CS6220_LiveFAQ_Colab_SentTransformerModel.ipynb

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    {
      "cell_type": "markdown",
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      "metadata": {
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      "source": [
        "## Packages"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U sentence-transformers"
      ],
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
            "\u001b[K     |████████████████████████████████| 85 kB 3.4 MB/s \n",
            "\u001b[?25hCollecting transformers<5.0.0,>=4.6.0\n",
            "  Downloading transformers-4.24.0-py3-none-any.whl (5.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 5.5 MB 16.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (4.64.1)\n",
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            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.97-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.3 MB 53.0 MB/s \n",
            "\u001b[?25hCollecting huggingface-hub>=0.4.0\n",
            "  Downloading huggingface_hub-0.10.1-py3-none-any.whl (163 kB)\n",
            "\u001b[K     |████████████████████████████████| 163 kB 63.9 MB/s \n",
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            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2022.6.2)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 7.6 MB 41.2 MB/s \n",
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            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->sentence-transformers) (7.1.2)\n",
            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125938 sha256=4116d63a2cfac56f6a37ab52959709138663a5be79b37a351965ee63fe0ad6e9\n",
            "  Stored in directory: /root/.cache/pip/wheels/bf/06/fb/d59c1e5bd1dac7f6cf61ec0036cc3a10ab8fecaa6b2c3d3ee9\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers, sentencepiece, sentence-transformers\n",
            "Successfully installed huggingface-hub-0.10.1 sentence-transformers-2.2.2 sentencepiece-0.1.97 tokenizers-0.13.1 transformers-4.24.0\n"
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      "execution_count": 4,
      "id": "ea5aaf08",
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        "id": "ea5aaf08"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "import matplotlib.pyplot as plt\n",
        "# %use matplotlib inline\n",
        "\n",
        "from sklearn.cluster import DBSCAN, KMeans\n",
        "from sklearn import metrics\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7fcc912e",
      "metadata": {
        "id": "7fcc912e"
      },
      "source": [
        "## Data Generation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "6bf25ca2",
      "metadata": {
        "id": "6bf25ca2"
      },
      "outputs": [],
      "source": [
        "column_names = [\"question\", \"label_info\", \"source\"]"
      ]
    },
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      "execution_count": 7,
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              "                                            question   label_info  \\\n",
              "0  How did serfdom develop in and then leave Russ...  DESC:manner   \n",
              "1   What films featured the character Popeye Doyle ?  ENTY:cremat   \n",
              "2  How can I find a list of celebrities ' real na...  DESC:manner   \n",
              "3  What fowl grabs the spotlight after the Chines...  ENTY:animal   \n",
              "4                    What is the full form of .com ?     ABBR:exp   \n",
              "\n",
              "                            source  \n",
              "0  experiemntal_qna_classification  \n",
              "1  experiemntal_qna_classification  \n",
              "2  experiemntal_qna_classification  \n",
              "3  experiemntal_qna_classification  \n",
              "4  experiemntal_qna_classification  "
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              "      <th>0</th>\n",
              "      <td>How did serfdom develop in and then leave Russ...</td>\n",
              "      <td>DESC:manner</td>\n",
              "      <td>experiemntal_qna_classification</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>What films featured the character Popeye Doyle ?</td>\n",
              "      <td>ENTY:cremat</td>\n",
              "      <td>experiemntal_qna_classification</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>How can I find a list of celebrities ' real na...</td>\n",
              "      <td>DESC:manner</td>\n",
              "      <td>experiemntal_qna_classification</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>What fowl grabs the spotlight after the Chines...</td>\n",
              "      <td>ENTY:animal</td>\n",
              "      <td>experiemntal_qna_classification</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>What is the full form of .com ?</td>\n",
              "      <td>ABBR:exp</td>\n",
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        "exp_data = pd.read_csv(\"Experimental Data for Question Classification train_3000.label.txt\", \\\n",
        "                       sep='\\t', \\\n",
        "                       encoding = \"ISO-8859-1\", \\\n",
        "                       header=None)  \n",
        "exp_data = exp_data[0].str.split(\" \", 1, expand=True)\n",
        "exp_data = exp_data[[1, 0]]\n",
        "exp_data[\"source\"] = \"experiemntal_qna_classification\"\n",
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        "exp_data.head()"
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              "                                            question   label_info  \\\n",
              "0  How did serfdom develop in and then leave Russ...  DESC:manner   \n",
              "1   What films featured the character Popeye Doyle ?  ENTY:cremat   \n",
              "2  How can I find a list of celebrities ' real na...  DESC:manner   \n",
              "3  What fowl grabs the spotlight after the Chines...  ENTY:animal   \n",
              "4                    What is the full form of .com ?     ABBR:exp   \n",
              "\n",
              "                            source  \n",
              "0  experiemntal_qna_classification  \n",
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              "      <td>What films featured the character Popeye Doyle ?</td>\n",
              "      <td>ENTY:cremat</td>\n",
              "      <td>experiemntal_qna_classification</td>\n",
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              "    }\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-bb3cd18d-2804-4ca2-8b08-716ef9346026 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-bb3cd18d-2804-4ca2-8b08-716ef9346026');\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": 8
        }
      ],
      "source": [
        "final_data = exp_data\n",
        "final_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "854ccad9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "854ccad9",
        "outputId": "7155e1f9-fe1c-43ee-9246-64a8ee63b558"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3000 entries, 0 to 2999\n",
            "Data columns (total 3 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   question    3000 non-null   object\n",
            " 1   label_info  3000 non-null   object\n",
            " 2   source      3000 non-null   object\n",
            "dtypes: object(3)\n",
            "memory usage: 70.4+ KB\n"
          ]
        }
      ],
      "source": [
        "final_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "f56374b4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f56374b4",
        "outputId": "ae69f713-e336-49f2-8813-6d969e8441a4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6\n",
            "['ABBR' 'DESC' 'ENTY' 'HUM' 'LOC' 'NUM']\n"
          ]
        }
      ],
      "source": [
        "print(len(np.unique(final_data[\"label_info\"].str.split(\":\", expand=True)[0].to_numpy())))\n",
        "print(np.unique(final_data[\"label_info\"].str.split(\":\", expand=True)[0].to_numpy()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "8a8eddf6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8a8eddf6",
        "outputId": "06e0790b-b5f1-4c45-9a0d-6fec55e33d31"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "50\n",
            "['ABBR:abb' 'ABBR:exp' 'DESC:def' 'DESC:desc' 'DESC:manner' 'DESC:reason'\n",
            " 'ENTY:animal' 'ENTY:body' 'ENTY:color' 'ENTY:cremat' 'ENTY:currency'\n",
            " 'ENTY:dismed' 'ENTY:event' 'ENTY:food' 'ENTY:instru' 'ENTY:lang'\n",
            " 'ENTY:letter' 'ENTY:other' 'ENTY:plant' 'ENTY:product' 'ENTY:religion'\n",
            " 'ENTY:sport' 'ENTY:substance' 'ENTY:symbol' 'ENTY:techmeth' 'ENTY:termeq'\n",
            " 'ENTY:veh' 'ENTY:word' 'HUM:desc' 'HUM:gr' 'HUM:ind' 'HUM:title'\n",
            " 'LOC:city' 'LOC:country' 'LOC:mount' 'LOC:other' 'LOC:state' 'NUM:code'\n",
            " 'NUM:count' 'NUM:date' 'NUM:dist' 'NUM:money' 'NUM:ord' 'NUM:other'\n",
            " 'NUM:perc' 'NUM:period' 'NUM:speed' 'NUM:temp' 'NUM:volsize' 'NUM:weight']\n"
          ]
        }
      ],
      "source": [
        "print(len(np.unique(final_data[\"label_info\"].to_numpy())))\n",
        "print(np.unique(final_data[\"label_info\"].to_numpy()))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5a02e908",
      "metadata": {
        "id": "5a02e908"
      },
      "source": [
        "## Sentence Transformer Model - all-mpnet-base-v2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "9560846a",
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "abc18e8d560146de95f8527db6315d56"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "sentTransformerModel = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b1cf52ff",
      "metadata": {
        "id": "b1cf52ff"
      },
      "source": [
        "### Generate Embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "2a0961bb",
      "metadata": {
        "id": "2a0961bb"
      },
      "outputs": [],
      "source": [
        "questionEmbeddings = sentTransformerModel.encode(final_data[\"question\"].to_numpy())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "7423ea56",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7423ea56",
        "outputId": "88e7154b-616f-4b8f-f58b-31f8605bab8b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 768)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "questionEmbeddings.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.save(file=\"qna_3000_emebddings\", arr = questionEmbeddings)"
      ],
      "metadata": {
        "id": "rkrTKY2l9k6r"
      },
      "id": "rkrTKY2l9k6r",
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "1fc9d3e0",
      "metadata": {
        "id": "1fc9d3e0"
      },
      "source": [
        "## Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "7703049e",
      "metadata": {
        "id": "7703049e"
      },
      "outputs": [],
      "source": [
        "pca = PCA(n_components=3)\n",
        "dimReducer = pca.fit(questionEmbeddings)\n",
        "X_r = dimReducer.transform(X=questionEmbeddings)\n",
        "# lda = LinearDiscriminantAnalysis(n_components=2)\n",
        "# X_r2 = lda.fit(X, y).transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "1180686d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1180686d",
        "outputId": "dbfe1fe4-b2e9-4c0b-efd6-d782fddc7f74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.0371138  0.03278541 0.02146814]\n"
          ]
        }
      ],
      "source": [
        "print(pca.explained_variance_ratio_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "bbaa46a0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbaa46a0",
        "outputId": "bae8f363-ad43-4c77-a260-aaefdf857727"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[9.872572  9.2790365 7.508615 ]\n"
          ]
        }
      ],
      "source": [
        "print(pca.singular_values_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "44569a03",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "44569a03",
        "outputId": "5ef11c6d-1416-4e12-ede7-91fa1ee2043c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(projection='3d')\n",
        "ax.scatter(X_r[:,0], X_r[:,1],X_r[:,2])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "b2e73189",
      "metadata": {
        "id": "b2e73189"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "37c2584f",
      "metadata": {
        "id": "37c2584f"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "010bcb0a",
      "metadata": {
        "id": "010bcb0a"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "ad1e496c",
      "metadata": {
        "id": "ad1e496c"
      },
      "source": [
        "## Clustering"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dcab650d",
      "metadata": {
        "id": "dcab650d"
      },
      "source": [
        "\n",
        "### KMeans"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "7189c04a",
      "metadata": {
        "id": "7189c04a"
      },
      "outputs": [],
      "source": [
        "X = questionEmbeddings\n",
        "X = StandardScaler().fit_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels_true = final_data[\"label_info\"].to_numpy()\n",
        "K_clusters = 50 \n",
        "\n",
        "# labels_true = final_data[\"label_info\"].str.split(\":\", expand=True)[0].to_numpy()\n",
        "# K_clusters = 6 "
      ],
      "metadata": {
        "id": "BWXgk_0v_nLL"
      },
      "id": "BWXgk_0v_nLL",
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "55301a0b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "55301a0b",
        "outputId": "b512bb16-28e9-4108-a6cb-6a792d42c72b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estimated number of clusters: 50\n",
            "Estimated number of noise points: 0\n",
            "Homogeneity: 0.437\n",
            "Completeness: 0.361\n",
            "V-measure: 0.395\n",
            "Adjusted Rand Index: 0.106\n",
            "Adjusted Mutual Information: 0.330\n",
            "Silhouette Coefficient: 0.032\n"
          ]
        }
      ],
      "source": [
        "kmeans = KMeans(n_clusters=K_clusters, random_state=0).fit(X)\n",
        "labels = kmeans.labels_\n",
        "\n",
        "# Number of clusters in labels, ignoring noise if present.\n",
        "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
        "n_noise_ = list(labels).count(-1)\n",
        "\n",
        "print(\"Estimated number of clusters: %d\" % n_clusters_)\n",
        "print(\"Estimated number of noise points: %d\" % n_noise_)\n",
        "print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels_true, labels))\n",
        "print(\"Completeness: %0.3f\" % metrics.completeness_score(labels_true, labels))\n",
        "print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels_true, labels))\n",
        "print(\"Adjusted Rand Index: %0.3f\" % metrics.adjusted_rand_score(labels_true, labels))\n",
        "print(\n",
        "    \"Adjusted Mutual Information: %0.3f\"\n",
        "    % metrics.adjusted_mutual_info_score(labels_true, labels)\n",
        ")\n",
        "print(\"Silhouette Coefficient: %0.3f\" % metrics.silhouette_score(X, labels))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "516670ec",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "516670ec",
        "outputId": "27bf489e-ea00-448b-ef7e-1140a2ce9756"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(projection='3d')\n",
        "\n",
        "rough_df = pd.DataFrame(dict(x=X_r[:,0], y=X_r[:,1], z=X_r[:,2], label=labels))\n",
        "\n",
        "groups = rough_df.groupby('label')\n",
        "\n",
        "for name, group in groups: \n",
        "  ax.scatter(group.x, group.y, group.z, marker='o', label=name)\n",
        "# ax.legend()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "be06455b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "be06455b",
        "outputId": "5d87e375-b4d5-4b9f-c5c1-d0a8934fb3d0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 44,  26],\n",
              "       [  4,  27],\n",
              "       [ 22,  32],\n",
              "       [ 19,  38],\n",
              "       [ 25,  39],\n",
              "       [ 14,  39],\n",
              "       [  8,  39],\n",
              "       [ 28,  39],\n",
              "       [ 24,  40],\n",
              "       [  2,  40],\n",
              "       [ 39,  41],\n",
              "       [ 45,  42],\n",
              "       [  1,  42],\n",
              "       [ 36,  43],\n",
              "       [ 20,  49],\n",
              "       [ 40,  50],\n",
              "       [ 21,  50],\n",
              "       [  5,  51],\n",
              "       [ 32,  52],\n",
              "       [ 47,  53],\n",
              "       [ 41,  55],\n",
              "       [ 13,  55],\n",
              "       [ 18,  56],\n",
              "       [  9,  56],\n",
              "       [ 26,  57],\n",
              "       [ 37,  58],\n",
              "       [ 33,  58],\n",
              "       [ 10,  59],\n",
              "       [  3,  60],\n",
              "       [ 31,  60],\n",
              "       [ 11,  61],\n",
              "       [ 46,  63],\n",
              "       [ 34,  65],\n",
              "       [ 43,  66],\n",
              "       [ 49,  68],\n",
              "       [ 42,  70],\n",
              "       [ 27,  72],\n",
              "       [ 17,  72],\n",
              "       [ 38,  75],\n",
              "       [  7,  75],\n",
              "       [ 16,  76],\n",
              "       [ 35,  78],\n",
              "       [ 23,  81],\n",
              "       [ 30,  81],\n",
              "       [ 12,  87],\n",
              "       [ 48,  90],\n",
              "       [  6,  96],\n",
              "       [  0,  99],\n",
              "       [ 29, 106],\n",
              "       [ 15, 113]])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "unique, counts = np.unique(labels, return_counts=True)\n",
        "freq_chart = np.asarray((unique, counts)).T\n",
        "freq_chart[freq_chart[:, 1].argsort()]\n",
        "# freq_chart"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "77ee573d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "77ee573d",
        "outputId": "e89f77dd-9991-43e6-e02e-be17cda9d774"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000,)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "(labels==15).shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_data.iloc[(labels==44),0:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "IdnornJ9RxOO",
        "outputId": "fd9e0497-c0ec-4363-9d88-c0fba3ac764d"
      },
      "id": "IdnornJ9RxOO",
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                                               question    label_info\n",
              "14    What is considered the costliest disaster the ...    ENTY:event\n",
              "33    Which company that manufactures video-game har...        HUM:gr\n",
              "128   What piano company claims its product is the `...        HUM:gr\n",
              "165   Which Japanese car maker had its biggest perce...        HUM:gr\n",
              "229        How much does a new railroad coal car cost ?     NUM:money\n",
              "309   What is the fastest commercial automobile that...    ENTY:other\n",
              "344   Who is the leading competitor of Trans Union C...        HUM:gr\n",
              "381   What is the abbreviation of the company name `...      ABBR:abb\n",
              "487   What stereo manufacturer is `` Slightly ahead ...        HUM:gr\n",
              "499   Which oil company almost picked a word that me...        HUM:gr\n",
              "786   What was the price of Varian Associates ' vacu...     NUM:money\n",
              "878   Why are electric cars less efficient in the no...   DESC:reason\n",
              "903   What company is being bought by Yahoo and how ...        HUM:gr\n",
              "1003  What car was driven in the 199 release of `` S...  ENTY:product\n",
              "1020  Where can I buy a good snowboard for less than...     LOC:other\n",
              "1038  What Japanese manufacturer is known for both i...        HUM:gr\n",
              "1225  How is the new Ford Econoline van F25 V1 engine ?   DESC:manner\n",
              "1356     What does an irate car owner call iron oxide ?   ENTY:termeq\n",
              "1414  What Japanese electronics company was named fo...        HUM:gr\n",
              "1592  Which company claimed to be `` the world 's bi...        HUM:gr\n",
              "1742       What is the abbreviation of General Motors ?      ABBR:abb\n",
              "2179                             Where is Inoco based ?     LOC:other\n",
              "2289  What is the all-time stock high of Apple Compu...     NUM:other\n",
              "2374     What 's the name of the Tokyo Stock Exchange ?   ENTY:termeq\n",
              "2506  What is main reason for that electric vehicles...   DESC:reason\n",
              "2747  What South African producer had a 1988 profit ...        HUM:gr"
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-qY1VOV3RxS5"
      },
      "id": "-qY1VOV3RxS5",
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "J4CMIgZQRxVr"
      },
      "id": "J4CMIgZQRxVr",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "d33a002c",
      "metadata": {
        "id": "d33a002c"
      },
      "source": [
        "### DBSCAN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e56bbad0",
      "metadata": {
        "id": "e56bbad0"
      },
      "outputs": [],
      "source": [
        "X = questionEmbeddings\n",
        "X = StandardScaler().fit_transform(X)\n",
        "labels_true = final_data[\"label_info\"].to_numpy()\n",
        "# labels_true = final_data[\"label_info\"].str.split(\":\", expand=True)[0].to_numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1970552b",
      "metadata": {
        "id": "1970552b"
      },
      "outputs": [],
      "source": [
        "db = DBSCAN(eps=10.3, min_samples=5).fit(X)\n",
        "core_samples_mask = np.zeros_like(db.labels_, dtype=bool)\n",
        "core_samples_mask[db.core_sample_indices_] = True\n",
        "labels = db.labels_\n",
        "\n",
        "# Number of clusters in labels, ignoring noise if present.\n",
        "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
        "n_noise_ = list(labels).count(-1)\n",
        "\n",
        "print(\"Estimated number of clusters: %d\" % n_clusters_)\n",
        "print(\"Estimated number of noise points: %d\" % n_noise_)\n",
        "print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels_true, labels))\n",
        "print(\"Completeness: %0.3f\" % metrics.completeness_score(labels_true, labels))\n",
        "print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels_true, labels))\n",
        "print(\"Adjusted Rand Index: %0.3f\" % metrics.adjusted_rand_score(labels_true, labels))\n",
        "print(\n",
        "    \"Adjusted Mutual Information: %0.3f\"\n",
        "    % metrics.adjusted_mutual_info_score(labels_true, labels)\n",
        ")\n",
        "print(\"Silhouette Coefficient: %0.3f\" % metrics.silhouette_score(X, labels))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "00db7ba9",
      "metadata": {
        "id": "00db7ba9"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "68fab961",
      "metadata": {
        "id": "68fab961"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5c3f8b35",
      "metadata": {
        "id": "5c3f8b35"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cb063919",
      "metadata": {
        "id": "cb063919"
      },
      "outputs": [],
      "source": []
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