02_Outfits_Preprocessing.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/zainab9271/Outfit-Compatibility-Prediction/blob/main/02_Outfits_Preprocessing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# Unzip Images"
      ],
      "metadata": {
        "id": "eP9mX6kqdv99"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Clai5xi5dh0P"
      },
      "outputs": [],
      "source": [
        "!unzip /content/drive/MyDrive/Outfit_Recommendation_Project/polyvore_outfits_images.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Libraries"
      ],
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import re\n",
        "import tqdm"
      ],
      "metadata": {
        "id": "aaypCk1zd_sX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "# Items Info"
      ],
      "metadata": {
        "id": "4ccohdz_e1LM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "item_img_df = pd.read_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/Data_CSV_Files/item_image.csv')\n",
        "item_img_df"
      ],
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        "id": "Jsmd42kSetdf",
        "outputId": "ab49e641-442a-4743-95de-94616f07e0d2"
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              "          item_id                                    item_image_path\n",
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              "261056   80647609  /content/content/polyvore_outfits/images/80647...\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 261057 entries, 0 to 261056\n",
            "Data columns (total 2 columns):\n",
            " #   Column           Non-Null Count   Dtype \n",
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            " 1   item_image_path  261057 non-null  object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 4.0+ MB\n"
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      "source": [
        "item_metadata_df = pd.read_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/Data_CSV_Files/item_metadata.csv')\n",
        "item_metadata_df"
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              "      <td>108411005.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251064</th>\n",
              "      <td>thom browne little boy knitted</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>236</td>\n",
              "      <td>outerwear</td>\n",
              "      <td>212452593.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251065</th>\n",
              "      <td>colorful vortex print shawl scarf</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>105</td>\n",
              "      <td>scarves</td>\n",
              "      <td>208651882.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251066</th>\n",
              "      <td>floral embroidered mesh see thru</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11</td>\n",
              "      <td>tops</td>\n",
              "      <td>209210426.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251067</th>\n",
              "      <td>shein sheinside extreme destroyed denim</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25</td>\n",
              "      <td>outerwear</td>\n",
              "      <td>181567392.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>251068 rows × 8 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a7a3797-e6a8-421d-ab76-bd63472127a3')\"\n",
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              "                                                     [key], {});\n",
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              "\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",
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "item_metadata_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6P-z5ifJgCaD",
        "outputId": "c63d063e-4da4-4c83-ed8d-31dcd57288b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 251068 entries, 0 to 251067\n",
            "Data columns (total 8 columns):\n",
            " #   Column             Non-Null Count   Dtype  \n",
            "---  ------             --------------   -----  \n",
            " 0   url_name           251008 non-null  object \n",
            " 1   description        72212 non-null   object \n",
            " 2   catgeories         74852 non-null   object \n",
            " 3   title              74852 non-null   object \n",
            " 4   related            74852 non-null   object \n",
            " 5   category_id        251008 non-null  object \n",
            " 6   semantic_category  251008 non-null  object \n",
            " 7   item_id            250948 non-null  float64\n",
            "dtypes: float64(1), object(7)\n",
            "memory usage: 15.3+ MB\n"
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    {
      "cell_type": "code",
      "source": [
        "item_metadata_df.item_id.isna().sum()"
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      "metadata": {
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        "outputId": "2baeac5f-6a7c-4a37-f4c0-530378999b8a"
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      "execution_count": null,
      "outputs": [
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          "metadata": {},
          "execution_count": 7
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        "item_metadata_df.dropna(subset = ['item_id'], inplace=True)\n",
        "item_metadata_df.info()"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 250948 entries, 0 to 251067\n",
            "Data columns (total 8 columns):\n",
            " #   Column             Non-Null Count   Dtype  \n",
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            " 7   item_id            250948 non-null  float64\n",
            "dtypes: float64(1), object(7)\n",
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      "outputs": []
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 250948 entries, 0 to 251067\n",
            "Data columns (total 8 columns):\n",
            " #   Column             Non-Null Count   Dtype \n",
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            " 4   related            74792 non-null   object\n",
            " 5   category_id        250948 non-null  object\n",
            " 6   semantic_category  250948 non-null  object\n",
            " 7   item_id            250948 non-null  int64 \n",
            "dtypes: int64(1), object(7)\n",
            "memory usage: 17.2+ MB\n"
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    {
      "cell_type": "code",
      "source": [
        "item_metadata_df"
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              "                                             url_name  \\\n",
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              "                                              description  \\\n",
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              "251064                                                NaN   \n",
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              "251063  [\"Women's Fashion\", 'Clothing', 'Skirts', 'Kne...   \n",
              "251064                                                NaN   \n",
              "251065                                                NaN   \n",
              "251066                                                NaN   \n",
              "251067                                                NaN   \n",
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              "                                                title  \\\n",
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              "3          Balmain Women High Waist Flared Knit Pants   \n",
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              "251064                                            NaN   \n",
              "251065                                            NaN   \n",
              "251066                                            NaN   \n",
              "251067                                            NaN   \n",
              "\n",
              "                                                  related category_id  \\\n",
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              "251063                 ['Red skirt', 'Embellished skirt']           9   \n",
              "251064                                                NaN         236   \n",
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              "251067                                                NaN          25   \n",
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              "      <td>Arnica Embellished Hand Painted Skirt</td>\n",
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              "      <td>NaN</td>\n",
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              "<p>250948 rows × 8 columns</p>\n",
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              "\n",
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              "\n",
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              "            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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "item_metadata_df.semantic_category.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VnpcoLLC8v3J",
        "outputId": "ccc66746-e626-401a-de9f-f8699eb99e52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['tops', 'bottoms', 'jewellery', 'shoes', 'outerwear', 'scarves',\n",
              "       'sunglasses', 'bags', 'hats', 'all-body', 'accessories'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train Outfits Info"
      ],
      "metadata": {
        "id": "k6jgDrl0oivV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_outfits_imgs_df = pd.read_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/Data_CSV_Files/disjiont_train_outfits_images.csv')\n",
        "train_outfits_imgs_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "lesfJWQxoU-d",
        "outputId": "a4056b5b-4a92-4d16-dd51-94cb13bd9c3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "      outfits_images_id                                outfits_images_path\n",
              "0           199244701_1  /content/content/polyvore_outfits/images/13262...\n",
              "1           199244701_2  /content/content/polyvore_outfits/images/15396...\n",
              "2           199244701_3  /content/content/polyvore_outfits/images/17116...\n",
              "3           199244701_4  /content/content/polyvore_outfits/images/16279...\n",
              "4           199244701_5  /content/content/polyvore_outfits/images/17253...\n",
              "...                 ...                                                ...\n",
              "85930       205613256_4  /content/content/polyvore_outfits/images/17596...\n",
              "85931       205613256_5  /content/content/polyvore_outfits/images/16292...\n",
              "85932       205613256_6  /content/content/polyvore_outfits/images/18060...\n",
              "85933       225327855_1  /content/content/polyvore_outfits/images/18038...\n",
              "85934       225327855_2  /content/content/polyvore_outfits/images/20191...\n",
              "\n",
              "[85935 rows x 2 columns]"
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              "      <th>outfits_images_path</th>\n",
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              "      <td>/content/content/polyvore_outfits/images/15396...</td>\n",
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              "      <td>199244701_3</td>\n",
              "      <td>/content/content/polyvore_outfits/images/17116...</td>\n",
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              "      <th>3</th>\n",
              "      <td>199244701_4</td>\n",
              "      <td>/content/content/polyvore_outfits/images/16279...</td>\n",
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              "      <th>4</th>\n",
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              "      <td>/content/content/polyvore_outfits/images/17253...</td>\n",
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              "      <th>85930</th>\n",
              "      <td>205613256_4</td>\n",
              "      <td>/content/content/polyvore_outfits/images/17596...</td>\n",
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              "    <tr>\n",
              "      <th>85931</th>\n",
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              "      <td>/content/content/polyvore_outfits/images/16292...</td>\n",
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              "    <tr>\n",
              "      <th>85932</th>\n",
              "      <td>205613256_6</td>\n",
              "      <td>/content/content/polyvore_outfits/images/18060...</td>\n",
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              "    <tr>\n",
              "      <th>85933</th>\n",
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              "      <td>/content/content/polyvore_outfits/images/18038...</td>\n",
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              "      <th>85934</th>\n",
              "      <td>225327855_2</td>\n",
              "      <td>/content/content/polyvore_outfits/images/20191...</td>\n",
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              "<p>85935 rows × 2 columns</p>\n",
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              "            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",
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    {
      "cell_type": "code",
      "source": [
        "comp_train_outfits_df = pd.read_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/Data_CSV_Files/disjoint_compatibility_train_outfits.csv')\n",
        "comp_train_outfits_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "W2ZsSXr65cQj",
        "outputId": "e4c53206-3dea-4cbf-fc42-19262a31a3aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       compatibility                                      outfit_sample\n",
              "0                  1  ['199244701_1', '199244701_2', '199244701_3', ...\n",
              "1                  1  ['200742384_1', '200742384_2', '200742384_3', ...\n",
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              "4                  1  ['219393187_1', '219393187_2', '219393187_3', ...\n",
              "...              ...                                                ...\n",
              "33985              0  ['218506324_1', '221405016_1', '206461374_3', ...\n",
              "33986              0  ['105184990_1', '211494606_3', '204086912_4', ...\n",
              "33987              0  ['66630383_1', '132553628_1', '224927539_2', '...\n",
              "33988              0  ['149252664_1', '224932578_2', '205854787_3', ...\n",
              "33989              0                     ['119274863_2', '211094404_4']\n",
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              "          document.querySelector('#df-c55eed56-bf07-447d-8874-cef9ad883c85 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-c55eed56-bf07-447d-8874-cef9ad883c85');\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "comp_train_outfits_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BbgMXvvfBwQX",
        "outputId": "6d0f7d33-b945-415f-c36d-4b340e245676"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 33990 entries, 0 to 33989\n",
            "Data columns (total 2 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   compatibility  33990 non-null  int64 \n",
            " 1   outfit_sample  33990 non-null  object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 531.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test Outfits Info"
      ],
      "metadata": {
        "id": "qwRXHDay66Z8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_outfits_imgs_df = pd.read_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/Data_CSV_Files/disjiont_test_outfits_images.csv')\n",
        "test_outfits_imgs_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "1378705e-499c-4781-a84b-3c60dbff902b",
        "id": "lZcoHjJW66Z8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      outfits_images_id                                outfits_images_path\n",
              "0           222049137_1  /content/content/polyvore_outfits/images/15940...\n",
              "1           222049137_2  /content/content/polyvore_outfits/images/20081...\n",
              "2           222049137_3  /content/content/polyvore_outfits/images/20888...\n",
              "3           222049137_4  /content/content/polyvore_outfits/images/20889...\n",
              "4           222049137_5  /content/content/polyvore_outfits/images/17683...\n",
              "...                 ...                                                ...\n",
              "74257        42911078_7  /content/content/polyvore_outfits/images/39806...\n",
              "74258       210601169_1  /content/content/polyvore_outfits/images/18912...\n",
              "74259       210601169_2  /content/content/polyvore_outfits/images/18912...\n",
              "74260       210601169_3  /content/content/polyvore_outfits/images/18912...\n",
              "74261       210601169_4  /content/content/polyvore_outfits/images/18912...\n",
              "\n",
              "[74262 rows x 2 columns]"
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              "      <th>2</th>\n",
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              "      <td>/content/content/polyvore_outfits/images/20888...</td>\n",
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              "      <td>/content/content/polyvore_outfits/images/39806...</td>\n",
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              "      <td>/content/content/polyvore_outfits/images/18912...</td>\n",
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              "      <th>74259</th>\n",
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              "      <td>/content/content/polyvore_outfits/images/18912...</td>\n",
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              "      <td>/content/content/polyvore_outfits/images/18912...</td>\n",
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              "<p>74262 rows × 2 columns</p>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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    {
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      "source": [
        "comp_test_outfits_df = pd.read_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/Data_CSV_Files/disjoint_compatibility_test_outfits.csv')\n",
        "comp_test_outfits_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "b4d97465-d51a-49c5-cabc-4922cd5d1edc",
        "id": "WE8SrH3l66Z9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       compatibility                                      outfit_sample\n",
              "0                  1  ['222049137_1', '222049137_2', '222049137_3', ...\n",
              "1                  1  ['171638917_1', '171638917_2', '171638917_3', ...\n",
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              "4                  1  ['181727193_1', '181727193_2', '181727193_3', ...\n",
              "...              ...                                                ...\n",
              "30285              0  ['208697202_1', '222058995_1', '60152115_3', '...\n",
              "30286              0  ['163646445_2', '154832568_2', '190013480_4', ...\n",
              "30287              0  ['145970783_1', '190480945_1', '212286826_4', ...\n",
              "30288              0  ['177747016_1', '178806147_2', '164069948_5', ...\n",
              "30289              0  ['217323982_1', '174060325_3', '206566023_3', ...\n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
          "execution_count": 17
        }
      ]
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      "cell_type": "code",
      "source": [
        "comp_test_outfits_df.info()"
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      "metadata": {
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        },
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        "outputId": "b21646de-8ae3-41b4-c73a-c0e6fadc5181"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 30290 entries, 0 to 30289\n",
            "Data columns (total 2 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   compatibility  30290 non-null  int64 \n",
            " 1   outfit_sample  30290 non-null  object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 473.4+ KB\n"
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    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "VOfujo1xMKqP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Valid Outfits Info"
      ],
      "metadata": {
        "id": "Y5VmluyHMLJJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "valid_outfits_imgs_df = pd.read_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/Data_CSV_Files/disjiont_valid_outfits_images.csv')\n",
        "valid_outfits_imgs_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "9183ef82-9be6-409a-ff17-113abc79bc33",
        "id": "PagI6ZoDMLJJ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      outfits_images_id                                outfits_images_path\n",
              "0           223648758_1  /content/content/polyvore_outfits/images/21186...\n",
              "1           223648758_2  /content/content/polyvore_outfits/images/21179...\n",
              "2           223648758_3  /content/content/polyvore_outfits/images/18754...\n",
              "3           223648758_4  /content/content/polyvore_outfits/images/21208...\n",
              "4           223648758_5  /content/content/polyvore_outfits/images/21091...\n",
              "...                 ...                                                ...\n",
              "15283       198313686_6  /content/content/polyvore_outfits/images/16768...\n",
              "15284       223051514_1  /content/content/polyvore_outfits/images/18928...\n",
              "15285       223051514_2  /content/content/polyvore_outfits/images/20182...\n",
              "15286       223051514_3  /content/content/polyvore_outfits/images/14431...\n",
              "15287       223051514_4  /content/content/polyvore_outfits/images/17861...\n",
              "\n",
              "[15288 rows x 2 columns]"
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              "      <td>/content/content/polyvore_outfits/images/21208...</td>\n",
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              "      <td>/content/content/polyvore_outfits/images/17861...</td>\n",
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              "<p>15288 rows × 2 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
          "execution_count": 15
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    {
      "cell_type": "code",
      "source": [
        "comp_valid_outfits_df = pd.read_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/Data_CSV_Files/disjoint_compatibility_valid_outfits.csv')\n",
        "comp_valid_outfits_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "4f64dc79-f896-454d-9084-9d12925719b3",
        "id": "cWI9fIerMLJK"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      compatibility                                      outfit_sample\n",
              "0                 1  ['223648758_1', '223648758_2', '223648758_3', ...\n",
              "1                 1  ['219849644_1', '219849644_2', '219849644_3', ...\n",
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              "4                 1  ['216934649_1', '216934649_2', '216934649_3', ...\n",
              "...             ...                                                ...\n",
              "5995              0  ['215216859_1', '206870729_2', '209124426_3', ...\n",
              "5996              0  ['77811896_1', '207209295_2', '185443035_4', '...\n",
              "5997              0  ['214362907_1', '225321688_3', '158405253_9', ...\n",
              "5998              0  ['209869925_1', '204014202_3', '200907615_2', ...\n",
              "5999              0  ['166260715_1', '149847684_2', '197296523_2', ...\n",
              "\n",
              "[6000 rows x 2 columns]"
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              "<p>6000 rows × 2 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<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",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "comp_valid_outfits_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7bc427a-7aec-46a1-bb49-a7a3dae3e720",
        "id": "QnPeU-MdMLJK"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6000 entries, 0 to 5999\n",
            "Data columns (total 2 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   compatibility  6000 non-null   int64 \n",
            " 1   outfit_sample  6000 non-null   object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 93.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Remove Undesired Items from Outfit Samples"
      ],
      "metadata": {
        "id": "4NdxKVuW8OQ3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remove Items of Categories:\n",
        "\n",
        "* hats\n",
        "* accessories\n",
        "* jewellery\n",
        "* sunglasses\n",
        "* scarves\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "gHZr3OZG8Y10"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def parse_sample(outfit_sample):\n",
        "    '''\n",
        "    outfit_sample: STRING\n",
        "    returns list of items id\n",
        "    '''\n",
        "    outfit_sample = re.sub('[\\[\\]\\s\\']', '', outfit_sample)\n",
        "    outfit_items = np.array(outfit_sample.split(','))\n",
        "    return outfit_items"
      ],
      "metadata": {
        "id": "W8EHW6ry66Z9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def parse_outfit(outfit_items, train_test_valid = 'train' ):\n",
        "    '''\n",
        "    outfit_items: list of outfit items\n",
        "    returns dictionary of (outfit_item, item_path)\n",
        "    '''\n",
        "    if train_test_valid == 'train':\n",
        "        outfit_imgs_path = train_outfits_imgs_df.query(f'outfits_images_id in {outfit_items.tolist()}')\n",
        "    elif train_test_valid == 'test':\n",
        "        outfit_imgs_path = test_outfits_imgs_df.query(f'outfits_images_id in {outfit_items.tolist()}')\n",
        "    else:\n",
        "        outfit_imgs_path = valid_outfits_imgs_df.query(f'outfits_images_id in {outfit_items.tolist()}')\n",
        "    \n",
        "    outfit_imgs_path.set_index('outfits_images_id', drop=True, inplace=True)\n",
        "    \n",
        "    return outfit_imgs_path.to_dict()['outfits_images_path']\n",
        "    "
      ],
      "metadata": {
        "id": "XDnMSTQ_9yHZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_item_id(outfit_items_path):\n",
        "    '''\n",
        "    outfit_items_path: dictionary of (outfit_item, item_path)\n",
        "    returns dictionary of (outfit_item, item_id)\n",
        "    '''\n",
        "    outfit_items_id = {}\n",
        "    for key, value in outfit_items_path.items():\n",
        "        outfit_items_id[key] = item_img_df[item_img_df.item_image_path == value].item_id.to_numpy()[0]\n",
        "\n",
        "    return outfit_items_id"
      ],
      "metadata": {
        "id": "CNcYg5XLBiBk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_items_category(outfit_items_id):\n",
        "    '''\n",
        "    outfit_items_id: dictionary of (outfit_item, item_id)\n",
        "    returns dictionary of (outfit_item, category)\n",
        "    '''\n",
        "    outfit_items_category = {}\n",
        "    for key, value in outfit_items_id.items():\n",
        "        try:\n",
        "            outfit_items_category[key] = item_metadata_df.query(f'item_id == {value}').semantic_category.to_numpy()[0]\n",
        "        except:\n",
        "            outfit_items_category[key] = 'NON_AVAILABLE'\n",
        "    \n",
        "    return outfit_items_category"
      ],
      "metadata": {
        "id": "yzUAoz8MF5rO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_undesired_items(outfit_sample, train_test_valid = 'train'):\n",
        "    '''\n",
        "    outfit_sample: string\n",
        "    '''\n",
        "    undesired_categories = ('hats','accessories','jewellery','sunglasses','scarves', 'NON_AVAILABLE')\n",
        "    outfit_items = parse_sample(outfit_sample)\n",
        "    \n",
        "    outfit_items_path = parse_outfit(outfit_items, train_test_valid)\n",
        "    \n",
        "    outfit_items_id = get_item_id(outfit_items_path)\n",
        "    \n",
        "    outfit_items_category = get_items_category(outfit_items_id)\n",
        "\n",
        "    #in case we have several items of the same category, we only take one item from each category\n",
        "    group_items_by_categ = {}\n",
        "    for key, value in outfit_items_category.items():\n",
        "        if not group_items_by_categ.get(value):\n",
        "            group_items_by_categ[value] = []\n",
        "        group_items_by_categ[value].append(key)\n",
        "    \n",
        "    for category, outfit_items in group_items_by_categ.items():\n",
        "        group_items_by_categ[category] = outfit_items[np.random.randint(len(outfit_items))]\n",
        "\n",
        "    # modified_sample = np.array([])\n",
        "    modified_sample = {'tops':0, 'bottoms':0, 'shoes':0, 'outerwear':0, 'bags':0, 'all-body':0}\n",
        "    for category, outfit_item in group_items_by_categ.items():\n",
        "        if category not in undesired_categories:\n",
        "            modified_sample[category] = outfit_item\n",
        "\n",
        "    modified_sample = np.array2string(np.array(list(modified_sample.values())), separator=',')\n",
        "\n",
        "    return modified_sample\n"
      ],
      "metadata": {
        "id": "VbGehsLD-jU5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Compatibility Train Data"
      ],
      "metadata": {
        "id": "4mo9F0ndsw7N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in tqdm.tqdm(np.arange(comp_train_outfits_df.shape[0])):\n",
        "    comp_train_outfits_df.at[i,'outfit_sample'] = remove_undesired_items(comp_train_outfits_df.outfit_sample.iloc[i], train_test_valid = 'train')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "uHbCZIwEd_K8",
        "outputId": "bd97e1a0-1d33-42e5-be9a-af523eb17790"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 33990/33990 [1:10:17<00:00,  8.06it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "comp_train_outfits_df"
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          "height": 423
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        "id": "zx6L096efl9F",
        "outputId": "83a8dc5a-aa13-47c3-a2fd-c68bfb8ea789"
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              "       compatibility                                      outfit_sample\n",
              "0                  1  ['0','0','199244701_2','0','199244701_1','1992...\n",
              "1                  1  ['0','0','200742384_2','0','200742384_1','2007...\n",
              "2                  1  ['0','206955877_5','206955877_2','206955877_6'...\n",
              "3                  1  ['0','0','220340816_2','0','220340816_1','2203...\n",
              "4                  1  ['219393187_4','219393187_5','219393187_2','0'...\n",
              "...              ...                                                ...\n",
              "33985              0  ['0','0','221405016_1','0','218506324_1','2188...\n",
              "33986              0  ['0','0','211494606_3','0','105184990_1','2205...\n",
              "33987              0  ['85057420_4','206629909_3','132553628_1','0',...\n",
              "33988              0  ['205854787_3','212115201_6','224932578_2','0'...\n",
              "33989              0      ['0','0','119274863_2','0','0','211094404_4']\n",
              "\n",
              "[33990 rows x 2 columns]"
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>['85057420_4','206629909_3','132553628_1','0',...</td>\n",
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              "      <td>0</td>\n",
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              "      <th>33989</th>\n",
              "      <td>0</td>\n",
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        "comp_train_outfits_df.info()"
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      "outputs": [
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 33990 entries, 0 to 33989\n",
            "Data columns (total 2 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   compatibility  33990 non-null  int64 \n",
            " 1   outfit_sample  33990 non-null  object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 531.2+ KB\n"
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    {
      "cell_type": "markdown",
      "source": [
        "## Compatibility Test Data"
      ],
      "metadata": {
        "id": "Ny49H2ZVtKHm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in tqdm.tqdm(np.arange(comp_test_outfits_df.shape[0])):\n",
        "    comp_test_outfits_df.at[i,'outfit_sample'] = remove_undesired_items(comp_test_outfits_df.outfit_sample.iloc[i], train_test_valid = 'test')"
      ],
      "metadata": {
        "colab": {
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        "id": "-KzDZLGwhhDh",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 30290/30290 [1:00:05<00:00,  8.40it/s]\n"
          ]
        }
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "       compatibility                                      outfit_sample\n",
              "0                  1  ['222049137_3','222049137_6','222049137_1','0'...\n",
              "1                  1  ['171638917_4','171638917_5','171638917_2','0'...\n",
              "2                  1  ['209614451_3','209614451_5','209614451_2','0'...\n",
              "3                  1  ['208805759_3','0','208805759_2','0','20880575...\n",
              "4                  1      ['181727193_2','0','181727193_1','0','0','0']\n",
              "...              ...                                                ...\n",
              "30285              0  ['0','212297360_5','222058995_1','130650100_6'...\n",
              "30286              0  ['154832568_2','214779274_4','163646445_2','0'...\n",
              "30287              0  ['0','70763845_7','190480945_1','185277399_3',...\n",
              "30288              0  ['210542316_2','142695741_3','178806147_2','19...\n",
              "30289              0  ['206566023_3','221349485_5','0','0','21732398...\n",
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              "[30290 rows x 2 columns]"
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              "    <tr>\n",
              "      <th>30288</th>\n",
              "      <td>0</td>\n",
              "      <td>['210542316_2','142695741_3','178806147_2','19...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30289</th>\n",
              "      <td>0</td>\n",
              "      <td>['206566023_3','221349485_5','0','0','21732398...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>30290 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-92107a4c-6884-4312-95b8-70765b6a9f70')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\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-92107a4c-6884-4312-95b8-70765b6a9f70 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-92107a4c-6884-4312-95b8-70765b6a9f70');\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": 31
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: total number of rows (30290) exceeds max_rows (20000). Limiting to first (20000) rows.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "comp_test_outfits_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rX1El376agRl",
        "outputId": "a8c00215-cfdb-47dc-80dc-1ccd9b3e2ebd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 30290 entries, 0 to 30289\n",
            "Data columns (total 2 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   compatibility  30290 non-null  int64 \n",
            " 1   outfit_sample  30290 non-null  object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 473.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Compatibility Valid Data"
      ],
      "metadata": {
        "id": "zspbSacL9cjL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in tqdm.tqdm(np.arange(comp_valid_outfits_df.shape[0])):\n",
        "    comp_valid_outfits_df.at[i,'outfit_sample'] = remove_undesired_items(comp_valid_outfits_df.outfit_sample.iloc[i], train_test_valid = 'valid')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b182955-d4b8-40dc-fcab-5e49420e3f81",
        "id": "vZSZcAYY9cjL"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 6000/6000 [12:10<00:00,  8.21it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "comp_valid_outfits_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "8cbe3597-26ca-42a8-8a7d-7ca3def2788e",
        "id": "QVwJ7jnb9cjM"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      compatibility                                      outfit_sample\n",
              "0                 1  ['0','0','223648758_2','0','223648758_1','2236...\n",
              "1                 1  ['0','0','219849644_2','219849644_7','21984964...\n",
              "2                 1  ['208264087_4','208264087_5','208264087_2','0'...\n",
              "3                 1  ['0','135005113_4','135005113_2','135005113_5'...\n",
              "4                 1  ['0','0','216934649_2','0','216934649_1','2169...\n",
              "...             ...                                                ...\n",
              "5995              0  ['84426933_3','221864372_5','206870729_2','185...\n",
              "5996              0  ['185443035_4','162217880_5','207209295_2','0'...\n",
              "5997              0      ['0','0','214362907_1','209425151_8','0','0']\n",
              "5998              0  ['125425096_3','217512861_5','204014202_3','0'...\n",
              "5999              0  ['0','0','149847684_2','0','166260715_1','2157...\n",
              "\n",
              "[6000 rows x 2 columns]"
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              "      <th></th>\n",
              "      <th>compatibility</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>['0','0','219849644_2','219849644_7','21984964...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>['208264087_4','208264087_5','208264087_2','0'...</td>\n",
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              "      <td>['0','135005113_4','135005113_2','135005113_5'...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>['0','0','216934649_2','0','216934649_1','2169...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>['84426933_3','221864372_5','206870729_2','185...</td>\n",
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              "      <th>5996</th>\n",
              "      <td>0</td>\n",
              "      <td>['185443035_4','162217880_5','207209295_2','0'...</td>\n",
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              "    <tr>\n",
              "      <th>5997</th>\n",
              "      <td>0</td>\n",
              "      <td>['0','0','214362907_1','209425151_8','0','0']</td>\n",
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              "    <tr>\n",
              "      <th>5998</th>\n",
              "      <td>0</td>\n",
              "      <td>['125425096_3','217512861_5','204014202_3','0'...</td>\n",
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              "    <tr>\n",
              "      <th>5999</th>\n",
              "      <td>0</td>\n",
              "      <td>['0','0','149847684_2','0','166260715_1','2157...</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>6000 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6961d6f1-1e93-4e65-b750-8b92c51de59a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
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              "\n",
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              "\n",
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              "\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-6961d6f1-1e93-4e65-b750-8b92c51de59a 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-6961d6f1-1e93-4e65-b750-8b92c51de59a');\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": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "comp_valid_outfits_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04123269-d589-4be1-c266-0faa16545465",
        "id": "K54zOg819cjM"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6000 entries, 0 to 5999\n",
            "Data columns (total 2 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   compatibility  6000 non-null   int64 \n",
            " 1   outfit_sample  6000 non-null   object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 93.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save Dataframe"
      ],
      "metadata": {
        "id": "VZIGOwKKq15a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train Data"
      ],
      "metadata": {
        "id": "4Sgu_rbrrazj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# comp_train_outfits_df = comp_train_outfits_df.sample(frac=1, random_state=42).reset_index(drop=True)\n",
        "comp_train_outfits_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "d7wBrLwUXpF8",
        "outputId": "00666974-c0d7-4485-d8e6-2cf1642d4f7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       compatibility                                      outfit_sample\n",
              "0                  1  ['0','0','199244701_2','0','199244701_1','1992...\n",
              "1                  1  ['0','0','200742384_2','0','200742384_1','2007...\n",
              "2                  1  ['0','206955877_5','206955877_2','206955877_6'...\n",
              "3                  1  ['0','0','220340816_2','0','220340816_1','2203...\n",
              "4                  1  ['219393187_4','219393187_5','219393187_2','0'...\n",
              "...              ...                                                ...\n",
              "33985              0  ['0','0','221405016_1','0','218506324_1','2188...\n",
              "33986              0  ['0','0','211494606_3','0','105184990_1','2205...\n",
              "33987              0  ['85057420_4','206629909_3','132553628_1','0',...\n",
              "33988              0  ['205854787_3','212115201_6','224932578_2','0'...\n",
              "33989              0      ['0','0','119274863_2','0','0','211094404_4']\n",
              "\n",
              "[33990 rows x 2 columns]"
            ],
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              "      <th>3</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>['219393187_4','219393187_5','219393187_2','0'...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>33985</th>\n",
              "      <td>0</td>\n",
              "      <td>['0','0','221405016_1','0','218506324_1','2188...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33986</th>\n",
              "      <td>0</td>\n",
              "      <td>['0','0','211494606_3','0','105184990_1','2205...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33987</th>\n",
              "      <td>0</td>\n",
              "      <td>['85057420_4','206629909_3','132553628_1','0',...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33988</th>\n",
              "      <td>0</td>\n",
              "      <td>['205854787_3','212115201_6','224932578_2','0'...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33989</th>\n",
              "      <td>0</td>\n",
              "      <td>['0','0','119274863_2','0','0','211094404_4']</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>33990 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0b5caf3-181e-49fa-9822-afc09bb7dfd3')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a0b5caf3-181e-49fa-9822-afc09bb7dfd3 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-a0b5caf3-181e-49fa-9822-afc09bb7dfd3');\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": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "comp_train_outfits_df.to_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/Temp_Files/modified_disjoint_compatibility_train.csv', index=False)"
      ],
      "metadata": {
        "id": "jz1yOXLvY18-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test Data"
      ],
      "metadata": {
        "id": "bOkaTjaGrf_P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# comp_test_outfits_df = comp_test_outfits_df.sample(frac=1, random_state=42).reset_index(drop=True)\n",
        "comp_test_outfits_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "optm-2Soa8TH",
        "outputId": "f3fa99bc-e11c-404b-c560-362b2c5df612"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       compatibility                                      outfit_sample\n",
              "0                  1  ['222049137_3','222049137_6','222049137_1','0'...\n",
              "1                  1  ['171638917_4','171638917_5','171638917_2','0'...\n",
              "2                  1  ['209614451_3','209614451_5','209614451_2','0'...\n",
              "3                  1  ['208805759_3','0','208805759_2','0','20880575...\n",
              "4                  1      ['181727193_2','0','181727193_1','0','0','0']\n",
              "...              ...                                                ...\n",
              "30285              0  ['0','212297360_5','222058995_1','130650100_6'...\n",
              "30286              0  ['154832568_2','214779274_4','163646445_2','0'...\n",
              "30287              0  ['0','70763845_7','190480945_1','185277399_3',...\n",
              "30288              0  ['210542316_2','142695741_3','178806147_2','19...\n",
              "30289              0  ['206566023_3','221349485_5','0','0','21732398...\n",
              "\n",
              "[30290 rows x 2 columns]"
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>compatibility</th>\n",
              "      <th>outfit_sample</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>['171638917_4','171638917_5','171638917_2','0'...</td>\n",
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              "      <th>2</th>\n",
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              "      <td>['209614451_3','209614451_5','209614451_2','0'...</td>\n",
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              "      <th>3</th>\n",
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              "      <td>['208805759_3','0','208805759_2','0','20880575...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>['181727193_2','0','181727193_1','0','0','0']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>0</td>\n",
              "      <td>['154832568_2','214779274_4','163646445_2','0'...</td>\n",
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              "    <tr>\n",
              "      <th>30287</th>\n",
              "      <td>0</td>\n",
              "      <td>['0','70763845_7','190480945_1','185277399_3',...</td>\n",
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              "    <tr>\n",
              "      <th>30288</th>\n",
              "      <td>0</td>\n",
              "      <td>['210542316_2','142695741_3','178806147_2','19...</td>\n",
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              "    <tr>\n",
              "      <th>30289</th>\n",
              "      <td>0</td>\n",
              "      <td>['206566023_3','221349485_5','0','0','21732398...</td>\n",
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              "<p>30290 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2704d8d-6093-4d4b-8ad8-77e040459d8c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
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              "\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-f2704d8d-6093-4d4b-8ad8-77e040459d8c 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-f2704d8d-6093-4d4b-8ad8-77e040459d8c');\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": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "comp_test_outfits_df.to_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/Temp_Files/modified_disjoint_compatibility_test.csv', index=False)"
      ],
      "metadata": {
        "id": "sYa6eSP3bckk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Valid Data"
      ],
      "metadata": {
        "id": "BcbC6NF4tlZs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# comp_valid_outfits_df = comp_valid_outfits_df.sample(frac=1, random_state=42).reset_index(drop=True)\n",
        "comp_valid_outfits_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "0a1f0557-35f3-40b7-8c70-55d49a35c661",
        "id": "zzj2dsAM9cjM"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      compatibility                                      outfit_sample\n",
              "0                 1  ['0','0','223648758_2','0','223648758_1','2236...\n",
              "1                 1  ['0','0','219849644_2','219849644_7','21984964...\n",
              "2                 1  ['208264087_4','208264087_5','208264087_2','0'...\n",
              "3                 1  ['0','135005113_4','135005113_2','135005113_5'...\n",
              "4                 1  ['0','0','216934649_2','0','216934649_1','2169...\n",
              "...             ...                                                ...\n",
              "5995              0  ['84426933_3','221864372_5','206870729_2','185...\n",
              "5996              0  ['185443035_4','162217880_5','207209295_2','0'...\n",
              "5997              0      ['0','0','214362907_1','209425151_8','0','0']\n",
              "5998              0  ['125425096_3','217512861_5','204014202_3','0'...\n",
              "5999              0  ['0','0','149847684_2','0','166260715_1','2157...\n",
              "\n",
              "[6000 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "      <th>compatibility</th>\n",
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              "      <td>['0','0','219849644_2','219849644_7','21984964...</td>\n",
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              "      <td>['208264087_4','208264087_5','208264087_2','0'...</td>\n",
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              "      <td>['0','135005113_4','135005113_2','135005113_5'...</td>\n",
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              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>['0','0','216934649_2','0','216934649_1','2169...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>5995</th>\n",
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              "      <td>['185443035_4','162217880_5','207209295_2','0'...</td>\n",
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              "      <th>5997</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>['125425096_3','217512861_5','204014202_3','0'...</td>\n",
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              "      <th>5999</th>\n",
              "      <td>0</td>\n",
              "      <td>['0','0','149847684_2','0','166260715_1','2157...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6000 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1dd8efb1-d959-4d82-964e-bc81a2486993')\"\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 0 24 24\"\n",
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          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "comp_valid_outfits_df.to_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/Temp_Files/modified_disjoint_compatibility_valid.csv', index=False)"
      ],
      "metadata": {
        "id": "LzR3OdMO9cjM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Process Outfits"
      ],
      "metadata": {
        "id": "uyU7fJgp6l7v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Previous dfs contain only the desired items in outfits"
      ],
      "metadata": {
        "id": "oNp-9r5jGWbE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = pd.read_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/Temp_Files/modified_disjoint_compatibility_train.csv')\n",
        "train_df"
      ],
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          "height": 423
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        "id": "KcBknGozGkpW",
        "outputId": "ab752ad1-cde2-4e4c-dff5-d64d1f361af5"
      },
      "execution_count": null,
      "outputs": [
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        "test_df"
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        "id": "WUMSUS9IG6DD",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valid_df = pd.read_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/Temp_Files/modified_disjoint_compatibility_valid.csv')\n",
        "valid_df"
      ],
      "metadata": {
        "colab": {
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          "height": 423
        },
        "id": "pkGjY-oaHPHF",
        "outputId": "f49e6b98-d3b5-43d4-89d2-cc6d0d3b2fcc"
      },
      "execution_count": null,
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        {
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              "      compatibility                                      outfit_sample\n",
              "0                 1  ['0','0','223648758_2','0','223648758_1','2236...\n",
              "1                 1  ['0','0','219849644_2','219849644_7','21984964...\n",
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              "...             ...                                                ...\n",
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              "5998              0  ['125425096_3','217512861_5','204014202_3','0'...\n",
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          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What we will consider as outfits:\n",
        "1.   Normal Outfits:\n",
        "\n",
        "> * Tops\n",
        "> * Bottoms\n",
        "> * Shoes\n",
        "> * Outerwear (optional)\n",
        "> * Bags (optional)\n",
        "\n",
        "2.   All-Body Outfits:\n",
        "\n",
        "> * All-body\n",
        "> * Shoes\n",
        "> * Outerwear (optional)\n",
        "> * Bags (optional)\n"
      ],
      "metadata": {
        "id": "E4WbnJETOXT6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prepare Normal Outfits Data and All-body Outfits Data"
      ],
      "metadata": {
        "id": "COpVACWT7Ec9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "normal_outfits_df = pd.DataFrame(columns=['compatibility', 'outfit_sample'])\n",
        "allBody_outfits_df = pd.DataFrame(columns=['compatibility', 'outfit_sample'])"
      ],
      "metadata": {
        "id": "aGIoIM91J-4Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def process_outfit(outfit_sample, compatibility):\n",
        "    outfit_items = parse_sample(outfit_sample)\n",
        "    #index 0 = 'tops', 1 = 'bottoms', 2 = 'shoes', 3 = 'outerwear', 4 = 'bags', 5 = 'all-body'\n",
        "    global normal_outfits_df\n",
        "    global allBody_outfits_df\n",
        "    if outfit_items[2] != '0':\n",
        "        if outfit_items[0] != '0' and outfit_items[1] != '0':\n",
        "            normal_outfits_df = normal_outfits_df.append({'compatibility':compatibility,\n",
        "                                                          'outfit_sample':np.array2string(\n",
        "                                                              np.array(outfit_items[:-1]), \n",
        "                                                              separator=',')\n",
        "                                                          }, \n",
        "                                                         ignore_index=True)\n",
        "        elif outfit_items[5] != '0':\n",
        "            allBody_outfits_df = allBody_outfits_df.append({'compatibility':compatibility,\n",
        "                                                            'outfit_sample':np.array2string(\n",
        "                                                                np.array(outfit_items[-1:-5:-1]),\n",
        "                                                                separator=',')\n",
        "                                                            },\n",
        "                                                           ignore_index=True) \n",
        "\n",
        "    \n"
      ],
      "metadata": {
        "id": "korN0OS09cjN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Process Train Outfits"
      ],
      "metadata": {
        "id": "sNYiFZzYeJvM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in tqdm.tqdm(np.arange(train_df.shape[0])):\n",
        "    compatibility, outfit_sample = train_df.iloc[i]\n",
        "    process_outfit(outfit_sample, compatibility)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7UBF-PSrePEL",
        "outputId": "2de66a57-b6dc-4603-ed55-71fb22716037"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 33990/33990 [01:12<00:00, 465.86it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normal_outfits_df"
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        "id": "EGa8wIjBgGB0",
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      "execution_count": null,
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              "      compatibility                                      outfit_sample\n",
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              "          const element = document.querySelector('#df-8735739f-ffce-437b-895d-8b36ef9bf6ac');\n",
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              "            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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "allBody_outfits_df"
      ],
      "metadata": {
        "colab": {
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          "height": 423
        },
        "id": "UgmfqDjsgJZo",
        "outputId": "d2b26d50-5398-410e-eb17-cd978eb0335c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      compatibility                                    outfit_sample\n",
              "0                 1  ['199244701_6','199244701_1','0','199244701_2']\n",
              "1                 1  ['200742384_5','200742384_1','0','200742384_2']\n",
              "2                 1  ['220340816_5','220340816_1','0','220340816_2']\n",
              "3                 1  ['217183057_4','217183057_1','0','217183057_2']\n",
              "4                 1  ['199287279_5','199287279_1','0','199287279_2']\n",
              "...             ...                                              ...\n",
              "11508             0  ['158651152_2','219887726_1','0','203351828_2']\n",
              "11509             0  ['222516864_4','173244929_1','0','208172613_2']\n",
              "11510             0  ['218864675_6','218506324_1','0','221405016_1']\n",
              "11511             0  ['220595501_3','105184990_1','0','211494606_3']\n",
              "11512             0            ['211094404_4','0','0','119274863_2']\n",
              "\n",
              "[11513 rows x 2 columns]"
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              "    </tr>\n",
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              "      <td>0</td>\n",
              "      <td>['158651152_2','219887726_1','0','203351828_2']</td>\n",
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              "      <th>11509</th>\n",
              "      <td>0</td>\n",
              "      <td>['222516864_4','173244929_1','0','208172613_2']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11510</th>\n",
              "      <td>0</td>\n",
              "      <td>['218864675_6','218506324_1','0','221405016_1']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11511</th>\n",
              "      <td>0</td>\n",
              "      <td>['220595501_3','105184990_1','0','211494606_3']</td>\n",
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              "    <tr>\n",
              "      <th>11512</th>\n",
              "      <td>0</td>\n",
              "      <td>['211094404_4','0','0','119274863_2']</td>\n",
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              "<p>11513 rows × 2 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Save Normal Outfits Train Data"
      ],
      "metadata": {
        "id": "ApXropuViSz_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "normal_outfits_df.to_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/Normal_Outfits_Data/train_df.csv', index=False)"
      ],
      "metadata": {
        "id": "C9ziy0SPibJB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Save AllBody Outfits Train Data"
      ],
      "metadata": {
        "id": "24EBMPs8i6YN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "allBody_outfits_df.to_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/AllBody_Outfits_Data/train_df.csv', index=False)"
      ],
      "metadata": {
        "id": "Ty0tAh8JjK69"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "FnHkT-YQlC0g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Process Test Outfits"
      ],
      "metadata": {
        "id": "ckf47pm-lDm9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "normal_outfits_df = pd.DataFrame(columns=['compatibility', 'outfit_sample'])\n",
        "allBody_outfits_df = pd.DataFrame(columns=['compatibility', 'outfit_sample'])"
      ],
      "metadata": {
        "id": "lsETzkqYlZ8q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in tqdm.tqdm(np.arange(test_df.shape[0])):\n",
        "    compatibility, outfit_sample = test_df.iloc[i]\n",
        "    process_outfit(outfit_sample, compatibility)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2be65f2-02cc-47bf-b08d-aaa8a2edee6f",
        "id": "0cq8zlhVlDm9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 30290/30290 [01:01<00:00, 493.40it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normal_outfits_df"
      ],
      "metadata": {
        "colab": {
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          "height": 423
        },
        "outputId": "d03f4249-736e-44ab-e363-ce0e8c9ff9f4",
        "id": "fhooGLG0lDm-"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      compatibility                                      outfit_sample\n",
              "0                 1  ['222049137_3','222049137_6','222049137_1','0'...\n",
              "1                 1  ['171638917_4','171638917_5','171638917_2','0'...\n",
              "2                 1  ['209614451_3','209614451_5','209614451_2','0'...\n",
              "3                 1  ['157135030_3','157135030_5','157135030_2','0'...\n",
              "4                 1  ['163905816_7','163905816_10','163905816_2','0...\n",
              "...             ...                                                ...\n",
              "13232             0  ['221942012_5','163248533_6','183162091_1','0'...\n",
              "13233             0  ['143700091_4','117379950_6','222301575_2','19...\n",
              "13234             0  ['202402701_4','218309462_3','183005681_2','0'...\n",
              "13235             0  ['154832568_2','214779274_4','163646445_2','0'...\n",
              "13236             0  ['210542316_2','142695741_3','178806147_2','19...\n",
              "\n",
              "[13237 rows x 2 columns]"
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              "          document.querySelector('#df-6d573e65-c2ee-41ce-baa2-090ce150b8e6 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-6d573e65-c2ee-41ce-baa2-090ce150b8e6');\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": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "allBody_outfits_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "f97136a7-2e08-4b54-da0c-3d74a022faff",
        "id": "vEC9_X_vlDm-"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     compatibility                                      outfit_sample\n",
              "0                1  ['219905886_6','219905886_1','219905886_7','21...\n",
              "1                1                ['17682761_4','0','0','17682761_1']\n",
              "2                1                ['96522071_4','0','0','96522071_1']\n",
              "3                1  ['74477030_6','74477030_1','74477030_8','74477...\n",
              "4                1    ['196794889_6','196794889_1','0','196794889_2']\n",
              "...            ...                                                ...\n",
              "9306             0    ['212556911_4','223797921_1','0','218903300_2']\n",
              "9307             0      ['139981254_5','85586321_1','0','25253122_2']\n",
              "9308             0               ['210583888_5','0','0','72403647_2']\n",
              "9309             0              ['153181442_2','0','0','126141821_1']\n",
              "9310             0    ['215076467_2','146987378_1','0','220812780_2']\n",
              "\n",
              "[9311 rows x 2 columns]"
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              "    </tr>\n",
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              "      <th>...</th>\n",
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              "      <td>0</td>\n",
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              "      <th>9308</th>\n",
              "      <td>0</td>\n",
              "      <td>['210583888_5','0','0','72403647_2']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9309</th>\n",
              "      <td>0</td>\n",
              "      <td>['153181442_2','0','0','126141821_1']</td>\n",
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              "<p>9311 rows × 2 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75224384-807a-4790-bb27-95cf89ef8338')\"\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-75224384-807a-4790-bb27-95cf89ef8338');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Save Normal Outfits Test Data"
      ],
      "metadata": {
        "id": "p49ny_71lDm-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "normal_outfits_df.to_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/Normal_Outfits_Data/test_df.csv', index=False)"
      ],
      "metadata": {
        "id": "ua6mXNRVlDm-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Save AllBody Outfits Test Data"
      ],
      "metadata": {
        "id": "lYaeCFEYlDm-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "allBody_outfits_df.to_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/AllBody_Outfits_Data/test_df.csv', index=False)"
      ],
      "metadata": {
        "id": "cf_nCx_KlDm-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Ma9xQinznhv0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Process Valid Outfits"
      ],
      "metadata": {
        "id": "pQAmlK5hnpt9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "normal_outfits_df = pd.DataFrame(columns=['compatibility', 'outfit_sample'])\n",
        "allBody_outfits_df = pd.DataFrame(columns=['compatibility', 'outfit_sample'])"
      ],
      "metadata": {
        "id": "8BibXU14npt9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in tqdm.tqdm(np.arange(valid_df.shape[0])):\n",
        "    compatibility, outfit_sample = valid_df.iloc[i]\n",
        "    process_outfit(outfit_sample, compatibility)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "440453a0-480b-4340-b4c8-42b0cc1ec6c8",
        "id": "KDozGYlMnpt-"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 6000/6000 [00:12<00:00, 475.03it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normal_outfits_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "5c56f47a-40be-4650-e35c-586368321be3",
        "id": "h4_y3CBfnpt-"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     compatibility                                      outfit_sample\n",
              "0                1  ['208264087_4','208264087_5','208264087_2','0'...\n",
              "1                1  ['213647311_3','213647311_4','213647311_2','0'...\n",
              "2                1  ['201941341_3','201941341_4','201941341_2','0'...\n",
              "3                1  ['223690855_4','223690855_6','223690855_2','0'...\n",
              "4                1  ['191633508_2','191633508_4','191633508_1','19...\n",
              "...            ...                                                ...\n",
              "2847             0  ['217738041_4','223640936_4','222867317_3','0'...\n",
              "2848             0  ['217895694_2','222730603_6','198475603_3','20...\n",
              "2849             0  ['84426933_3','221864372_5','206870729_2','185...\n",
              "2850             0  ['185443035_4','162217880_5','207209295_2','0'...\n",
              "2851             0  ['125425096_3','217512861_5','204014202_3','0'...\n",
              "\n",
              "[2852 rows x 2 columns]"
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              "                                                     [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": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "allBody_outfits_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "a0381bd2-11d8-4df3-f3f1-7a7c0b28d467",
        "id": "TcmZ4gaZnpt-"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     compatibility                                      outfit_sample\n",
              "0                1    ['223648758_4','223648758_1','0','223648758_2']\n",
              "1                1  ['219849644_6','219849644_1','219849644_7','21...\n",
              "2                1    ['216934649_4','216934649_1','0','216934649_2']\n",
              "3                1    ['224937918_3','224937918_1','0','224937918_2']\n",
              "4                1    ['156821607_5','156821607_1','0','156821607_2']\n",
              "...            ...                                                ...\n",
              "2039             0    ['217383959_2','106671225_1','0','212977417_2']\n",
              "2040             0    ['220788406_5','212426726_1','0','201293621_2']\n",
              "2041             0    ['223815606_3','222871178_1','0','213204699_2']\n",
              "2042             0    ['218409103_6','220862629_1','0','219569732_1']\n",
              "2043             0    ['215779836_6','166260715_1','0','149847684_2']\n",
              "\n",
              "[2044 rows x 2 columns]"
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              "      <th></th>\n",
              "      <th>compatibility</th>\n",
              "      <th>outfit_sample</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <th>1</th>\n",
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              "      <td>['219849644_6','219849644_1','219849644_7','21...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>['216934649_4','216934649_1','0','216934649_2']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>['224937918_3','224937918_1','0','224937918_2']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>['156821607_5','156821607_1','0','156821607_2']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2039</th>\n",
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              "      <td>['217383959_2','106671225_1','0','212977417_2']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2040</th>\n",
              "      <td>0</td>\n",
              "      <td>['220788406_5','212426726_1','0','201293621_2']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2041</th>\n",
              "      <td>0</td>\n",
              "      <td>['223815606_3','222871178_1','0','213204699_2']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2042</th>\n",
              "      <td>0</td>\n",
              "      <td>['218409103_6','220862629_1','0','219569732_1']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2043</th>\n",
              "      <td>0</td>\n",
              "      <td>['215779836_6','166260715_1','0','149847684_2']</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2044 rows × 2 columns</p>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
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              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "    .colab-df-convert {\n",
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              "      border: none;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6a32f21b-6cfc-4ca6-8f71-bb9e396f28d7 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-6a32f21b-6cfc-4ca6-8f71-bb9e396f28d7');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Save Normal Outfits Valid Data"
      ],
      "metadata": {
        "id": "R2dn4Ej4npt-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "normal_outfits_df.to_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/Normal_Outfits_Data/valid_df.csv', index=False)"
      ],
      "metadata": {
        "id": "-1fDih04npt-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Save AllBody Outfits Valid Data"
      ],
      "metadata": {
        "id": "59XnybVJnpt-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "allBody_outfits_df.to_csv('/content/drive/MyDrive/Outfit_Recommendation_Project/AllBody_Outfits_Data/valid_df.csv', index=False)"
      ],
      "metadata": {
        "id": "vwhPbVJjnpt_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "fuMFuZwXnpt_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}