MLD_Project_Triplet_Mining.ipynb

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      "execution_count": null,
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          "text": [
            "Downloading fashion-product-images-small.zip to /content\n",
            " 96% 545M/565M [00:08<00:00, 81.6MB/s]\n",
            "100% 565M/565M [00:08<00:00, 68.3MB/s]\n"
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      },
      "execution_count": null,
      "outputs": []
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      "execution_count": null,
      "outputs": []
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              "      id gender masterCategory subCategory  articleType baseColour  season  \\\n",
              "0  15970    Men        Apparel     Topwear       Shirts  Navy Blue    Fall   \n",
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              "4  53759    Men        Apparel     Topwear      Tshirts       Grey  Summer   \n",
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              "     year   usage                             productDisplayName  \n",
              "0  2011.0  Casual               Turtle Check Men Navy Blue Shirt  \n",
              "1  2012.0  Casual             Peter England Men Party Blue Jeans  \n",
              "2  2016.0  Casual                       Titan Women Silver Watch  \n",
              "3  2011.0  Casual  Manchester United Men Solid Black Track Pants  \n",
              "4  2012.0  Casual                          Puma Men Grey T-shirt  "
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-d308f6e0-930b-44ce-b104-9ad30eeb6c14 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-d308f6e0-930b-44ce-b104-9ad30eeb6c14');\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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YGrf8wXPzwGM",
        "outputId": "6da8a0b6-6e9d-4c22-9a29-e793fb9f1f21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 44424 entries, 0 to 44423\n",
            "Data columns (total 10 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   id                  44424 non-null  int64  \n",
            " 1   gender              44424 non-null  object \n",
            " 2   masterCategory      44424 non-null  object \n",
            " 3   subCategory         44424 non-null  object \n",
            " 4   articleType         44424 non-null  object \n",
            " 5   baseColour          44409 non-null  object \n",
            " 6   season              44403 non-null  object \n",
            " 7   year                44423 non-null  float64\n",
            " 8   usage               44107 non-null  object \n",
            " 9   productDisplayName  44417 non-null  object \n",
            "dtypes: float64(1), int64(1), object(8)\n",
            "memory usage: 3.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.dropna()\n",
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "14fIG6DB1Yzr",
        "outputId": "9b31ef0b-36a4-4f5a-cd5c-cd8ed77faea1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 44077 entries, 0 to 44423\n",
            "Data columns (total 10 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   id                  44077 non-null  int64  \n",
            " 1   gender              44077 non-null  object \n",
            " 2   masterCategory      44077 non-null  object \n",
            " 3   subCategory         44077 non-null  object \n",
            " 4   articleType         44077 non-null  object \n",
            " 5   baseColour          44077 non-null  object \n",
            " 6   season              44077 non-null  object \n",
            " 7   year                44077 non-null  float64\n",
            " 8   usage               44077 non-null  object \n",
            " 9   productDisplayName  44077 non-null  object \n",
            "dtypes: float64(1), int64(1), object(8)\n",
            "memory usage: 3.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Clean, Filter and Analyze Data"
      ],
      "metadata": {
        "id": "Ve6moFE4H9gj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "articles_to_keep= [article for article,_ in  Counter(data['articleType']).most_common(22)]"
      ],
      "metadata": {
        "id": "ZvPZfwl30iQ4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = data[data['articleType'].isin(articles_to_keep)].reset_index(drop=True)\n",
        "data = data[data['gender'].isin([\"Men\",\"Women\"])].reset_index(drop=True)\n",
        "data = data[data['masterCategory']!='Free Items'].reset_index(drop=True)\n",
        "data = data[data['usage'].isin([\"Casual\",\"Sports\",\"Formal\",\"Ethnic\"])].reset_index(drop=True)\n"
      ],
      "metadata": {
        "id": "Xilx1LyZ4iUi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Counter(data['gender'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9RWi0G5aZbkN",
        "outputId": "aeac6530-bd1c-42fb-c9f3-50a4a23b1ec1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({'Men': 19494, 'Women': 11680})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Counter(data['masterCategory'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SUrbKP5yd2zF",
        "outputId": "fd6e2d7f-adf9-4545-983d-025889b70826"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({'Apparel': 15224,\n",
              "         'Accessories': 7367,\n",
              "         'Footwear': 7990,\n",
              "         'Personal Care': 593})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Counter(data['subCategory']).most_common(25)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qUpSAH1ZD65x",
        "outputId": "e74b1edd-a2a3-40ae-c6bb-0fc4ed94de6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Topwear', 12846),\n",
              " ('Shoes', 6499),\n",
              " ('Watches', 2351),\n",
              " ('Bags', 1779),\n",
              " ('Bottomwear', 1529),\n",
              " ('Eyewear', 920),\n",
              " ('Wallets', 901),\n",
              " ('Innerwear', 847),\n",
              " ('Belts', 801),\n",
              " ('Sandal', 748),\n",
              " ('Flip Flops', 743),\n",
              " ('Socks', 614),\n",
              " ('Fragrance', 588),\n",
              " ('Perfumes', 6),\n",
              " ('Loungewear and Nightwear', 2)]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.loc[data['subCategory']=='Loungewear and Nightwear','subCategory'] = 'Innerwear'"
      ],
      "metadata": {
        "id": "D61iXwrgETzp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.loc[data['subCategory']=='Perfumes','subCategory'] = 'Fragrance'"
      ],
      "metadata": {
        "id": "148D9yt1EdxB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Counter(data['subCategory']).most_common(25)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H3m127jLEkbB",
        "outputId": "2b2f6795-07f1-4163-e1ec-963a9f0b9523"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Topwear', 12846),\n",
              " ('Shoes', 6499),\n",
              " ('Watches', 2351),\n",
              " ('Bags', 1779),\n",
              " ('Bottomwear', 1529),\n",
              " ('Eyewear', 920),\n",
              " ('Wallets', 901),\n",
              " ('Innerwear', 849),\n",
              " ('Belts', 801),\n",
              " ('Sandal', 748),\n",
              " ('Flip Flops', 743),\n",
              " ('Socks', 614),\n",
              " ('Fragrance', 594)]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Counter(data['usage']).most_common(25)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bpcDn_sPH34_",
        "outputId": "d89381a8-7333-4fbb-a97d-0917a7b4c469"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Casual', 24025), ('Sports', 3251), ('Formal', 1990), ('Ethnic', 1908)]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "articles_to_keep"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KrSh6jwCQG7N",
        "outputId": "0bd1cabc-d27c-49e0-fe3e-28f2d591a5f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Tshirts',\n",
              " 'Shirts',\n",
              " 'Casual Shoes',\n",
              " 'Watches',\n",
              " 'Sports Shoes',\n",
              " 'Kurtas',\n",
              " 'Tops',\n",
              " 'Handbags',\n",
              " 'Heels',\n",
              " 'Sunglasses',\n",
              " 'Wallets',\n",
              " 'Flip Flops',\n",
              " 'Sandals',\n",
              " 'Briefs',\n",
              " 'Belts',\n",
              " 'Backpacks',\n",
              " 'Socks',\n",
              " 'Formal Shoes',\n",
              " 'Jeans',\n",
              " 'Perfume and Body Mist',\n",
              " 'Shorts',\n",
              " 'Trousers']"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "tqdm.pandas()"
      ],
      "metadata": {
        "id": "5qpxORfEHADs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "descriptions_without_gender = []\n",
        "for idx,item in tqdm(data.iterrows(),total=data.shape[0]):\n",
        "  try:\n",
        "    data.loc[idx,'productDisplayName'] = item['productDisplayName'][item['productDisplayName'].index(item['gender']):]\n",
        "  except:\n",
        "    descriptions_without_gender.append(idx)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "3399016d00fe4c4aa427c35ac40f8a0c",
            "f24c83f3b9ba4add8261cb2bd090c5a7",
            "b6da0fc249af43b4b61678587c6b9e15",
            "91bf8b72e54243abbc54b26078252de6",
            "3b4426868b54432bbad839e4b5d40547",
            "41be9bc1247a47c8ad33188fd4975ed7",
            "25224d198c2c48468faa1bac3bf99573",
            "6597df7b40ee4afe9e1e70d84060703f",
            "f7a50c37357e423c8554519a432928b1",
            "151f9a2e09c3444b83a8d1ffc4f91265",
            "7651fda2dea344ea870c8fd3f91d499a"
          ]
        },
        "id": "PDobKGc6Fr9B",
        "outputId": "5202a7e0-486c-46e0-de23-afd69d571778"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/31174 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3399016d00fe4c4aa427c35ac40f8a0c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(descriptions_without_gender)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uklt2DUJIbYg",
        "outputId": "6454b641-0aeb-42ae-e0c7-afce18002b57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "874"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.drop(labels=descriptions_without_gender,axis=0).reset_index(drop=True)"
      ],
      "metadata": {
        "id": "mx-87qHzIeVa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fhMJ6QM4Ihwt",
        "outputId": "fd968fb7-17d2-4ef2-f36f-ee1ebcdde035"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 30300 entries, 0 to 30299\n",
            "Data columns (total 10 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   id                  30300 non-null  int64  \n",
            " 1   gender              30300 non-null  object \n",
            " 2   masterCategory      30300 non-null  object \n",
            " 3   subCategory         30300 non-null  object \n",
            " 4   articleType         30300 non-null  object \n",
            " 5   baseColour          30300 non-null  object \n",
            " 6   season              30300 non-null  object \n",
            " 7   year                30300 non-null  float64\n",
            " 8   usage               30300 non-null  object \n",
            " 9   productDisplayName  30300 non-null  object \n",
            "dtypes: float64(1), int64(1), object(8)\n",
            "memory usage: 2.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "first_word = []\n",
        "\n",
        "for idx,item in tqdm(data.iterrows(),total=data.shape[0]):\n",
        "  try:\n",
        "    first_word.append(item['productDisplayName'].split(' ')[0])\n",
        "  except:\n",
        "    pass\n",
        "\n",
        "Counter(first_word)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263,
          "referenced_widgets": [
            "632f5a6e69e9421cbe878eed139de74c",
            "1a49dc0ab03441ad9821640dfc8d4114",
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            "0f634393a45a42e0bda5ecf656c7cd2b",
            "d38285d519db46b5af45c6119712cec3",
            "ea37dff3d31842d1b5bbd47b04b1287c",
            "62ffa29e3bbc46eeb2beb43dd5cc9c36",
            "dbfadffc5567452d8e957c7656dda2f1",
            "32f586cc43db4cceb56833cafe6d9a40",
            "c973e43d0079423ab98c3a4e0e4d11c7",
            "bb687354925a42a1a8b9149fb94e5f6d"
          ]
        },
        "id": "ObLKM26_I0HL",
        "outputId": "52f8935a-5939-4605-8e1a-62cd6d68dc26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/30300 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "632f5a6e69e9421cbe878eed139de74c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({'Men': 16675,\n",
              "         'Women': 10717,\n",
              "         'Mens': 185,\n",
              "         \"Men's\": 2236,\n",
              "         \"Women's\": 444,\n",
              "         'Womens': 37,\n",
              "         'MenCasual': 1,\n",
              "         'Women.': 1,\n",
              "         'MenNavy': 1,\n",
              "         'MenLime': 1,\n",
              "         'MenSpeeder': 1,\n",
              "         'MenShape-Ups': 1})"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for idx,item in tqdm(data.iterrows(),total=data.shape[0]):\n",
        "\n",
        "  try:\n",
        "    if 'Mens' in item['productDisplayName']:\n",
        "      data.loc[idx,'productDisplayName'] = item['productDisplayName'].replace(\"Mens\",\"Men\")\n",
        "\n",
        "    if \"Men's\" in item['productDisplayName']:\n",
        "      data.loc[idx,'productDisplayName'] = item['productDisplayName'].replace(\"Men's\",\"Men\")\n",
        "\n",
        "    if 'MenCasual' in item['productDisplayName']:\n",
        "      data.loc[idx,'productDisplayName'] = item['productDisplayName'].replace(\"MenCasual\",\"Men\")\n",
        "\n",
        "    if 'MenNavy' in item['productDisplayName']:\n",
        "      data.loc[idx,'productDisplayName'] = item['productDisplayName'].replace(\"MenNavy\",\"Men\")\n",
        "\n",
        "    if 'MenLime' in item['productDisplayName']:\n",
        "      data.loc[idx,'productDisplayName'] = item['productDisplayName'].replace(\"MenLime\",\"Men\")\n",
        "\n",
        "    if 'MenSpeeder' in item['productDisplayName']:\n",
        "      data.loc[idx,'productDisplayName'] = item['productDisplayName'].replace(\"MenSpeeder\",\"Men\")\n",
        "\n",
        "    if 'MenShape-Ups' in item['productDisplayName']:\n",
        "      data.loc[idx,'productDisplayName'] = item['productDisplayName'].replace(\"MenShape-Ups\",\"Men\")\n",
        "\n",
        "    if \"Women's\" in item['productDisplayName']:\n",
        "      data.loc[idx,'productDisplayName'] = item['productDisplayName'].replace(\"Women's\",\"Women\")\n",
        "    \n",
        "    if 'Womens' in item['productDisplayName']:\n",
        "      data.loc[idx,'productDisplayName'] = item['productDisplayName'].replace(\"Womens\",\"Women\")\n",
        "\n",
        "    if 'Women.' in item['productDisplayName']:\n",
        "      data.loc[idx,'productDisplayName'] = item['productDisplayName'].replace(\"Women.\",\"Women\")\n",
        "    \n",
        "  except:\n",
        "    pass\n",
        "\n",
        "first_word = []\n",
        "\n",
        "for idx,item in tqdm(data.iterrows(),total=data.shape[0]):\n",
        "  try:\n",
        "    first_word.append(item['productDisplayName'].split(' ')[0])\n",
        "  except:\n",
        "    pass\n",
        "\n",
        "Counter(first_word)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 99,
          "referenced_widgets": [
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            "f4260987f08a4cf7a6d765298da2aba1"
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        "outputId": "69603c3e-d3f5-47f1-bcd1-3f649337ac2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/30300 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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        {
          "output_type": "display_data",
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              "  0%|          | 0/30300 [00:00<?, ?it/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({'Men': 19101, 'Women': 11199})"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a='Men Navy Blue Shirt'.split(' ')\n",
        "a.insert(1,'Casual')\n",
        "a=\" \".join(a)\n",
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "rHnkmxCsOgJI",
        "outputId": "358ad692-c7b4-40bc-cc7a-8cb57c1f2236"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Men Casual Navy Blue Shirt'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "descriptions_without_gender = []\n",
        "for idx,item in tqdm(data.iterrows(),total=data.shape[0]):\n",
        "  try:\n",
        "    s = item['productDisplayName'].split(' ')\n",
        "    s.insert(1,item['usage'])\n",
        "    s = \" \".join(s)\n",
        "    data.loc[idx,'productDisplayName'] = s\n",
        "  except:\n",
        "    descriptions_without_gender.append(idx)\n",
        "\n",
        "descriptions_without_gender"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/30300 [00:00<?, ?it/s]"
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          "output_type": "execute_result",
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              "[]"
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          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_01t-CPRHcun",
        "outputId": "ac33df26-8e49-4380-b132-99c73439d916"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id gender masterCategory subCategory articleType baseColour  season  \\\n",
              "0  15970    Men        Apparel     Topwear      Shirts  Navy Blue    Fall   \n",
              "1  39386    Men        Apparel  Bottomwear       Jeans       Blue  Summer   \n",
              "2  59263  Women    Accessories     Watches     Watches     Silver  Winter   \n",
              "3  53759    Men        Apparel     Topwear     Tshirts       Grey  Summer   \n",
              "4   1855    Men        Apparel     Topwear     Tshirts       Grey  Summer   \n",
              "\n",
              "     year   usage                 productDisplayName  \n",
              "0  2011.0  Casual         Men Casual Navy Blue Shirt  \n",
              "1  2012.0  Casual        Men Casual Party Blue Jeans  \n",
              "2  2016.0  Casual          Women Casual Silver Watch  \n",
              "3  2012.0  Casual            Men Casual Grey T-shirt  \n",
              "4  2011.0  Casual  Men Casual Chain Reaction T-shirt  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-99edba20-91af-4114-b8b6-38e3a6040617\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th>id</th>\n",
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              "      <td>2012.0</td>\n",
              "      <td>Casual</td>\n",
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              "    </tr>\n",
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              "      <td>59263</td>\n",
              "      <td>Women</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>Watches</td>\n",
              "      <td>Watches</td>\n",
              "      <td>Silver</td>\n",
              "      <td>Winter</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Women Casual Silver Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53759</td>\n",
              "      <td>Men</td>\n",
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              "      <td>Topwear</td>\n",
              "      <td>Tshirts</td>\n",
              "      <td>Grey</td>\n",
              "      <td>Summer</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Men Casual Grey T-shirt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1855</td>\n",
              "      <td>Men</td>\n",
              "      <td>Apparel</td>\n",
              "      <td>Topwear</td>\n",
              "      <td>Tshirts</td>\n",
              "      <td>Grey</td>\n",
              "      <td>Summer</td>\n",
              "      <td>2011.0</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Men Casual Chain Reaction T-shirt</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-99edba20-91af-4114-b8b6-38e3a6040617')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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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",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for value in set(data['articleType']):\n",
        "  print(value)\n",
        "  print(data[data['articleType']==value].groupby(by='gender')['gender'].count())\n",
        "  print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ECq_W4PoQbtm",
        "outputId": "d9eabb0c-54b1-4baf-fe57-cd7e9094559f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sports Shoes\n",
            "gender\n",
            "Men      1542\n",
            "Women     385\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Briefs\n",
            "gender\n",
            "Men      557\n",
            "Women    242\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Tops\n",
            "gender\n",
            "Men         1\n",
            "Women    1370\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Watches\n",
            "gender\n",
            "Men      1449\n",
            "Women     874\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Shorts\n",
            "gender\n",
            "Men      335\n",
            "Women     58\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Formal Shoes\n",
            "gender\n",
            "Men    627\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Backpacks\n",
            "gender\n",
            "Men      53\n",
            "Women    32\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Handbags\n",
            "gender\n",
            "Men         4\n",
            "Women    1628\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Flip Flops\n",
            "gender\n",
            "Men      493\n",
            "Women    247\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Jeans\n",
            "gender\n",
            "Men      314\n",
            "Women    221\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Perfume and Body Mist\n",
            "gender\n",
            "Men      323\n",
            "Women    268\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Casual Shoes\n",
            "gender\n",
            "Men      2231\n",
            "Women     327\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Sandals\n",
            "gender\n",
            "Men      745\n",
            "Women      2\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Belts\n",
            "gender\n",
            "Men      538\n",
            "Women    263\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Shirts\n",
            "gender\n",
            "Men      2740\n",
            "Women     211\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Socks\n",
            "gender\n",
            "Men      535\n",
            "Women     79\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Trousers\n",
            "gender\n",
            "Men      416\n",
            "Women     62\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Kurtas\n",
            "gender\n",
            "Men        78\n",
            "Women    1746\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Sunglasses\n",
            "gender\n",
            "Men      596\n",
            "Women    322\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Tshirts\n",
            "gender\n",
            "Men      5094\n",
            "Women    1086\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Wallets\n",
            "gender\n",
            "Men      430\n",
            "Women    464\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Heels\n",
            "gender\n",
            "Women    1312\n",
            "Name: gender, dtype: int64\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for value in set(data['articleType']):\n",
        "  print(value)\n",
        "  print(data[data['articleType']==value].groupby(by='gender')['gender'].count())\n",
        "  print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qxJI1oXSTCRh",
        "outputId": "53ec5c8a-fe96-44b2-d9af-7e2a20fb5f84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sports Shoes\n",
            "gender\n",
            "Men      1542\n",
            "Women     385\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Briefs\n",
            "gender\n",
            "Men      557\n",
            "Women    242\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Tops\n",
            "gender\n",
            "Men         1\n",
            "Women    1370\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Watches\n",
            "gender\n",
            "Men      1449\n",
            "Women     874\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Shorts\n",
            "gender\n",
            "Men      335\n",
            "Women     58\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Formal Shoes\n",
            "gender\n",
            "Men    627\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Backpacks\n",
            "gender\n",
            "Men      53\n",
            "Women    32\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Handbags\n",
            "gender\n",
            "Men         4\n",
            "Women    1628\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Flip Flops\n",
            "gender\n",
            "Men      493\n",
            "Women    247\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Jeans\n",
            "gender\n",
            "Men      314\n",
            "Women    221\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Perfume and Body Mist\n",
            "gender\n",
            "Men      323\n",
            "Women    268\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Casual Shoes\n",
            "gender\n",
            "Men      2231\n",
            "Women     327\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Sandals\n",
            "gender\n",
            "Men      745\n",
            "Women      2\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Belts\n",
            "gender\n",
            "Men      538\n",
            "Women    263\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Shirts\n",
            "gender\n",
            "Men      2740\n",
            "Women     211\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Socks\n",
            "gender\n",
            "Men      535\n",
            "Women     79\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Trousers\n",
            "gender\n",
            "Men      416\n",
            "Women     62\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Kurtas\n",
            "gender\n",
            "Men        78\n",
            "Women    1746\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Sunglasses\n",
            "gender\n",
            "Men      596\n",
            "Women    322\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Tshirts\n",
            "gender\n",
            "Men      5094\n",
            "Women    1086\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Wallets\n",
            "gender\n",
            "Men      430\n",
            "Women    464\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "Heels\n",
            "gender\n",
            "Women    1312\n",
            "Name: gender, dtype: int64\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "colour_not_in_product_descp = []\n",
        "\n",
        "for idx,item in tqdm(data.iterrows(),total = data.shape[0]):\n",
        "  if item['gender'] in item['productDisplayName']:\n",
        "    pass\n",
        "  \n",
        "  else:\n",
        "    colour_not_in_product_descp.append(idx)\n",
        "\n",
        "colour_not_in_product_descp\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "fb0e680396464ec2a6fec9deda53523a",
            "01ea066e37cf458a90a5a14d27da1670",
            "ffb766a0b5f24ba7b38bff8f90a532d9",
            "a28a1b3d027d4a379c1e6c56129c9882",
            "c273f478f08d457f9f25a72cfa451142",
            "5aaf9ccb6e2e45cbb1c7e89d4d7c78b1",
            "7ac504ac1f9c4d72b799ef39c2ea70db",
            "12205dfd11c24de5ab25e1b4ea9e7502",
            "d10d545b7dd840b7bd19fc7f43a37ee6",
            "1e188210c0a148d8a319d86a9a9d9daa",
            "7e0f50d34d4a4ea58249a05e2eae9fb3"
          ]
        },
        "id": "xZkQrPG1UnTL",
        "outputId": "5e8f942a-5fb8-4967-cd6f-d9411713c8fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/30300 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fb0e680396464ec2a6fec9deda53523a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for value in set(data['articleType']):\n",
        "  print(value)\n",
        "  print(data[data['articleType']==value].groupby(by='baseColour')['baseColour'].count())\n",
        "  print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OcIob3yfSw4B",
        "outputId": "4f69aa3a-5ed3-42d0-dd1f-5db3818c9da6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sports Shoes\n",
            "baseColour\n",
            "Beige                  3\n",
            "Black                521\n",
            "Blue                  89\n",
            "Bronze                 1\n",
            "Brown                 23\n",
            "Charcoal               7\n",
            "Cream                  1\n",
            "Fluorescent Green      2\n",
            "Green                  9\n",
            "Grey                 209\n",
            "Khaki                  1\n",
            "Lavender               1\n",
            "Maroon                 2\n",
            "Multi                  1\n",
            "Navy Blue             37\n",
            "Olive                  4\n",
            "Orange                 9\n",
            "Pink                  12\n",
            "Purple                16\n",
            "Red                   30\n",
            "Sea Green              1\n",
            "Silver                72\n",
            "White                868\n",
            "Yellow                 8\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Briefs\n",
            "baseColour\n",
            "Beige               2\n",
            "Black             131\n",
            "Blue               68\n",
            "Brown              22\n",
            "Burgundy            5\n",
            "Charcoal           39\n",
            "Cream               5\n",
            "Gold                2\n",
            "Green              17\n",
            "Grey               88\n",
            "Grey Melange       48\n",
            "Khaki               4\n",
            "Lavender            5\n",
            "Maroon             15\n",
            "Mauve               1\n",
            "Multi              12\n",
            "Navy Blue          89\n",
            "Nude                1\n",
            "Off White           1\n",
            "Olive              11\n",
            "Orange              3\n",
            "Peach               4\n",
            "Pink               34\n",
            "Purple             12\n",
            "Red                52\n",
            "Rose                2\n",
            "Skin                7\n",
            "Taupe               2\n",
            "Turquoise Blue      1\n",
            "White             111\n",
            "Yellow              5\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Tops\n",
            "baseColour\n",
            "Beige              26\n",
            "Black             202\n",
            "Blue              147\n",
            "Brown              33\n",
            "Burgundy            2\n",
            "Charcoal            2\n",
            "Cream              27\n",
            "Green              82\n",
            "Grey               88\n",
            "Grey Melange       10\n",
            "Lavender           13\n",
            "Lime Green          1\n",
            "Magenta            21\n",
            "Maroon             10\n",
            "Mauve               1\n",
            "Multi              21\n",
            "Mustard             5\n",
            "Navy Blue          38\n",
            "Nude                1\n",
            "Off White          11\n",
            "Olive              13\n",
            "Orange             22\n",
            "Peach              33\n",
            "Pink              141\n",
            "Purple             98\n",
            "Red                66\n",
            "Rust                3\n",
            "Sea Green           3\n",
            "Tan                 1\n",
            "Teal               10\n",
            "Turquoise Blue      7\n",
            "White             184\n",
            "Yellow             49\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Watches\n",
            "baseColour\n",
            "Beige             6\n",
            "Black           897\n",
            "Blue             98\n",
            "Bronze            6\n",
            "Brown            51\n",
            "Burgundy          2\n",
            "Charcoal          4\n",
            "Coffee Brown      1\n",
            "Copper           11\n",
            "Cream            20\n",
            "Gold             51\n",
            "Green            15\n",
            "Grey             61\n",
            "Lavender          4\n",
            "Maroon            5\n",
            "Metallic          2\n",
            "Multi             1\n",
            "Navy Blue        15\n",
            "Off White         8\n",
            "Olive             5\n",
            "Orange           13\n",
            "Pink             49\n",
            "Purple           19\n",
            "Red              24\n",
            "Rose              1\n",
            "Silver          259\n",
            "Steel           147\n",
            "White           542\n",
            "Yellow            6\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Shorts\n",
            "baseColour\n",
            "Beige             7\n",
            "Black           109\n",
            "Blue             46\n",
            "Brown            14\n",
            "Charcoal          3\n",
            "Cream             4\n",
            "Green             8\n",
            "Grey             51\n",
            "Grey Melange      4\n",
            "Khaki             3\n",
            "Multi             1\n",
            "Navy Blue        59\n",
            "Off White         1\n",
            "Olive             8\n",
            "Orange            2\n",
            "Pink              6\n",
            "Purple            6\n",
            "Red              14\n",
            "White            43\n",
            "Yellow            4\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Formal Shoes\n",
            "baseColour\n",
            "Beige               1\n",
            "Black             392\n",
            "Blue                1\n",
            "Brown             214\n",
            "Coffee Brown        1\n",
            "Maroon              2\n",
            "Mushroom Brown      1\n",
            "Tan                15\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Backpacks\n",
            "baseColour\n",
            "Beige         1\n",
            "Black        35\n",
            "Blue          9\n",
            "Brown         1\n",
            "Green         5\n",
            "Grey          4\n",
            "Multi         1\n",
            "Navy Blue     8\n",
            "Olive         1\n",
            "Orange        1\n",
            "Pink         10\n",
            "Purple        6\n",
            "Red           2\n",
            "White         1\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Handbags\n",
            "baseColour\n",
            "Beige              87\n",
            "Black             313\n",
            "Blue              111\n",
            "Bronze              4\n",
            "Brown             286\n",
            "Burgundy            1\n",
            "Charcoal            5\n",
            "Coffee Brown        4\n",
            "Copper              1\n",
            "Cream               7\n",
            "Gold               13\n",
            "Green              85\n",
            "Grey               83\n",
            "Khaki               4\n",
            "Lavender            7\n",
            "Lime Green          1\n",
            "Magenta             3\n",
            "Maroon             36\n",
            "Mauve               3\n",
            "Multi              21\n",
            "Mushroom Brown      1\n",
            "Mustard             8\n",
            "Navy Blue          21\n",
            "Off White          10\n",
            "Olive              39\n",
            "Orange             38\n",
            "Peach              12\n",
            "Pink               91\n",
            "Purple            105\n",
            "Red               101\n",
            "Rust                1\n",
            "Silver              8\n",
            "Tan                 4\n",
            "Taupe               2\n",
            "Teal               23\n",
            "Turquoise Blue      3\n",
            "White              63\n",
            "Yellow             27\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Flip Flops\n",
            "baseColour\n",
            "Beige               4\n",
            "Black             198\n",
            "Blue               93\n",
            "Brown              54\n",
            "Coffee Brown        1\n",
            "Cream               1\n",
            "Gold               10\n",
            "Green              41\n",
            "Grey               36\n",
            "Lavender            2\n",
            "Maroon              2\n",
            "Multi               7\n",
            "Navy Blue          48\n",
            "Olive               3\n",
            "Orange             13\n",
            "Peach               1\n",
            "Pink               37\n",
            "Purple             27\n",
            "Red                76\n",
            "Silver              3\n",
            "Tan                 1\n",
            "Teal                1\n",
            "Turquoise Blue      3\n",
            "White              56\n",
            "Yellow             22\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Jeans\n",
            "baseColour\n",
            "Black              67\n",
            "Blue              403\n",
            "Brown               1\n",
            "Grey               16\n",
            "Lavender            1\n",
            "Navy Blue          39\n",
            "Peach               1\n",
            "Pink                1\n",
            "Purple              1\n",
            "Turquoise Blue      1\n",
            "White               4\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Perfume and Body Mist\n",
            "baseColour\n",
            "Beige               2\n",
            "Black              95\n",
            "Blue              102\n",
            "Bronze              2\n",
            "Brown              27\n",
            "Copper              1\n",
            "Cream              12\n",
            "Gold               15\n",
            "Green              40\n",
            "Grey               24\n",
            "Lavender            4\n",
            "Magenta             1\n",
            "Maroon              3\n",
            "Metallic            1\n",
            "Navy Blue           4\n",
            "Off White           2\n",
            "Olive               1\n",
            "Orange             21\n",
            "Peach               6\n",
            "Pink               46\n",
            "Purple             18\n",
            "Red                33\n",
            "Silver             36\n",
            "Steel               1\n",
            "Turquoise Blue      1\n",
            "White              84\n",
            "Yellow              9\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Casual Shoes\n",
            "baseColour\n",
            "Beige              44\n",
            "Black             797\n",
            "Blue              149\n",
            "Brown             490\n",
            "Burgundy            2\n",
            "Charcoal           12\n",
            "Coffee Brown        2\n",
            "Cream               6\n",
            "Gold                2\n",
            "Green              65\n",
            "Grey              172\n",
            "Khaki              43\n",
            "Magenta             1\n",
            "Maroon             16\n",
            "Multi               2\n",
            "Mushroom Brown      6\n",
            "Mustard             1\n",
            "Navy Blue         116\n",
            "Off White           4\n",
            "Olive              48\n",
            "Orange              4\n",
            "Peach               1\n",
            "Pink                7\n",
            "Purple             21\n",
            "Red                90\n",
            "Rust                2\n",
            "Silver              9\n",
            "Tan                25\n",
            "Taupe               1\n",
            "Teal                2\n",
            "White             403\n",
            "Yellow             15\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Sandals\n",
            "baseColour\n",
            "Beige               9\n",
            "Black             325\n",
            "Blue               19\n",
            "Brown             227\n",
            "Charcoal            2\n",
            "Gold                1\n",
            "Green               9\n",
            "Grey               44\n",
            "Khaki              17\n",
            "Maroon              1\n",
            "Multi               2\n",
            "Mushroom Brown      1\n",
            "Navy Blue          21\n",
            "Olive              27\n",
            "Purple              1\n",
            "Red                11\n",
            "Tan                16\n",
            "Turquoise Blue      1\n",
            "White              10\n",
            "Yellow              3\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Belts\n",
            "baseColour\n",
            "Beige         21\n",
            "Black        319\n",
            "Blue          19\n",
            "Bronze         2\n",
            "Brown        246\n",
            "Charcoal       2\n",
            "Cream          5\n",
            "Gold           2\n",
            "Green         13\n",
            "Grey          16\n",
            "Khaki          2\n",
            "Maroon         1\n",
            "Mauve          1\n",
            "Multi          7\n",
            "Mustard        1\n",
            "Navy Blue     15\n",
            "Off White      1\n",
            "Olive         17\n",
            "Orange         3\n",
            "Peach          1\n",
            "Pink          12\n",
            "Purple        20\n",
            "Red           28\n",
            "Silver         4\n",
            "Tan           15\n",
            "Teal           3\n",
            "White         22\n",
            "Yellow         3\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Shirts\n",
            "baseColour\n",
            "Beige              24\n",
            "Black             274\n",
            "Blue              734\n",
            "Brown              80\n",
            "Burgundy            5\n",
            "Charcoal            3\n",
            "Cream              10\n",
            "Gold                1\n",
            "Green             231\n",
            "Grey              178\n",
            "Khaki               4\n",
            "Lavender           22\n",
            "Magenta             2\n",
            "Maroon             64\n",
            "Multi               8\n",
            "Mustard             2\n",
            "Navy Blue         174\n",
            "Off White           1\n",
            "Olive              16\n",
            "Orange             20\n",
            "Peach               4\n",
            "Pink               81\n",
            "Purple            235\n",
            "Red               239\n",
            "Rust                3\n",
            "Teal                4\n",
            "Turquoise Blue      3\n",
            "White             476\n",
            "Yellow             53\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Socks\n",
            "baseColour\n",
            "Beige         16\n",
            "Black        169\n",
            "Blue          32\n",
            "Brown         42\n",
            "Charcoal       5\n",
            "Cream         12\n",
            "Green          7\n",
            "Grey          79\n",
            "Khaki          1\n",
            "Lavender       3\n",
            "Multi         12\n",
            "Navy Blue     62\n",
            "Olive          3\n",
            "Orange         1\n",
            "Pink          13\n",
            "Purple         7\n",
            "Red           13\n",
            "White        136\n",
            "Yellow         1\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Trousers\n",
            "baseColour\n",
            "Beige            45\n",
            "Black           121\n",
            "Blue              9\n",
            "Brown            59\n",
            "Charcoal         31\n",
            "Coffee Brown      2\n",
            "Cream            11\n",
            "Green            10\n",
            "Grey             83\n",
            "Khaki            25\n",
            "Navy Blue        46\n",
            "Olive            27\n",
            "Orange            1\n",
            "Pink              1\n",
            "Tan               2\n",
            "White             4\n",
            "Yellow            1\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Kurtas\n",
            "baseColour\n",
            "Beige              63\n",
            "Black             153\n",
            "Blue              227\n",
            "Brown             104\n",
            "Burgundy            1\n",
            "Charcoal            1\n",
            "Coffee Brown        1\n",
            "Cream              57\n",
            "Gold                2\n",
            "Green             219\n",
            "Grey               41\n",
            "Khaki               2\n",
            "Lavender           11\n",
            "Magenta            21\n",
            "Maroon             57\n",
            "Multi              72\n",
            "Mustard            21\n",
            "Navy Blue          39\n",
            "Off White          23\n",
            "Olive              18\n",
            "Orange             49\n",
            "Peach               9\n",
            "Pink              121\n",
            "Purple             99\n",
            "Red               121\n",
            "Rust               19\n",
            "Sea Green           5\n",
            "Silver              1\n",
            "Teal                9\n",
            "Turquoise Blue      8\n",
            "White             226\n",
            "Yellow             24\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Sunglasses\n",
            "baseColour\n",
            "Black        328\n",
            "Blue          31\n",
            "Bronze         4\n",
            "Brown        213\n",
            "Copper        33\n",
            "Cream          1\n",
            "Gold          52\n",
            "Green         20\n",
            "Grey          60\n",
            "Lavender       1\n",
            "Maroon         4\n",
            "Metallic      27\n",
            "Navy Blue      1\n",
            "Olive          1\n",
            "Orange         1\n",
            "Pink           9\n",
            "Purple        35\n",
            "Red           12\n",
            "Silver        19\n",
            "Steel         38\n",
            "White         26\n",
            "Yellow         2\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Tshirts\n",
            "baseColour\n",
            "Beige                 37\n",
            "Black                987\n",
            "Blue                 916\n",
            "Brown                 94\n",
            "Burgundy               7\n",
            "Charcoal              42\n",
            "Coffee Brown           3\n",
            "Cream                 46\n",
            "Fluorescent Green      1\n",
            "Green                484\n",
            "Grey                 593\n",
            "Grey Melange          20\n",
            "Khaki                  2\n",
            "Lavender              11\n",
            "Lime Green             1\n",
            "Magenta                9\n",
            "Maroon                84\n",
            "Multi                  9\n",
            "Mushroom Brown         1\n",
            "Mustard               11\n",
            "Navy Blue            396\n",
            "Off White             49\n",
            "Olive                 27\n",
            "Orange               115\n",
            "Peach                 33\n",
            "Pink                 206\n",
            "Purple               285\n",
            "Red                  491\n",
            "Rust                   6\n",
            "Sea Green              2\n",
            "Tan                    2\n",
            "Teal                  22\n",
            "Turquoise Blue         5\n",
            "White                948\n",
            "Yellow               235\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Wallets\n",
            "baseColour\n",
            "Beige              19\n",
            "Black             288\n",
            "Blue               34\n",
            "Bronze              3\n",
            "Brown             259\n",
            "Burgundy            1\n",
            "Charcoal            1\n",
            "Coffee Brown        6\n",
            "Cream               5\n",
            "Gold                1\n",
            "Green              28\n",
            "Grey               21\n",
            "Khaki               1\n",
            "Lavender            2\n",
            "Magenta             2\n",
            "Maroon              9\n",
            "Multi               4\n",
            "Mushroom Brown      3\n",
            "Mustard             3\n",
            "Navy Blue           8\n",
            "Olive              10\n",
            "Orange              8\n",
            "Pink               38\n",
            "Purple             58\n",
            "Red                41\n",
            "Rose                1\n",
            "Rust                1\n",
            "Silver              2\n",
            "Tan                 7\n",
            "Teal                7\n",
            "Turquoise Blue      3\n",
            "White              13\n",
            "Yellow              7\n",
            "Name: baseColour, dtype: int64\n",
            "\n",
            "Heels\n",
            "baseColour\n",
            "Beige              88\n",
            "Black             332\n",
            "Blue               90\n",
            "Bronze             34\n",
            "Brown             199\n",
            "Charcoal            2\n",
            "Coffee Brown        1\n",
            "Copper              8\n",
            "Cream               7\n",
            "Gold              107\n",
            "Green              20\n",
            "Grey               35\n",
            "Khaki               1\n",
            "Maroon             12\n",
            "Metallic            5\n",
            "Multi              37\n",
            "Mushroom Brown      1\n",
            "Mustard             2\n",
            "Navy Blue           6\n",
            "Nude                1\n",
            "Olive               6\n",
            "Orange             11\n",
            "Pink               33\n",
            "Purple             13\n",
            "Red                60\n",
            "Silver             75\n",
            "Tan                12\n",
            "Taupe               5\n",
            "Teal                3\n",
            "Turquoise Blue      6\n",
            "White              78\n",
            "Yellow             22\n",
            "Name: baseColour, dtype: int64\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Triplet Mining"
      ],
      "metadata": {
        "id": "r8r6kRgXDzP8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "triplet_map={\"Trousers\":{\"semi\":{\"articles\":[\"Trousers\",\"Shorts\",\"Jeans\"],\"gender\":\"opposite\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Trousers\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Kurtas\":{\"semi\":{\"articles\":[\"Kurtas\",\"Tshirts\",\"Shirts\"],\"gender\":\"opposite\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Kurtas\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Shorts\":{\"semi\":{\"articles\":[\"Shorts\",\"Trousers\",\"Jeans\"],\"gender\":\"opposite\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Shorts\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Casual Shoes\":{\"semi\":{\"articles\":[\"Casual Shoes\",\"Formal Shoes\",\"Sports Shoes\",\"Flip Flops\"],\"gender\":\"opposite\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Casual Shoes\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Formal Shoes\":{\"semi\":{\"articles\":[\"Casual Shoes\",\"Formal Shoes\",\"Sports Shoes\",\"Flip Flops\"],\"gender\":\"opposite\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Formal Shoes\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Sports Shoes\":{\"semi\":{\"articles\":[\"Casual Shoes\",\"Formal Shoes\",\"Sports Shoes\",\"Flip Flops\"],\"gender\":\"opposite\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Sports Shoes\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Flip Flops\":{\"semi\":{\"articles\":[\"Casual Shoes\",\"Formal Shoes\",\"Sports Shoes\",\"Flip Flops\"],\"gender\":\"opposite\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Flip Flops\",\"Sandals\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Sandals\":{\"semi\":{\"articles\":[\"Casual Shoes\",\"Formal Shoes\",\"Sports Shoes\",\"Flip Flops\"],\"gender\":\"opposite\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Flip Flops\",\"Sandals\",\"Heels\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Heels\":{\"semi\":{\"articles\":[\"Casual Shoes\",\"Formal Shoes\",\"Sports Shoes\",\"Flip Flops\"],\"gender\":\"same\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Heels\",\"Sandals\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Briefs\":{\"semi\":{\"articles\":[\"Briefs\",\"Shorts\"],\"gender\":\"opposite\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Briefs\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Backpacks\":{\"semi\":{\"articles\":[\"Wallets\",\"Handbags\"],\"gender\":\"same\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Backpacks\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Belts\":{\"semi\":{\"articles\":[\"Belts\",\"Wallets\"],\"gender\":\"opposite\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Belts\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Handbags\":{\"semi\":{\"articles\":[\"Backpacks\",\"Wallets\",\"Handbags\"],\"gender\":\"same\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Handbags\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Socks\":{\"semi\":{\"articles\":[\"Socks\",\"Flip Flops\"],\"gender\":\"opposite\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Socks\",\"Sandals\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Watches\":{\"semi\":{\"articles\":[\"Watches\",\"Wallets\"],\"gender\":\"opposite\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Watches\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Tshirts\":{\"semi\":{\"articles\":[\"Shirts\",\"Tshirts\",\"Tops\"],\"gender\":\"opposite\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Tshirts\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Shirts\":{\"semi\":{\"articles\":[\"Shirts\",\"Tshirts\",\"Tops\"],\"gender\":\"opposite\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Shirts\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Tops\":{\"semi\":{\"articles\":[\"Shirts\",\"Tshirts\"],\"gender\":\"same\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Tops\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Perfume and Body Mist\":{\"semi\":{\"articles\":[\"Perfume and Body Mist\"],\"gender\":\"opposite\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Perfume and Body Mist\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Wallets\":{\"semi\":{\"articles\":[\"Belts\",\"Wallets\",\"Watches\"],\"gender\":\"opposite\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Wallets\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Jeans\":{\"semi\":{\"articles\":[\"Jeans\",\"Trousers\",\"Shorts\"],\"gender\":\"opposite\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Jeans\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "             \n",
        "             \"Sunglasses\":{\"semi\":{\"articles\":[\"Belts\",\"Wallets\",\"Sunglasses\"],\"gender\":\"opposite\",\"baseColour\":\"different\"},\n",
        "                         \"hard\":{\"articles\":[\"Sunglasses\"],\"gender\":\"same\",\"baseColour\":\"same\"}\n",
        "                         },\n",
        "                          \n",
        "\n",
        "             }"
      ],
      "metadata": {
        "id": "c27tMnVYD0xn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.iloc[:2,:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "2TcIgCCtUav_",
        "outputId": "3f253c8e-bb2b-44de-d98f-135c57a8bd5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id gender masterCategory subCategory articleType baseColour  season  \\\n",
              "0  15970    Men        Apparel     Topwear      Shirts  Navy Blue    Fall   \n",
              "1  39386    Men        Apparel  Bottomwear       Jeans       Blue  Summer   \n",
              "\n",
              "     year   usage           productDisplayName  \n",
              "0  2011.0  Casual   Men Casual Navy Blue Shirt  \n",
              "1  2012.0  Casual  Men Casual Party Blue Jeans  "
            ],
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              "\n",
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              "      <div>\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>gender</th>\n",
              "      <th>masterCategory</th>\n",
              "      <th>subCategory</th>\n",
              "      <th>articleType</th>\n",
              "      <th>baseColour</th>\n",
              "      <th>season</th>\n",
              "      <th>year</th>\n",
              "      <th>usage</th>\n",
              "      <th>productDisplayName</th>\n",
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              "      <th>0</th>\n",
              "      <td>15970</td>\n",
              "      <td>Men</td>\n",
              "      <td>Apparel</td>\n",
              "      <td>Topwear</td>\n",
              "      <td>Shirts</td>\n",
              "      <td>Navy Blue</td>\n",
              "      <td>Fall</td>\n",
              "      <td>2011.0</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Men Casual Navy Blue Shirt</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>39386</td>\n",
              "      <td>Men</td>\n",
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              "      <td>Bottomwear</td>\n",
              "      <td>Jeans</td>\n",
              "      <td>Blue</td>\n",
              "      <td>Summer</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>Casual</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "male_data = data[data['gender']=='Men']\n",
        "female_data = data[data['gender']=='Women']\n",
        "male_data.shape[0],female_data.shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30arH6PwNOov",
        "outputId": "05479558-13c3-4969-ef81-532d0852afb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19101, 11199)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "set(data['masterCategory'])-{'Accessories'}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dcxnffl0SSl9",
        "outputId": "dac0b0b8-7325-4ad9-83e1-db549102ef4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Apparel', 'Footwear', 'Personal Care'}"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        " \n",
        "random_seed = 1\n",
        "\n",
        "\n",
        "def gen_triplets(data,male_data,female_data,\n",
        "                 num_semi_negatives = 4,\n",
        "                 num_hard_negatives = 3):\n",
        "  \n",
        "  triples= []\n",
        "  colours = set(data['baseColour'])\n",
        "\n",
        "  exceptions = []\n",
        "\n",
        "  for idx, item in tqdm(data.iterrows(), total=data.shape[0]):\n",
        "    try:\n",
        "      masterCategory = item['masterCategory']\n",
        "      articleType = item['articleType']\n",
        "      gender = item['gender']\n",
        "      baseColour = item['baseColour']\n",
        "      easy_samples = list(set(data['masterCategory'])-{masterCategory})\n",
        "      \n",
        "      for category in easy_samples:\n",
        "        triple={}\n",
        "        easy_negative=data[data['masterCategory']==category].sample(1,random_state=random_seed)['productDisplayName'].tolist()[0]\n",
        "        \n",
        "      \n",
        "        triple[\"anchor\"]=item['id']\n",
        "        triple[\"positive\"]=item['productDisplayName']\n",
        "        triple[\"negative\"] = easy_negative\n",
        "        triple[\"level\"] = \"easy\"\n",
        "        triples.append(triple)\n",
        "      \n",
        "      gender_sample = triplet_map[articleType][\"semi\"][\"gender\"]\n",
        "      gender_possibility = gender if gender_sample==\"same\" else list({\"Men\",\"Women\"}-{gender})[0]\n",
        "      colour_sample = triplet_map[articleType][\"semi\"][\"baseColour\"]\n",
        "      colour_possibilities = [baseColour] if colour_sample==\"same\" else list(colours - {baseColour})\n",
        "\n",
        "      article_possibilities = triplet_map[articleType][\"semi\"][\"articles\"]\n",
        "\n",
        "      \n",
        "      semi_negatives = data[(data['gender']==gender_possibility) & (data['baseColour'].isin(colour_possibilities))\n",
        "                            & (data['articleType'].isin(article_possibilities))]\n",
        "\n",
        "      loop_count=0\n",
        "\n",
        "      while(True):\n",
        "        loop_count+=1\n",
        "        semi_negatives = semi_negatives.sample(n=num_semi_negatives,random_state=random_seed)\n",
        "        if item['id'] not in set(semi_negatives['id']) or loop_count==10:\n",
        "          break\n",
        "\n",
        "      semi_negatives = semi_negatives['productDisplayName'].tolist()\n",
        "\n",
        "      \n",
        "      for semi_negative in semi_negatives:\n",
        "        triple={}       \n",
        "        triple[\"anchor\"]=item['id']\n",
        "        triple[\"positive\"]=item['productDisplayName']\n",
        "        triple[\"negative\"] = semi_negative\n",
        "        triple[\"level\"] = \"semi\"\n",
        "        triples.append(triple)\n",
        "\n",
        "\n",
        "      # ##Hard Negatives\n",
        "\n",
        "      gender_sample = triplet_map[articleType][\"hard\"][\"gender\"]\n",
        "      gender_possibility = gender if gender_sample==\"same\" else list({\"Men\",\"Women\"}-{gender})[0]\n",
        "      colour_sample = triplet_map[articleType][\"hard\"][\"baseColour\"]\n",
        "      colour_possibilities = [baseColour] if colour_sample==\"same\" else list(colours - {baseColour})\n",
        "\n",
        "      article_possibilities = triplet_map[articleType][\"hard\"][\"articles\"]\n",
        "\n",
        "\n",
        "      \n",
        "      hard_negatives = data[(data['gender']==gender_possibility) & (data['baseColour'].isin(colour_possibilities))\n",
        "                            & (data['articleType'].isin(article_possibilities))]\n",
        "\n",
        "      loop_count=0\n",
        "\n",
        "      while(True):\n",
        "        loop_count+=1\n",
        "        hard_negatives = hard_negatives.sample(n=num_hard_negatives,random_state=random_seed)\n",
        "        if item['id'] not in set(hard_negatives['id']) or loop_count==10:\n",
        "          break\n",
        "\n",
        "      hard_negatives = hard_negatives['productDisplayName'].tolist()\n",
        "\n",
        "      \n",
        "      for hard_negative in hard_negatives:\n",
        "        triple={}       \n",
        "        triple[\"anchor\"]=item['id']\n",
        "        triple[\"positive\"]=item['productDisplayName']\n",
        "        triple[\"negative\"] = hard_negative\n",
        "        triple[\"level\"] = \"hard\"\n",
        "        triples.append(triple)\n",
        "\n",
        "    except:\n",
        "      exceptions.append(idx)\n",
        "    \n",
        "    \n",
        "\n",
        "\n",
        "  return (pd.DataFrame(triples)),exceptions\n",
        "   \n",
        "\n",
        "\n"
      ],
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        "id": "F2DaTUivNIst"
      },
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              "302077   51623  Women Casual Pink Dial Chronograph Watch ES3050   \n",
              "302078   51623  Women Casual Pink Dial Chronograph Watch ES3050   \n",
              "302079   51623  Women Casual Pink Dial Chronograph Watch ES3050   \n",
              "302080   51623  Women Casual Pink Dial Chronograph Watch ES3050   \n",
              "302081   51623  Women Casual Pink Dial Chronograph Watch ES3050   \n",
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              "4                   Women Casual Light Winter Purple Tops  semi  \n",
              "...                                                   ...   ...  \n",
              "302077                       Men Casual Silver Dial Watch  semi  \n",
              "302078                        Men Casual White Dial Watch  semi  \n",
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              "302080  Women Casual Pink Dial Chronograph Watch SX005...  hard  \n",
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          "metadata": {},
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      "cell_type": "code",
      "source": [
        "triplet_data.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "xiWYH_T7rOqx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "triplet_data = triplet_data.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "lZncgKiIrR4o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "least_common = Counter(triplet_data['anchor']).most_common()[-1]\n",
        "least_common"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UmhtmBi6rW7u",
        "outputId": "90a86ed8-1c91-4a75-a959-4e60325eda72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42936, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 50
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    {
      "cell_type": "markdown",
      "source": [
        "#Final Data"
      ],
      "metadata": {
        "id": "K75ztxdBIOL7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "triplet_data"
      ],
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              "        anchor                                         positive  \\\n",
              "0        15970                       Men Casual Navy Blue Shirt   \n",
              "1        15970                       Men Casual Navy Blue Shirt   \n",
              "2        15970                       Men Casual Navy Blue Shirt   \n",
              "3        15970                       Men Casual Navy Blue Shirt   \n",
              "4        15970                       Men Casual Navy Blue Shirt   \n",
              "...        ...                                              ...   \n",
              "294967   51623  Women Casual Pink Dial Chronograph Watch ES3050   \n",
              "294968   51623  Women Casual Pink Dial Chronograph Watch ES3050   \n",
              "294969   51623  Women Casual Pink Dial Chronograph Watch ES3050   \n",
              "294970   51623  Women Casual Pink Dial Chronograph Watch ES3050   \n",
              "294971   51623  Women Casual Pink Dial Chronograph Watch ES3050   \n",
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              "2                            Men Casual Yellow Flip Flops  easy  \n",
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              "294967           Men Casual Sliver Dial Chronograph Watch  semi  \n",
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              "294970                       Women Casual Pink Dial Watch  hard  \n",
              "294971  Women Casual Pink Dial Chronograph Watch SX005...  hard  \n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-2a622b27-5651-4a64-8cf9-98c071e18dcb 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-2a622b27-5651-4a64-8cf9-98c071e18dcb');\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": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "triplet_data.to_csv(\"triplet_data.csv\",index=False)"
      ],
      "metadata": {
        "id": "2esdGAJlppHk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.read_csv('/content/triplet_data.csv').info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kv8nHyEusHjT",
        "outputId": "3d800c1c-893c-43f6-b53f-9aa9ff80c83c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294972 entries, 0 to 294971\n",
            "Data columns (total 4 columns):\n",
            " #   Column    Non-Null Count   Dtype \n",
            "---  ------    --------------   ----- \n",
            " 0   anchor    294972 non-null  int64 \n",
            " 1   positive  294972 non-null  object\n",
            " 2   negative  294972 non-null  object\n",
            " 3   level     294972 non-null  object\n",
            "dtypes: int64(1), object(3)\n",
            "memory usage: 9.0+ MB\n"
          ]
        }
      ]
    }
  ]
}