PatentSBERTa_Example_For_Patent_Classification.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/AI-Growth-Lab/PatentSBERTa/blob/main/PatentSBERTa_Example_For_Patent_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets -q\n",
        "!pip install sentence_transformers -q"
      ],
      "metadata": {
        "id": "3hfsgcTr5GV6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28883da3-d916-44e5-c579-edf91a44a316"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 365 kB 4.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 212 kB 52.9 MB/s \n",
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            "\u001b[K     |████████████████████████████████| 120 kB 57.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 127 kB 63.6 MB/s \n",
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            "\u001b[K     |████████████████████████████████| 4.9 MB 29.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.3 MB 54.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 33.4 MB/s \n",
            "\u001b[?25h  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "import datasets\n",
        "\n",
        "import pandas as pd\n"
      ],
      "metadata": {
        "id": "12LX6FGT4Vvg"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = datasets.load_dataset(\"AI-Growth-Lab/patents_claims_1.5m_traim_test\", split=\"train[:100]\")\n",
        "test_dataset = datasets.load_dataset(\"AI-Growth-Lab/patents_claims_1.5m_traim_test\", split=\"test[:10]\")\n",
        "\n",
        "df_claim_cpc_test = pd.DataFrame(test_dataset)\n",
        "df_claim_cpc_train = pd.DataFrame(train_dataset)"
      ],
      "metadata": {
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      "execution_count": 3,
      "outputs": [
        {
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          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration AI-Growth-Lab--patents_claims_1.5m_traim_test-82d9dcde80f3b44f\n"
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          "text": [
            "Downloading and preparing dataset csv/AI-Growth-Lab--patents_claims_1.5m_traim_test to /root/.cache/huggingface/datasets/AI-Growth-Lab___csv/AI-Growth-Lab--patents_claims_1.5m_traim_test-82d9dcde80f3b44f/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a...\n"
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          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/AI-Growth-Lab___csv/AI-Growth-Lab--patents_claims_1.5m_traim_test-82d9dcde80f3b44f/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a. Subsequent calls will reuse this data.\n"
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          "text": [
            "WARNING:datasets.builder:Using custom data configuration AI-Growth-Lab--patents_claims_1.5m_traim_test-82d9dcde80f3b44f\n",
            "WARNING:datasets.builder:Reusing dataset csv (/root/.cache/huggingface/datasets/AI-Growth-Lab___csv/AI-Growth-Lab--patents_claims_1.5m_traim_test-82d9dcde80f3b44f/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a)\n"
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        "df_claim_cpc_test.head()"
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        "outputId": "e4d361c1-3cc4-4894-f71c-8ae8d67ff7fc"
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      "execution_count": 4,
      "outputs": [
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5f125202-cb5a-401d-a296-5364734dac51 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5f125202-cb5a-401d-a296-5364734dac51');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_claim_cpc_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gxPrWlq5GvNZ",
        "outputId": "f06133da-9583-49c6-b0c6-b0a8c84e13ba"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10, 666)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_claim_cpc_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xYuxr2jBGyz4",
        "outputId": "5bd839d7-66ce-4c61-cf55-cace0199986b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 666)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "model = SentenceTransformer('AI-Growth-Lab/PatentSBERTa')\n",
        "\n",
        "#Sentences are encoded by calling model.encode()\n",
        "test_embeddings = model.encode(df_claim_cpc_test.text, convert_to_tensor=True, show_progress_bar=True)\n",
        "claim_embeddings = model.encode(df_claim_cpc_train.text, convert_to_tensor=True, show_progress_bar=True)\n",
        "stored_patent_test_embeddings_id = df_claim_cpc_test['id']\n",
        "stored_patent_train_embeddings_id = df_claim_cpc_train['id']\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552,
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        "id": "4SBNKt7JICWH",
        "outputId": "98cefa60-a108-4387-9d36-4935ddf6041e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moving 0 files to the new cache system\n"
          ]
        },
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      "cell_type": "code",
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        "id": "vX_5VVlY38Qo",
        "outputId": "a4b5d016-c876-4d21-ceda-4c6e8a9ec42e",
        "colab": {
          "base_uri": "https://localhost:8080/"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Runtime of the program is 0.4439220428466797\n",
            "F1Measure:  0.0\n",
            "Recall:  0.0\n",
            "Accuracy:  0.0\n",
            "Precision:  0.0\n",
            "Hamming_Loss:  0.0022727272727272726\n",
            "Runtime of the program is 0.7478082180023193\n",
            "F1Measure:  0.0\n",
            "Recall:  0.0\n",
            "Accuracy:  0.0\n",
            "Precision:  0.0\n",
            "Hamming_Loss:  0.0022727272727272726\n",
            "Runtime of the program is 1.1712114810943604\n",
            "F1Measure:  0.0\n",
            "Recall:  0.0\n",
            "Accuracy:  0.0\n",
            "Precision:  0.0\n",
            "Hamming_Loss:  0.0022727272727272726\n",
            "Runtime of the program is 1.7298846244812012\n",
            "F1Measure:  0.1\n",
            "Recall:  0.1\n",
            "Accuracy:  0.1\n",
            "Precision:  0.1\n",
            "Hamming_Loss:  0.0021212121212121214\n",
            "Runtime of the program is 2.5483720302581787\n",
            "F1Measure:  0.1\n",
            "Recall:  0.1\n",
            "Accuracy:  0.1\n",
            "Precision:  0.1\n",
            "Hamming_Loss:  0.0022727272727272726\n",
            "Runtime of the program is 3.3747544288635254\n",
            "F1Measure:  0.1\n",
            "Recall:  0.1\n",
            "Accuracy:  0.1\n",
            "Precision:  0.1\n",
            "Hamming_Loss:  0.0024242424242424242\n",
            "Runtime of the program is 4.466465711593628\n",
            "F1Measure:  0.26666666666666666\n",
            "Recall:  0.26666666666666666\n",
            "Accuracy:  0.25\n",
            "Precision:  0.26666666666666666\n",
            "Hamming_Loss:  0.00196969696969697\n",
            "Runtime of the program is 5.678404331207275\n",
            "F1Measure:  0.26666666666666666\n",
            "Recall:  0.26666666666666666\n",
            "Accuracy:  0.25\n",
            "Precision:  0.26666666666666666\n",
            "Hamming_Loss:  0.0024242424242424242\n",
            "Runtime of the program is 7.02397894859314\n",
            "F1Measure:  0.2333333333333333\n",
            "Recall:  0.21666666666666665\n",
            "Accuracy:  0.2\n",
            "Precision:  0.26666666666666666\n",
            "Hamming_Loss:  0.002575757575757576\n",
            "Runtime of the program is 8.367991924285889\n",
            "F1Measure:  0.2333333333333333\n",
            "Recall:  0.21666666666666665\n",
            "Accuracy:  0.2\n",
            "Precision:  0.26666666666666666\n",
            "Hamming_Loss:  0.002575757575757576\n",
            "Runtime of the program is 9.962693214416504\n",
            "F1Measure:  0.2333333333333333\n",
            "Recall:  0.21666666666666665\n",
            "Accuracy:  0.2\n",
            "Precision:  0.26666666666666666\n",
            "Hamming_Loss:  0.0027272727272727275\n",
            "Runtime of the program is 11.692523717880249\n",
            "F1Measure:  0.2333333333333333\n",
            "Recall:  0.21666666666666665\n",
            "Accuracy:  0.2\n",
            "Precision:  0.26666666666666666\n",
            "Hamming_Loss:  0.003181818181818182\n",
            "Runtime of the program is 13.561903238296509\n",
            "F1Measure:  0.2333333333333333\n",
            "Recall:  0.21666666666666665\n",
            "Accuracy:  0.2\n",
            "Precision:  0.26666666666666666\n",
            "Hamming_Loss:  0.0034848484848484847\n",
            "Runtime of the program is 15.5503249168396\n",
            "F1Measure:  0.2333333333333333\n",
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            "Accuracy:  0.2\n",
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            "Runtime of the program is 17.689054489135742\n",
            "F1Measure:  0.2\n",
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            "Accuracy:  0.15\n",
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            "Runtime of the program is 20.059929847717285\n",
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            "Accuracy:  0.15\n",
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            "Runtime of the program is 22.552921295166016\n",
            "F1Measure:  0.16666666666666666\n",
            "Recall:  0.13333333333333333\n",
            "Accuracy:  0.11666666666666665\n",
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            "Runtime of the program is 25.078810930252075\n",
            "F1Measure:  0.16666666666666666\n",
            "Recall:  0.13333333333333333\n",
            "Accuracy:  0.11666666666666665\n",
            "Precision:  0.26666666666666666\n",
            "Hamming_Loss:  0.004393939393939394\n",
            "Runtime of the program is 27.739117860794067\n",
            "F1Measure:  0.16666666666666666\n",
            "Recall:  0.13333333333333333\n",
            "Accuracy:  0.11666666666666665\n",
            "Precision:  0.26666666666666666\n",
            "Hamming_Loss:  0.004545454545454545\n",
            "Runtime of the program is 30.63825225830078\n",
            "F1Measure:  0.15714285714285714\n",
            "Recall:  0.11666666666666665\n",
            "Accuracy:  0.10666666666666666\n",
            "Precision:  0.26666666666666666\n",
            "Hamming_Loss:  0.005303030303030303\n"
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      "source": [
        "# get_ipython().system('pip install sentence_transformers')\n",
        "import scipy.spatial\n",
        "import numpy as np\n",
        "import os, json\n",
        "import glob\n",
        "import re\n",
        "import torch\n",
        "import pandas as pd\n",
        "from sentence_transformers import SentenceTransformer, util\n",
        "from tokenizers import Tokenizer\n",
        "from datetime import datetime\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "\n",
        "torch.manual_seed(1)\n",
        "import pandas as pd\n",
        "import torch\n",
        "import random\n",
        "import itertools\n",
        "import pickle\n",
        "import time\n",
        "\n",
        "start = time.time()\n",
        "\n",
        "F1Measure_list = []\n",
        "Recall_list = []\n",
        "Accuracy_list = []\n",
        "Precision_list = []\n",
        "Hamming_Loss_list = []\n",
        "\n",
        "\n",
        "def get_top_n_similar_patents_df(new_claim, claim_embeddings):\n",
        "    search_hits_list = []\n",
        "    search_hits = util.semantic_search(new_claim, claim_embeddings, 10000, 5000000, 20)\n",
        "    top_claim_order = []\n",
        "    top_claim_ids = []\n",
        "    top_similarity_scores = []\n",
        "    for item in range(len(search_hits[0])):\n",
        "        top_claim_order = search_hits[0][item].get('corpus_id')\n",
        "        top_claim_ids.append(stored_patent_train_embeddings_id[top_claim_order])\n",
        "        top_similarity_scores.append(search_hits[0][item].get('score'))\n",
        "        \n",
        "    top_100_similar_patents_df = pd.DataFrame({\n",
        "        'top_claim_ids': top_claim_ids,\n",
        "        'cosine_similarity': top_similarity_scores,\n",
        "    })\n",
        "\n",
        "        \n",
        "    \n",
        "    return top_100_similar_patents_df\n",
        "\n",
        "def F1Measure(y_true, y_pred):\n",
        "    save_F1 = []\n",
        "    temp = 0\n",
        "    for i in range(y_true.shape[0]):\n",
        "        if (sum(y_true[i]) == 0) and (sum(y_pred[i]) == 0):\n",
        "            continue\n",
        "        temp_save = (2*sum(np.logical_and(y_true[i], y_pred[i])))/ (sum(y_true[i])+sum(y_pred[i]))\n",
        "        save_F1.append(temp_save)\n",
        "        temp += temp_save\n",
        "\n",
        "    save_F1 = pd.DataFrame(save_F1)\n",
        "    save_F1_ids = pd.concat([result, save_F1], axis=1, ignore_index=True)\n",
        "    return temp/ y_true.shape[0]\n",
        "\n",
        "def Recall(y_true, y_pred):\n",
        "    temp = 0\n",
        "    for i in range(y_true.shape[0]):\n",
        "        if sum(y_pred[i]) == 0:\n",
        "            continue\n",
        "        temp+= sum(np.logical_and(y_true[i], y_pred[i]))/ sum(y_pred[i])\n",
        "    return temp/ y_true.shape[0]\n",
        "\n",
        "def Precision(y_true, y_pred):\n",
        "    temp = 0\n",
        "    for i in range(y_true.shape[0]):\n",
        "        if sum(y_true[i]) == 0:\n",
        "            continue\n",
        "        temp+= sum(np.logical_and(y_true[i], y_pred[i]))/ sum(y_true[i])\n",
        "    return temp/ y_true.shape[0]\n",
        "\n",
        "def Hamming_Loss(y_true, y_pred):\n",
        "    temp=0\n",
        "    for i in range(y_true.shape[0]):\n",
        "        temp += np.size(y_true[i] == y_pred[i]) - np.count_nonzero(y_true[i] == y_pred[i])\n",
        "    return temp/(y_true.shape[0] * y_true.shape[1])\n",
        "\n",
        "def Accuracy(y_true, y_pred):\n",
        "    temp = 0\n",
        "    for i in range(y_true.shape[0]):\n",
        "        temp += sum(np.logical_and(y_true[i], y_pred[i])) / sum(np.logical_or(y_true[i], y_pred[i]))\n",
        "    return temp / y_true.shape[0]\n",
        "\n",
        "\n",
        "\n",
        "claims = list(df_claim_cpc_train.text)\n",
        "patent_id = list(df_claim_cpc_train.id)\n",
        "\n",
        "listofpredictdfs = []\n",
        "\n",
        "start = time.time()\n",
        "\n",
        "for i in range(len(df_claim_cpc_test[:100])):\n",
        "    get_top_n_similar_patents_df_predict = get_top_n_similar_patents_df(np.array(test_embeddings[i]).reshape(1,-1), claim_embeddings)\n",
        "    result = pd.merge(get_top_n_similar_patents_df_predict, df_claim_cpc_train, left_on='top_claim_ids',right_on='id',how='left',suffixes=('_left','_right'))\n",
        "    locals()[\"predict_n\"+str(i)] = result.copy()\n",
        "    listofpredictdfs.append(\"predict_n\"+str(i))\n",
        "\n",
        "df = pd.concat(map(lambda x: eval(x), listofpredictdfs),keys= listofpredictdfs ,axis=0)\n",
        "\n",
        "top_k = 20\n",
        "for k in range(top_k):\n",
        "    top_n = k\n",
        "    predict = pd.DataFrame(columns= df_claim_cpc_test.columns[6:])\n",
        "    for item in range(len(listofpredictdfs)):\n",
        "        k_similar_patents = df.xs(listofpredictdfs[item]).nlargest(top_n, ['cosine_similarity'])\n",
        "        result_k_similar_patents = pd.DataFrame(0, index=np.arange(1),columns= k_similar_patents.columns[8:])\n",
        "        for i in range(top_n):\n",
        "            result_k_similar_patents  = result_k_similar_patents + k_similar_patents.iloc[i, 8:].values\n",
        "        result_k_similar_patents_df = pd.DataFrame(result_k_similar_patents, columns= k_similar_patents.columns[8:])\n",
        "        result_k_similar_patents_df.insert(0, \"input_aptent_id\", df_claim_cpc_test.id.iloc[item], True)\n",
        "        locals()[\"predict\"+str(item)] = result_k_similar_patents_df.copy()\n",
        "        predict = pd.concat([predict, locals()[\"predict\"+str(item)]], ignore_index=True)\n",
        "        result_k_similar_patents_df = result_k_similar_patents_df[0:0]\n",
        "    data = torch.tensor((predict.to_numpy()).astype(float), dtype=torch.float32)\n",
        "    m = nn.Sigmoid()\n",
        "    output = m(data)\n",
        "    output = (output>0.9).float()\n",
        "    output_df = pd.DataFrame(output, columns=predict.columns).astype(float) \n",
        "    y_pred = output_df.iloc[:, :-1].to_numpy()\n",
        "    y_true = df_claim_cpc_test.iloc[:100, 6:].to_numpy()\n",
        "    result = pd.concat([output_df, df_claim_cpc_test], axis=1, ignore_index=True)    \n",
        "    F1Measure_list.append(F1Measure(y_true,y_pred))\n",
        "    Recall_list.append(Recall(y_true,y_pred))\n",
        "    Accuracy_list.append(Accuracy(y_true, y_pred))\n",
        "    Precision_list.append(Precision(y_true,y_pred))\n",
        "    Hamming_Loss_list.append(Hamming_Loss(y_true, y_pred))\n",
        "    end = time.time()\n",
        "    print(f\"Runtime of the program is {end - start}\")\n",
        "    print(\"F1Measure: \", F1Measure_list[top_n])\n",
        "    print(\"Recall: \", Recall_list[top_n])\n",
        "    print(\"Accuracy: \", Accuracy_list[top_n])\n",
        "    print(\"Precision: \", Precision_list[top_n])\n",
        "    print(\"Hamming_Loss: \", Hamming_Loss_list[top_n])\n",
        "    \n",
        "output_d_metrics = {'F1Measure':F1Measure_list,'Recall_list':Recall_list, 'Accuracy_list':Accuracy_list,'Precision_list':Precision_list,'Hamming_Loss_list':Hamming_Loss_list}\n",
        "output_df_metrics = pd.DataFrame(output_d_metrics)\n"
      ]
    },
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