_clusters))\n\u001b[0m\u001b[1;32m    864\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    865\u001b[0m         \u001b[0mtol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_tolerance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: n_samples=1 should be >= n_clusters=2"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pt-fNuljyf7l"
      },
      "source": [
        " \"\"\"distances, indices = nbrs.kneighbors(cluster_center.reshape(number_extract,-1))\n",
        "    indices\n",
        "    topic_answer_string\n",
        "    print(os.path)\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}