{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        ":::{.content-hidden}\n",
        "# Visual Exploration\n",
        ":::\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "cpSH14tMVHzL"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the CSV file\n",
        "memespector_file = \"/content/drive/MyDrive/2024-01-09-Bauernproteste/2024-01-11-Google-Vision-All.csv\"\n",
        "df = pd.read_csv(memespector_file)\n",
        "\n",
        "df = df[['Image_BaseName', 'GV_Label_Descriptions']]\n",
        "\n",
        "# Splitting the 'GV_Label_Descriptions' into individual labels\n",
        "split_labels = df['GV_Label_Descriptions'].str.split(';').apply(pd.Series, 1).stack()\n",
        "split_labels.index = split_labels.index.droplevel(-1)  # to line up with df's index\n",
        "split_labels.name = 'Label'\n",
        "\n",
        "# Joining the split labels with the original dataframe\n",
        "df_split = df.join(split_labels)\n",
        "\n",
        "# Creating a matrix of True/False values for each label per Image_BaseName\n",
        "matrix = pd.pivot_table(df_split, index='Image_BaseName', columns='Label', aggfunc=lambda x: True, fill_value=False)\n",
        "\n",
        "# Resetting the column headers to be the label names only\n",
        "matrix.columns = [col[1] for col in matrix.columns.values]\n",
        "\n",
        "# Now 'matrix' has a single level of column headers with only the label names\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "BdWLaadBbNAD",
        "outputId": "9b2fbc27-82b1-4b34-e008-fbecf5841b47"
      },
      "outputs": [
        {
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              "<p>982 rows × 681 columns</p>\n",
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            "text/plain": [
              "                          Adaptation   Advertising   Afterglow  \\\n",
              "Image_BaseName                                                   \n",
              "6750551853789891846.jpg        False         False       False   \n",
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              "...                              ...           ...         ...   \n",
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              "7321806774967815457.jpg         True         False       False   \n",
              "7321806890906701089.jpg        False         False       False   \n",
              "\n",
              "                          Agricultural machinery   Agriculture   Air travel  \\\n",
              "Image_BaseName                                                                \n",
              "6750551853789891846.jpg                    False         False        False   \n",
              "6750761577349254405.jpg                    False         False        False   \n",
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              "...                                          ...           ...          ...   \n",
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              "7321806774967815457.jpg                    False         False        False   \n",
              "7321806890906701089.jpg                    False         False        False   \n",
              "\n",
              "                          Aircraft   Airliner   Airplane   Alloy wheel  ...  \\\n",
              "Image_BaseName                                                          ...   \n",
              "6750551853789891846.jpg      False      False      False         False  ...   \n",
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              "\n",
              "                         Vertebrate  Water  Water resources  Wheel  Whiskers  \\\n",
              "Image_BaseName                                                                 \n",
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              "\n",
              "                         White  Window   Wood  Working animal  World  \n",
              "Image_BaseName                                                        \n",
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              "7321806890906701089.jpg  False   False  False           False  False  \n",
              "\n",
              "[982 rows x 681 columns]"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "I3DQEP4mZBRY"
      },
      "outputs": [],
      "source": [
        "from sklearn.decomposition import PCA\n",
        "from sklearn.cluster import KMeans\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Ensuring that 'Image_BaseName' is not part of the matrix to apply PCA\n",
        "image_base_names = matrix.index  # Saving the image base names for later use\n",
        "label_matrix = matrix.values  # Convert to numpy array for PCA\n",
        "\n",
        "# Dimensionality reduction using PCA\n",
        "# Considering a variance ratio of 0.95 to determine the number of components\n",
        "pca = PCA(n_components=0.95)\n",
        "matrix_reduced = pca.fit_transform(label_matrix)\n",
        "\n",
        "# If needed, you can create a DataFrame from the PCA-reduced matrix and reattach the 'Image_BaseName' column\n",
        "matrix_reduced_df = pd.DataFrame(matrix_reduced, index=image_base_names)"
      ]
    },
    {
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      "execution_count": 4,
      "metadata": {
        "colab": {
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          "height": 455
        },
        "id": "H4OyJJ7qZL2-",
        "outputId": "ce27bed9-3828-4113-9bbb-25d920574bb8"
      },
      "outputs": [
        {
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5cbbcd4e-55fd-4c6f-8481-8d0e770270b5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-5cbbcd4e-55fd-4c6f-8481-8d0e770270b5 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-5cbbcd4e-55fd-4c6f-8481-8d0e770270b5');\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",
              "\n",
              "\n",
              "<div id=\"df-96a14176-3b5b-48bc-87f1-84d5f9c732d0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-96a14176-3b5b-48bc-87f1-84d5f9c732d0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     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-96a14176-3b5b-48bc-87f1-84d5f9c732d0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                              0         1         2         3         4    \\\n",
              "Image_BaseName                                                              \n",
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              "6763591353164254469.jpg  0.657532 -0.007257 -0.226448 -0.142833 -0.615043   \n",
              "6766552734108749062.jpg  1.638604 -0.418596 -0.178993 -0.522654  0.663303   \n",
              "...                           ...       ...       ...       ...       ...   \n",
              "7321800737606896928.jpg -0.698156  0.191274 -0.529836  0.047008  0.862388   \n",
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              "7321804909290999045.jpg  1.005015  0.923683  0.371054  0.533427  0.356759   \n",
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              "7321806890906701089.jpg -0.042383 -0.138050  0.075564 -0.396196  0.056236   \n",
              "\n",
              "                              5         6         7         8         9    \\\n",
              "Image_BaseName                                                              \n",
              "6750551853789891846.jpg -0.313126  0.376667  0.370456 -0.012519 -0.898472   \n",
              "6750761577349254405.jpg  0.278001  0.860551 -0.387782 -0.041959  0.146992   \n",
              "6751467034741067014.jpg  0.130516  0.835040  0.056190 -0.175465 -0.551632   \n",
              "6763591353164254469.jpg -0.208217 -0.082478  0.181550  0.899774  0.462160   \n",
              "6766552734108749062.jpg -0.186928  1.000894 -0.307874 -0.172688  0.336597   \n",
              "...                           ...       ...       ...       ...       ...   \n",
              "7321800737606896928.jpg -0.111187 -0.390502 -0.089231  0.144091  0.326504   \n",
              "7321804342179204384.jpg  0.128612  0.042362  1.019634 -0.367217  1.025644   \n",
              "7321804909290999045.jpg  0.813597  0.087288 -0.289707  0.377865  1.242866   \n",
              "7321806774967815457.jpg -0.209626 -0.179683  0.529353 -0.239506  0.048401   \n",
              "7321806890906701089.jpg  0.612394 -0.272538 -0.230238 -0.379339 -0.668773   \n",
              "\n",
              "                         ...       232       233       234       235  \\\n",
              "Image_BaseName           ...                                           \n",
              "6750551853789891846.jpg  ... -0.007803  0.022912 -0.002782  0.019272   \n",
              "6750761577349254405.jpg  ...  0.020865  0.027422  0.064993  0.046791   \n",
              "6751467034741067014.jpg  ... -0.009497  0.144801 -0.020713  0.035502   \n",
              "6763591353164254469.jpg  ... -0.025889  0.006257  0.060421  0.028564   \n",
              "6766552734108749062.jpg  ... -0.009052 -0.002043  0.007575 -0.031553   \n",
              "...                      ...       ...       ...       ...       ...   \n",
              "7321800737606896928.jpg  ... -0.015025 -0.068188 -0.023787  0.009343   \n",
              "7321804342179204384.jpg  ... -0.002146 -0.042328  0.114229 -0.066740   \n",
              "7321804909290999045.jpg  ...  0.005721  0.000672  0.021087  0.020260   \n",
              "7321806774967815457.jpg  ... -0.012399  0.023383 -0.073488  0.063523   \n",
              "7321806890906701089.jpg  ... -0.106623 -0.214393  0.209117  0.021869   \n",
              "\n",
              "                              236       237       238       239       240  \\\n",
              "Image_BaseName                                                              \n",
              "6750551853789891846.jpg -0.005465 -0.005129  0.011833  0.000200  0.006499   \n",
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              "6751467034741067014.jpg -0.085562 -0.169911  0.083582  0.045916 -0.123521   \n",
              "6763591353164254469.jpg  0.045773  0.000179  0.003499  0.027838  0.007171   \n",
              "6766552734108749062.jpg  0.007831 -0.005779 -0.023599 -0.021165 -0.000496   \n",
              "...                           ...       ...       ...       ...       ...   \n",
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              "7321804342179204384.jpg -0.051395 -0.021397  0.012134  0.046365 -0.005712   \n",
              "7321804909290999045.jpg  0.037709  0.000290  0.015725  0.013237  0.018040   \n",
              "7321806774967815457.jpg  0.013320  0.020351 -0.033865  0.029809 -0.080413   \n",
              "7321806890906701089.jpg  0.220278  0.070092 -0.198979  0.140981 -0.004653   \n",
              "\n",
              "                              241  \n",
              "Image_BaseName                     \n",
              "6750551853789891846.jpg  0.010995  \n",
              "6750761577349254405.jpg -0.045372  \n",
              "6751467034741067014.jpg  0.032273  \n",
              "6763591353164254469.jpg -0.051516  \n",
              "6766552734108749062.jpg -0.006467  \n",
              "...                           ...  \n",
              "7321800737606896928.jpg  0.110626  \n",
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              "7321804909290999045.jpg -0.002060  \n",
              "7321806774967815457.jpg -0.074329  \n",
              "7321806890906701089.jpg -0.070667  \n",
              "\n",
              "[982 rows x 242 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "matrix_reduced_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "n-N08-gqZEhB",
        "outputId": "ad978bab-0987-44bb-ade1-d824a550f184"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Elbow method to determine optimal number of clusters\n",
        "inertia = []\n",
        "range_values = range(1, 20)  # Checking for 1 to 10 clusters\n",
        "\n",
        "for i in range_values:\n",
        "    kmeans = KMeans(n_clusters=i, n_init=10, random_state=0)\n",
        "    kmeans.fit(matrix_reduced_df)\n",
        "    inertia.append(kmeans.inertia_)\n",
        "\n",
        "# Plotting the Elbow Curve\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(range_values, inertia, marker='o')\n",
        "plt.title('Elbow Method')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('Inertia')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "mdLzMx2zfOqe",
        "outputId": "63ec4548-c0a2-4f6f-83bd-efcd97672e34"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.cluster import KMeans\n",
        "from sklearn.metrics import silhouette_score\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define the range of clusters to try\n",
        "range_values = range(2, 20)\n",
        "\n",
        "silhouette_scores = []\n",
        "\n",
        "# Perform k-means clustering and compute silhouette scores\n",
        "for i in range_values:\n",
        "    try:\n",
        "        kmeans = KMeans(n_clusters=i, n_init=10, random_state=0)\n",
        "        kmeans.fit(matrix_reduced_df)\n",
        "        score = silhouette_score(matrix_reduced_df, kmeans.labels_)\n",
        "        silhouette_scores.append(score)\n",
        "    except Exception as e:\n",
        "        print(f\"An error occurred with {i} clusters: {e}\")\n",
        "\n",
        "# Plotting the Silhouette Scores\n",
        "with plt.style.context('seaborn-whitegrid'):\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.plot(range_values, silhouette_scores, marker='o')\n",
        "    plt.title('Silhouette Method')\n",
        "    plt.xlabel('Number of clusters')\n",
        "    plt.ylabel('Silhouette Score')\n",
        "    plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "Wv8wzZ3rZsjF"
      },
      "outputs": [],
      "source": [
        "# Final k-means clustering using n clusters\n",
        "kmeans_final = KMeans(n_clusters=11, n_init=10, random_state=0)\n",
        "clusters = kmeans_final.fit_predict(matrix_reduced)\n",
        "\n",
        "# Adding the cluster information back to the original dataframe\n",
        "matrix['Cluster'] = clusters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "iur6ta_jeZa0",
        "outputId": "441ed3aa-0dce-4718-b5ee-6a974e09c110"
      },
      "outputs": [
        {
          "data": {
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              "      <th>Adaptation</th>\n",
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              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-fc4d689f-8d2c-4101-b5f6-9132f8ca9b5f 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-fc4d689f-8d2c-4101-b5f6-9132f8ca9b5f');\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",
              "\n",
              "\n",
              "<div id=\"df-42aff5dc-c78e-49bc-bd32-acd2496587e1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-42aff5dc-c78e-49bc-bd32-acd2496587e1')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     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-42aff5dc-c78e-49bc-bd32-acd2496587e1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                          Adaptation   Advertising   Afterglow  \\\n",
              "Image_BaseName                                                   \n",
              "6750551853789891846.jpg        False         False       False   \n",
              "6750761577349254405.jpg        False         False       False   \n",
              "6751467034741067014.jpg        False         False       False   \n",
              "6763591353164254469.jpg        False         False       False   \n",
              "6766552734108749062.jpg        False         False       False   \n",
              "\n",
              "                          Agricultural machinery   Agriculture   Air travel  \\\n",
              "Image_BaseName                                                                \n",
              "6750551853789891846.jpg                    False         False        False   \n",
              "6750761577349254405.jpg                    False         False        False   \n",
              "6751467034741067014.jpg                    False          True        False   \n",
              "6763591353164254469.jpg                    False         False        False   \n",
              "6766552734108749062.jpg                    False         False        False   \n",
              "\n",
              "                          Aircraft   Airliner   Airplane   Alloy wheel  ...  \\\n",
              "Image_BaseName                                                          ...   \n",
              "6750551853789891846.jpg      False      False      False         False  ...   \n",
              "6750761577349254405.jpg      False      False      False         False  ...   \n",
              "6751467034741067014.jpg      False      False      False         False  ...   \n",
              "6763591353164254469.jpg      False      False      False         False  ...   \n",
              "6766552734108749062.jpg      False      False      False         False  ...   \n",
              "\n",
              "                         Water  Water resources  Wheel  Whiskers  White  \\\n",
              "Image_BaseName                                                            \n",
              "6750551853789891846.jpg  False            False  False     False  False   \n",
              "6750761577349254405.jpg  False            False  False     False  False   \n",
              "6751467034741067014.jpg  False            False  False     False  False   \n",
              "6763591353164254469.jpg  False            False  False     False  False   \n",
              "6766552734108749062.jpg  False            False  False     False  False   \n",
              "\n",
              "                         Window   Wood  Working animal  World  Cluster  \n",
              "Image_BaseName                                                          \n",
              "6750551853789891846.jpg   False  False           False  False        8  \n",
              "6750761577349254405.jpg   False  False           False  False        2  \n",
              "6751467034741067014.jpg   False  False           False  False        6  \n",
              "6763591353164254469.jpg   False  False           False  False        0  \n",
              "6766552734108749062.jpg   False  False           False  False        8  \n",
              "\n",
              "[5 rows x 682 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Displaying the first few rows of the dataframe with cluster information\n",
        "matrix.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mg2PzVbQaCGA"
      },
      "outputs": [],
      "source": [
        "!unzip /content/drive/MyDrive/2024-01-09-Bauernproteste/2024-01-09-Images-Clean.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "oFzwaDLAaEIw"
      },
      "outputs": [],
      "source": [
        "# Display the result. See linked notebook for code."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}