Text_clustering_arxiv.ipynb

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        "<a href=\"https://colab.research.google.com/github/1234tirm/Hello-world/blob/main/Text_clustering_arxiv.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        {
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            "Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
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            "\u001b[?25hDownloading pynndescent-0.5.13-py3-none-any.whl (56 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.9/56.9 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading pyct-0.5.0-py2.py3-none-any.whl (15 kB)\n",
            "Building wheels for collected packages: Pyqtree\n",
            "  Building wheel for Pyqtree (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for Pyqtree: filename=Pyqtree-1.0.0-py3-none-any.whl size=5967 sha256=e6905201444467362ca84aa860f8e131f764db02ebda941a206816bb79ed3351\n",
            "  Stored in directory: /root/.cache/pip/wheels/63/e6/90/6e15bfb4299fd41f88a9affca879f44bde40d3dc6f398462a8\n",
            "Successfully built Pyqtree\n",
            "Installing collected packages: rjsmin, rcssmin, Pyqtree, xxhash, pyct, fsspec, dill, colorspacious, multiprocess, pynndescent, pylabeladjust, hdbscan, umap-learn, datashader, datasets, datamapplot, bertopic\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2024.10.0\n",
            "    Uninstalling fsspec-2024.10.0:\n",
            "      Successfully uninstalled fsspec-2024.10.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed Pyqtree-1.0.0 bertopic-0.16.4 colorspacious-1.1.2 datamapplot-0.4.2 datasets-3.1.0 datashader-0.16.3 dill-0.3.8 fsspec-2024.9.0 hdbscan-0.8.40 multiprocess-0.70.16 pyct-0.5.0 pylabeladjust-0.1.13 pynndescent-0.5.13 rcssmin-1.2.0 rjsmin-1.2.3 umap-learn-0.5.7 xxhash-3.5.0\n"
          ]
        }
      ],
      "source": [
        "!pip install bertopic datasets openai datamapplot"
      ]
    },
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        },
        "id": "NDA9OnXCTC5X",
        "outputId": "b711c92d-11f8-48ef-e73a-b59e4c24f0be"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
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            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
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        {
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              "version_minor": 0
            },
            "text/plain": [
              "data.csv:   0%|          | 0.00/53.2M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split:   0%|          | 0/44949 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Load data from huggingface\n",
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"MaartenGr/arxiv_nlp\")[\"train\"]\n",
        "\n",
        "# Extract metadata\n",
        "abstracts = dataset[\"Abstracts\"]\n",
        "title = dataset[\"Titles\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rYCwcm8LWR5I"
      },
      "source": [
        "# Embedding Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "checking\n"
      ]
    },
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      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "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"
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              "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "236ed76ac1bf479fa427eec19e720cd7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "93258e42521b47428a0bd0406215e031",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Batches:   0%|          | 0/1405 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "# Create an embedding for each abstract\n",
        "embedding_model = SentenceTransformer(\"thenlper/gte-small\")\n",
        "embeddings = embedding_model.encode(abstracts, show_progress_bar= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tKApBjeT3AII",
        "outputId": "f9eff09a-cfb4-49ab-ab95-af1b2c2bbf4a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(44949, 384)"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "embeddings.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3LHGeoU13Zxm"
      },
      "source": [
        "# Reducing the dimensionality"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SvrJ-gbf3gAC",
        "outputId": "7774dfb3-c5b8-4073-a6d4-b7c594c9d9b1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(\n"
          ]
        }
      ],
      "source": [
        "from umap import UMAP\n",
        "\n",
        "# we reduce the input embeddings dimensionality from 384 to 5\n",
        "\n",
        "umap_model = UMAP(n_components=5, min_dist= 0.0, metric= 'cosine', random_state = 42)\n",
        "reduced_embeddings = umap_model.fit_transform(embeddings)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ol2hvuQC38eH"
      },
      "source": [
        "# Cluster the Reduced Embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ntJwf5DO37xc",
        "outputId": "e44f0609-2c0b-4273-edc8-66ddec643772"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "153\n"
          ]
        }
      ],
      "source": [
        "from hdbscan import HDBSCAN\n",
        "\n",
        "# We fit the model and extract the clusters\n",
        "hdbscan_model = HDBSCAN(\n",
        "    min_cluster_size= 50, metric='euclidean', cluster_selection_method= 'eom'\n",
        ").fit(reduced_embeddings)\n",
        "clusters = hdbscan_model.labels_\n",
        "\n",
        "# number of clusters\n",
        "print(len(set(clusters)))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        },
        "id": "S71IBdHy_p5K",
        "outputId": "1ddd1ed7-29a2-48f4-84b4-3ce56a47b838"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[(array([  313,   319,   337,   339,   518,   768,   769,   776,   777,\n",
            "        1073,  1741,  2437,  2534,  2996,  3991,  4460,  7471, 11676,\n",
            "       11715, 12740, 13159, 13631, 14095, 14174, 14175, 16966, 19402,\n",
            "       19723, 19807, 22548, 22641, 22865, 23257, 24030, 24392, 24609,\n",
            "       24651, 25045, 25231, 25750, 25814, 25859, 26436, 26770, 26891,\n",
            "       27653, 27680, 28969, 29035, 29085, 29106, 29601, 29900, 30532,\n",
            "       30637, 31628, 31910, 32524, 33002, 33162, 33400, 33752, 34055,\n",
            "       35105, 36132, 36160, 36365, 36499, 36522, 37072, 37606, 38061,\n",
            "       38107, 38324, 38325, 38327, 38576, 38599, 39315, 40717, 41447,\n",
            "       41661, 42088, 43092, 43116, 43198]),)]\n",
            "(array([  313,   319,   337,   339,   518,   768,   769,   776,   777,\n",
            "        1073,  1741,  2437,  2534,  2996,  3991,  4460,  7471, 11676,\n",
            "       11715, 12740, 13159, 13631, 14095, 14174, 14175, 16966, 19402,\n",
            "       19723, 19807, 22548, 22641, 22865, 23257, 24030, 24392, 24609,\n",
            "       24651, 25045, 25231, 25750, 25814, 25859, 26436, 26770, 26891,\n",
            "       27653, 27680, 28969, 29035, 29085, 29106, 29601, 29900, 30532,\n",
            "       30637, 31628, 31910, 32524, 33002, 33162, 33400, 33752, 34055,\n",
            "       35105, 36132, 36160, 36365, 36499, 36522, 37072, 37606, 38061,\n",
            "       38107, 38324, 38325, 38327, 38576, 38599, 39315, 40717, 41447,\n",
            "       41661, 42088, 43092, 43116, 43198]),)\n",
            "[  313   319   337   339   518   768   769   776   777  1073  1741  2437\n",
            "  2534  2996  3991  4460  7471 11676 11715 12740 13159 13631 14095 14174\n",
            " 14175 16966 19402 19723 19807 22548 22641 22865 23257 24030 24392 24609\n",
            " 24651 25045 25231 25750 25814 25859 26436 26770 26891 27653 27680 28969\n",
            " 29035 29085 29106 29601 29900 30532 30637 31628 31910 32524 33002 33162\n",
            " 33400 33752 34055 35105 36132 36160 36365 36499 36522 37072 37606 38061\n",
            " 38107 38324 38325 38327 38576 38599 39315 40717 41447 41661 42088 43092\n",
            " 43116 43198]\n",
            "1\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<>:8: SyntaxWarning: 'int' object is not subscriptable; perhaps you missed a comma?\n",
            "<>:8: SyntaxWarning: 'int' object is not subscriptable; perhaps you missed a comma?\n",
            "<ipython-input-20-d0bc8ae413ef>:8: SyntaxWarning: 'int' object is not subscriptable; perhaps you missed a comma?\n",
            "  print(len(y[0[0]]))\n"
          ]
        },
        {
          "ename": "TypeError",
          "evalue": "'int' object is not subscriptable",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-d0bc8ae413ef>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'int' object is not subscriptable"
          ]
        }
      ],
      "source": [
        "y=[]\n",
        "cluster = 0\n",
        "y.append(np.where(clusters==cluster))\n",
        "print(y)\n",
        "print(y[0])\n",
        "print(y[0][0])\n",
        "print(len(y))\n",
        "print(len(y[0[0]]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEAKkA5i4vrK"
      },
      "source": [
        "# Inspeting the clusters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XPq0sd9F4cMl",
        "outputId": "e3aa7703-9308-4131-9994-152fbe767109"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  This works aims to design a statistical machine translation from English text\n",
            "to American Sign Language (ASL). The system is based on Moses tool with some\n",
            "modifications and the results are synthesized through a 3D avatar for\n",
            "interpretation. First, we translate the input text to gloss, a written fo....\n",
            "\n",
            "  Researches on signed languages still strongly dissociate lin- guistic issues\n",
            "related on phonological and phonetic aspects, and gesture studies for\n",
            "recognition and synthesis purposes. This paper focuses on the imbrication of\n",
            "motion and meaning for the analysis, synthesis and evaluation of sign lang....\n",
            "\n",
            "  Modern computational linguistic software cannot produce important aspects of\n",
            "sign language translation. Using some researches we deduce that the majority of\n",
            "automatic sign language translation systems ignore many aspects when they\n",
            "generate animation; therefore the interpretation lost the truth inf....\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "# Print the first three documents in cluster 0\n",
        "cluster = 0\n",
        "for index in np.where(clusters==cluster)[0][:3]:    # np.where returns the indices of the array that satisfy certain condition\n",
        "  print(abstracts[index][:300] + \"....\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7HdOUPB45Nnc"
      },
      "source": [
        "Next, we reduce our embeddings to 2 dimensions so that we can plot them and get a rough understanding of the generated clusters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5uEX5pSd5w8D",
        "outputId": "321149f6-353c-40f7-b17a-bc5d981237c7"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Reduce 384-dimensional embeddings to 2 dimensions  for easier visualization\n",
        "reduced_embeddings = UMAP(n_components= 2, min_dist = 0.0, metric = 'cosine', random_state=43).fit_transform(embeddings)\n",
        "\n",
        "# Create dataframe\n",
        "df = pd.DataFrame(reduced_embeddings, columns = [\"x\", \"y\"])\n",
        "df[\"title\"] = title\n",
        "df[\"cluster\"] = [str(c) for c in clusters]\n",
        "\n",
        "# Select outliers and non-outliers (clusters)\n",
        "clusters_df = df.loc[df.cluster != \"-1\", :]\n",
        "outliers_df = df.loc[df.cluster == \"-1\", :]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "obUQi1ii-WJv",
        "outputId": "90e074c8-53fe-4fe0-c974-0cc3f3272bae"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 44949,\n  \"fields\": [\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 44925,\n        \"samples\": [\n          -2.715153694152832,\n          2.381049156188965,\n          -2.1673831939697266\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 44866,\n        \"samples\": [\n          8.699148178100586,\n          6.511498928070068,\n          10.78858470916748\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 44935,\n        \"samples\": [\n          \"Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and\\n  Layers\",\n          \"LAraBench: Benchmarking Arabic AI with Large Language Models\",\n          \"Survey in Characterization of Semantic Change\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cluster\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 153,\n        \"samples\": [\n          \"57\",\n          \"77\",\n          \"80\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
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              "        vertical-align: middle;\n",
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              "      <td>On the Development of Text Input Method - Less...</td>\n",
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              "      <th>3</th>\n",
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              "      <td>8.711252</td>\n",
              "      <td>Network statistics on early English Syntax: St...</td>\n",
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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-eb03dd83-3856-422f-b3ad-d10dc8fe374b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          x          y                                              title  \\\n",
              "0  4.653550  12.112965  Introduction to Arabic Speech Recognition Usin...   \n",
              "1  4.632953  12.116715  Arabic Speech Recognition System using CMU-Sph...   \n",
              "2  4.026181  11.799395  On the Development of Text Input Method - Less...   \n",
              "3  3.274959   8.711252  Network statistics on early English Syntax: St...   \n",
              "4  3.816295   7.216149  Segmentation and Context of Literary and Music...   \n",
              "\n",
              "  cluster  \n",
              "0      -1  \n",
              "1     126  \n",
              "2      -1  \n",
              "3     133  \n",
              "4      16  "
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XACHrP137IV1"
      },
      "source": [
        "# Static plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "8IAr6MNP7KyF",
        "outputId": "bd266468-6cd8-4964-dc10-58d64f0685ec"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(-8.854348158836364,\n",
              " 11.825715517997741,\n",
              " -1.3012729734182358,\n",
              " 16.896038219332695)"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot outliers and non-outliers separately\n",
        "plt.scatter(\n",
        "    clusters_df.x, clusters_df.y, c=clusters_df.cluster.astype(int),\n",
        "    alpha=0.6, s=2, cmap= 'tab20b'\n",
        ")\n",
        "plt.axis('off')\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "authorship_tag": "ABX9TyPZ95Ut+53PFNRD0sb8cglC",
      "gpuType": "T4",
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
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