sibgrapi_evaluation.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/dudaholandah/sibgrapi2022_wuw/blob/main/sibgrapi_evaluation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# Imports"
      ],
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "!apt-get install poppler-utils\n",
        "!pip install pdf2image\n",
        "!pip install unidecode\n",
        "!pip install umap-learn[plot]\n",
        "!pip install trimap\n",
        "!pip install -U kaleido\n",
        "!pip install scikit-learn-extra"
      ],
      "metadata": {
        "id": "WGib2oaNT1MM"
      },
      "execution_count": null,
      "outputs": []
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      "execution_count": 2,
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        "id": "qBfSL0Ccbty6"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from unidecode import unidecode\n",
        "from sklearn import preprocessing\n",
        "from pandas.core.frame import DataFrame\n",
        "import plotly.express as px\n",
        "from sklearn.manifold import TSNE\n",
        "import re\n",
        "import umap\n",
        "import trimap\n",
        "from sklearn.decomposition import PCA as sklearnPCA\n",
        "from sklearn.manifold import Isomap\n",
        "from sklearn.manifold import trustworthiness\n",
        "import plotly.graph_objects as go\n",
        "from sklearn.metrics import silhouette_samples, silhouette_score\n",
        "from sklearn.cluster import KMeans\n",
        "import plotly\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.io as pio\n",
        "import pdf2image\n",
        "import os\n",
        "from sklearn.metrics import silhouette_samples, silhouette_score\n",
        "from sklearn_extra.cluster import KMedoids"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pre-Processing Data"
      ],
      "metadata": {
        "id": "G0ogYP7UcjBB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_excel(\"vegan_dataset.xlsx\", sheet_name='Veganos')"
      ],
      "metadata": {
        "id": "38rpysEbcCOe"
      },
      "execution_count": 4,
      "outputs": []
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      "source": [
        "data.head()"
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      "execution_count": 5,
      "outputs": [
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              "  Classification                Description  \\\n",
              "0         MEAT 2      Carne Moída do Futuro   \n",
              "1         MEAT 3      Carne Moída do Futuro   \n",
              "2      POULTRY 3           Frango do Futuro   \n",
              "3        PORK 1          Linguiça do Futuro   \n",
              "4         MEAT 1  Hamburguer de Soja Goshen   \n",
              "\n",
              "                                         Ingredients  Serving Size   Kcal  \\\n",
              "0  water, texturized soy protein, pea protein, ch...          80.0  168.0   \n",
              "1  water, texturized soy protein, isolated soy pr...          80.0  168.0   \n",
              "2  water, texturized soy protein, isolated soy pr...          80.0  168.0   \n",
              "3  water, texturized soy protein, isolated soy pr...          50.0  139.0   \n",
              "4   texturized soy protein, water, soy oil, modif...          50.0  118.0   \n",
              "\n",
              "   Carbohydrate  Sugars  Proteins   Total Fats   Saturated Fats  \\\n",
              "0          11.0     0.0       8.2         10.0              9.0   \n",
              "1          11.0     0.0       8.2         10.0              9.0   \n",
              "2          11.0     0.0       8.2         10.0              9.0   \n",
              "3           7.7     0.0       5.7          9.5              3.9   \n",
              "4           2.8     0.0       7.0          9.0              1.3   \n",
              "\n",
              "    Dietary Fiber   Sodium  B12  Calcium  Zinc  \n",
              "0              0.7   392.0  0.0      0.0   0.0  \n",
              "1              0.7   392.0  0.0      0.0   0.0  \n",
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              "      <td>Linguiça do Futuro</td>\n",
              "      <td>water, texturized soy protein, isolated soy pr...</td>\n",
              "      <td>50.0</td>\n",
              "      <td>139.0</td>\n",
              "      <td>7.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.7</td>\n",
              "      <td>9.5</td>\n",
              "      <td>3.9</td>\n",
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              "      <th>4</th>\n",
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              "      <td>Hamburguer de Soja Goshen</td>\n",
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              "      <td>118.0</td>\n",
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    {
      "cell_type": "markdown",
      "source": [
        "### Separating data"
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        "id": "iUaEyrS5hi9I"
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      "cell_type": "code",
      "source": [
        "data_ingredients = pd.DataFrame(data['Ingredients'])\n",
        "data_nutrients = data.drop(columns=['Ingredients', 'Classification', \"Description\"])\n",
        "data_clasification = pd.DataFrame(data['Classification'])\n",
        "data_name = pd.DataFrame(data[\"Description\"])\n",
        "label_data = data_clasification.join(data_name)"
      ],
      "metadata": {
        "id": "uflKESOVddI4"
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      "execution_count": 6,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "data_nutrients.head()"
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              "   Serving Size   Kcal  Carbohydrate  Sugars  Proteins   Total Fats  \\\n",
              "0          80.0  168.0          11.0     0.0       8.2         10.0   \n",
              "1          80.0  168.0          11.0     0.0       8.2         10.0   \n",
              "2          80.0  168.0          11.0     0.0       8.2         10.0   \n",
              "3          50.0  139.0           7.7     0.0       5.7          9.5   \n",
              "4          50.0  118.0           2.8     0.0       7.0          9.0   \n",
              "\n",
              "    Saturated Fats   Dietary Fiber   Sodium  B12  Calcium  Zinc  \n",
              "0              9.0              0.7   392.0  0.0      0.0   0.0  \n",
              "1              9.0              0.7   392.0  0.0      0.0   0.0  \n",
              "2              9.0              0.7   392.0  0.0      0.0   0.0  \n",
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              "          const element = document.querySelector('#df-e303cbba-54e3-4113-a636-3be8b66fb2d6');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Normalizing Nutrient Values (MinMax Scaler) "
      ],
      "metadata": {
        "id": "glGmmTNFhZwJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = data_nutrients.values\n",
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CMXY3FP5ekWw",
        "outputId": "61eef1a0-ca3f-4950-e597-bc11cf7e12a2"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 80.  168.   11.  ...   0.    0.    0. ]\n",
            " [ 80.  168.   11.  ...   0.    0.    0. ]\n",
            " [ 80.  168.   11.  ...   0.    0.    0. ]\n",
            " ...\n",
            " [ 40.   84.    5.2 ...   0.    0.    0. ]\n",
            " [ 10.   25.    3.3 ...   0.    0.    0. ]\n",
            " [  7.   28.    1.2 ...   0.    0.    0. ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "attributes_dummies = data_nutrients.columns\n",
        "normalize = preprocessing.MinMaxScaler()\n",
        "xscaled = normalize.fit_transform(X)\n",
        "\n",
        "nutrients_normalized = pd.DataFrame(xscaled,columns=attributes_dummies)\n",
        "nutrients_normalized = nutrients_normalized.replace(np.nan,0)\n",
        "\n",
        "nutrients_normalized.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "55oXzPRqgA8-",
        "outputId": "29e401b6-60df-4f74-d95e-edf48efc4847"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Serving Size      Kcal  Carbohydrate  Sugars  Proteins   Total Fats  \\\n",
              "0      0.212828  0.263815      0.244444     0.0  0.195238     0.204082   \n",
              "1      0.212828  0.263815      0.244444     0.0  0.195238     0.204082   \n",
              "2      0.212828  0.263815      0.244444     0.0  0.195238     0.204082   \n",
              "3      0.125364  0.212121      0.171111     0.0  0.135714     0.193878   \n",
              "4      0.125364  0.174688      0.062222     0.0  0.166667     0.183673   \n",
              "\n",
              "    Saturated Fats   Dietary Fiber     Sodium  B12  Calcium  Zinc  \n",
              "0         0.489130         0.007447  0.414376  0.0      0.0   0.0  \n",
              "1         0.489130         0.007447  0.414376  0.0      0.0   0.0  \n",
              "2         0.489130         0.007447  0.414376  0.0      0.0   0.0  \n",
              "3         0.211957         0.002128  0.310782  0.0      0.0   0.0  \n",
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              "      <td>0.0</td>\n",
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              "      <td>0.174688</td>\n",
              "      <td>0.062222</td>\n",
              "      <td>0.0</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-3c527afd-b898-4379-89fe-03e1b8118822');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Normalizing Ingredients (One Hot Encode)"
      ],
      "metadata": {
        "id": "47hZh5mjFl6H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def pre_process(text):\n",
        "    text = re.sub(r'[.,():%-]+', \" \", text)\n",
        "    text = re.sub(r'[\\s]+', \" \", text)\n",
        "    text = unidecode(text.strip().lower())\n",
        "    return text"
      ],
      "metadata": {
        "id": "xZ6urS9cdQdX"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vocab = set()\n",
        "\n",
        "for i in range(data_ingredients.shape[0]):\n",
        "  aux = data_ingredients.at[i,'Ingredients'].split(\",\")\n",
        "  for word in aux:\n",
        "    word = pre_process(word)\n",
        "    if len(word) > 1: vocab.add(word.strip())\n",
        "\n",
        "sorted(vocab)"
      ],
      "metadata": {
        "id": "lcbd5lEyBRfc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ingredients_normalized = pd.DataFrame()\n",
        "\n",
        "for i in range(276):\n",
        "  aux = data_ingredients.at[i,'Ingredients'].split(\",\")\n",
        "  for word in aux:\n",
        "    word = pre_process(word)\n",
        "    ingredients_normalized.at[i,word] = 1\n",
        "\n",
        "ingredients_normalized = ingredients_normalized.replace(np.nan,0)\n",
        "ingredients_normalized.head()"
      ],
      "metadata": {
        "id": "r_fKMe2ghv6k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "a5fac4fb-bdc3-4cff-9279-89f10ddd1961"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   water  texturized soy protein  pea protein  chickpea flour  vegetal fat  \\\n",
              "0    1.0                     1.0          1.0             1.0          1.0   \n",
              "1    1.0                     1.0          1.0             1.0          1.0   \n",
              "2    1.0                     1.0          1.0             1.0          1.0   \n",
              "3    1.0                     1.0          1.0             1.0          1.0   \n",
              "4    1.0                     1.0          0.0             0.0          0.0   \n",
              "\n",
              "   modified starch  onion  meat flavored condiment  salt  sugar  ...  \\\n",
              "0              1.0    1.0                      1.0   1.0    1.0  ...   \n",
              "1              1.0    1.0                      1.0   1.0    0.0  ...   \n",
              "2              1.0    1.0                      1.0   1.0    0.0  ...   \n",
              "3              1.0    1.0                      0.0   1.0    1.0  ...   \n",
              "4              0.0    0.0                      0.0   1.0    1.0  ...   \n",
              "\n",
              "   tricalcium phosphatxanthan gum  phosphoric acid  aquafaba  \\\n",
              "0                             0.0              0.0       0.0   \n",
              "1                             0.0              0.0       0.0   \n",
              "2                             0.0              0.0       0.0   \n",
              "3                             0.0              0.0       0.0   \n",
              "4                             0.0              0.0       0.0   \n",
              "\n",
              "   potassium sorbate disodium calcium  avocado  potassium chloride  \\\n",
              "0                                 0.0      0.0                 0.0   \n",
              "1                                 0.0      0.0                 0.0   \n",
              "2                                 0.0      0.0                 0.0   \n",
              "3                                 0.0      0.0                 0.0   \n",
              "4                                 0.0      0.0                 0.0   \n",
              "\n",
              "   isolated vegetable protein  soy  pea starch  isolated rice protein  \n",
              "0                         0.0  0.0         0.0                    0.0  \n",
              "1                         0.0  0.0         0.0                    0.0  \n",
              "2                         0.0  0.0         0.0                    0.0  \n",
              "3                         0.0  0.0         0.0                    0.0  \n",
              "4                         0.0  0.0         0.0                    0.0  \n",
              "\n",
              "[5 rows x 383 columns]"
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Preparing data"
      ],
      "metadata": {
        "id": "azEImQ_cGFi_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_data = pd.DataFrame(ingredients_normalized)\n",
        "raw_data = pd.concat([raw_data,nutrients_normalized], axis=1)\n",
        "raw_data = raw_data.replace(np.nan,0)\n",
        "\n",
        "raw_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "tTomN3J0kd6h",
        "outputId": "bfe14509-efb5-4e35-e7c2-f911d2caf3fa"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   water  texturized soy protein  pea protein  chickpea flour  vegetal fat  \\\n",
              "0    1.0                     1.0          1.0             1.0          1.0   \n",
              "1    1.0                     1.0          1.0             1.0          1.0   \n",
              "2    1.0                     1.0          1.0             1.0          1.0   \n",
              "3    1.0                     1.0          1.0             1.0          1.0   \n",
              "4    1.0                     1.0          0.0             0.0          0.0   \n",
              "\n",
              "   modified starch  onion  meat flavored condiment  salt  sugar  ...  \\\n",
              "0              1.0    1.0                      1.0   1.0    1.0  ...   \n",
              "1              1.0    1.0                      1.0   1.0    0.0  ...   \n",
              "2              1.0    1.0                      1.0   1.0    0.0  ...   \n",
              "3              1.0    1.0                      0.0   1.0    1.0  ...   \n",
              "4              0.0    0.0                      0.0   1.0    1.0  ...   \n",
              "\n",
              "   Carbohydrate  Sugars  Proteins   Total Fats   Saturated Fats  \\\n",
              "0      0.244444     0.0  0.195238     0.204082         0.489130   \n",
              "1      0.244444     0.0  0.195238     0.204082         0.489130   \n",
              "2      0.244444     0.0  0.195238     0.204082         0.489130   \n",
              "3      0.171111     0.0  0.135714     0.193878         0.211957   \n",
              "4      0.062222     0.0  0.166667     0.183673         0.070652   \n",
              "\n",
              "    Dietary Fiber     Sodium  B12  Calcium  Zinc  \n",
              "0         0.007447  0.414376  0.0      0.0   0.0  \n",
              "1         0.007447  0.414376  0.0      0.0   0.0  \n",
              "2         0.007447  0.414376  0.0      0.0   0.0  \n",
              "3         0.002128  0.310782  0.0      0.0   0.0  \n",
              "4         0.021277  0.363636  0.0      0.0   0.0  \n",
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              "    }\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-e3b63340-db6a-45cd-904a-9abbcee6ed84 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-e3b63340-db6a-45cd-904a-9abbcee6ed84');\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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = raw_data.values\n",
        "X_ingredients = ingredients_normalized.values\n",
        "X_nutrients = nutrients_normalized.values\n",
        "y = label_data.values\n",
        "all_data = label_data.join(raw_data)\n",
        "\n",
        "for i in range(276):\n",
        "  label = label_data['Classification'][i].rstrip()\n",
        "  label_data.at[i,'Classification'] = label\n",
        "  all_data.at[i,'Classification'] = label\n",
        "\n",
        "print(all_data['Classification'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8hjs_JCcDiL",
        "outputId": "18ce8646-ffb5-4d78-8cdb-ad3e460d10b1"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DAIRY 1      80\n",
            "DAIRY 3      45\n",
            "MEAT 1       34\n",
            "MEAT 3       18\n",
            "POULTRY 1    14\n",
            "EGG 1        14\n",
            "POULTRY 3    13\n",
            "PORK 1       12\n",
            "DAIRY 2      11\n",
            "MEAT 2        9\n",
            "FISH 1        8\n",
            "PORK 2        6\n",
            "POULTRY 2     5\n",
            "FISH 2        5\n",
            "EGG 2         2\n",
            "Name: Classification, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualization Techniques"
      ],
      "metadata": {
        "id": "fc67aC8Lj5hE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(276):\n",
        "  aux = data_ingredients.at[i,'Ingredients']\n",
        "  data_ingredients.at[i,'Ingredients'] = re.sub(',\\s*',\"<br>\",aux).lstrip()"
      ],
      "metadata": {
        "id": "fPvDbUMGFeWs"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if not os.path.exists(\"imgs\"):\n",
        "    os.mkdir(\"imgs\")"
      ],
      "metadata": {
        "id": "E3cA0tsme9Yz"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## t-SNE"
      ],
      "metadata": {
        "id": "XDaLYnRncUbJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tsne = TSNE(n_components=2,perplexity=5,learning_rate=350,metric='euclidean', init='pca')\n",
        "X_tsne = tsne.fit_transform(X)\n",
        "\n",
        "X_tsne[1:4, :]\n",
        "all_data['x'] = X_tsne[:,0]\n",
        "all_data['y'] = X_tsne[:,1]\n",
        "all_data['Ingredients'] = data_ingredients\n",
        "\n",
        "fig = px.scatter(\n",
        "    all_data, \n",
        "    x='x', \n",
        "    y='y', \n",
        "    color='Classification', \n",
        "    template=\"simple_white\",\n",
        "    labels={ \n",
        "      \"Classification\": \"Classification (Label)\"\n",
        "    },\n",
        "    color_discrete_sequence= px.colors.qualitative.Plotly + px.colors.qualitative.Bold,\n",
        "    hover_name=\"Description\",\n",
        "    hover_data={'x':False,\n",
        "                'y':False,  \n",
        "                'Ingredients':True},\n",
        "    width=700)\n",
        "\n",
        "fig.update_traces(showlegend=False,\n",
        "                  marker=dict(size=8,),\n",
        "                              # line=dict(width=1,\n",
        "                              #           color='DarkSlateGrey')),\n",
        "                  selector=dict(mode='markers'))\n",
        "\n",
        "fig.update_layout(xaxis={'visible': False},\n",
        "                  yaxis={'visible': False},\n",
        "                  margin=dict(l=0,r=0,b=0,t=0))\n",
        "\n",
        "\n",
        "plt.savefig('imgs/TSNE_ALL.eps', format='eps')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "o-rjtAxAqlNJ",
        "outputId": "ab3e8835-69da-430c-c82e-8f45460addae"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "\n",
              "// Listen for the removal of the full notebook cells\n",
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              "</body>\n",
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## UMAP"
      ],
      "metadata": {
        "id": "A7LLQVJotd72"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "reducer = umap.UMAP(n_components=2,n_neighbors=10, min_dist=0.1,metric='euclidean', random_state=0)\n",
        "X_umap = reducer.fit_transform(X)\n",
        "\n",
        "X_umap[1:4, :]\n",
        "all_data['x'] = X_umap[:,0]\n",
        "all_data['y'] = X_umap[:,1]\n",
        "\n",
        "fig = px.scatter(\n",
        "    all_data, \n",
        "    x='x', \n",
        "    y='y', \n",
        "    color='Classification', \n",
        "    template=\"simple_white\",\n",
        "    labels={ \n",
        "      \"Classification\": \"Classification (Label)\"\n",
        "    },\n",
        "    color_discrete_sequence= px.colors.qualitative.Plotly + px.colors.qualitative.Bold,\n",
        "    hover_name=\"Description\",\n",
        "    hover_data={'x':False,\n",
        "                'y':False,  \n",
        "                'Ingredients':True}, \n",
        "    width=800)\n",
        "\n",
        "fig.update_traces(marker=dict(size=8),\n",
        "                  selector=dict(mode='markers'))\n",
        "\n",
        "fig.update_layout(xaxis={'visible': False},\n",
        "                  yaxis={'visible': False},\n",
        "                  legend=dict(font=dict(size=16)),\n",
        "                  margin=dict(l=0,r=0,b=0,t=0))\n",
        "\n",
        "plt.savefig('imgs/UMAP_ALL.eps', format='eps')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "outputId": "1620c442-0375-4f38-f32f-388b0776c51c",
        "id": "fWUVbgzjt1fQ"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "            Plotly.purge(gd);\n",
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              "</html>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## PCA"
      ],
      "metadata": {
        "id": "29zJ_sqZd1gv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sklearn_pca = sklearnPCA(n_components=2)\n",
        "X_pca = sklearn_pca.fit_transform(X)\n",
        "\n",
        "X_pca[1:4, :]\n",
        "all_data['x'] = X_pca[:,0]\n",
        "all_data['y'] = X_pca[:,1]\n",
        "\n",
        "fig = px.scatter(\n",
        "    all_data, \n",
        "    x='x', \n",
        "    y='y', \n",
        "    color='Classification', \n",
        "    template=\"simple_white\",\n",
        "    labels={ \n",
        "      \"Classification\": \"Classification (Label)\"\n",
        "    },\n",
        "    color_discrete_sequence= px.colors.qualitative.Plotly + px.colors.qualitative.Bold,\n",
        "    hover_name=\"Description\",\n",
        "    hover_data={'x':False,\n",
        "                'y':False,  \n",
        "                # 'Kcal ': [f' {x}' for x in data['Kcal']],\n",
        "                # 'Serving Size ': [f' {y}' for y in data['Serving Size']],\n",
        "                # 'Carbohydrate ': [f' {z}' for z in data['Carbohydrate']],\n",
        "                # 'Sugars ': [f' {a}' for a in data['Sugars']],\n",
        "                # 'Proteins ': [f' {b}' for b in data['Proteins']],\n",
        "                'Ingredients':True}, \n",
        "    width=700)\n",
        "\n",
        "fig.update_traces(showlegend=False,\n",
        "                  marker=dict(size=8),\n",
        "                  selector=dict(mode='markers'))\n",
        "\n",
        "fig.update_layout(xaxis={'visible': False},\n",
        "                  yaxis={'visible': False},\n",
        "                  margin=dict(l=0,r=0,b=0,t=0))\n",
        "\n",
        "plt.savefig('imgs/PCA_ALL.eps', format='eps')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "3LsXNNwnd4VR",
        "outputId": "9529188a-0e01-45f2-dc9b-21a81204ed6e"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
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              "                            \n",
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              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "\n",
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              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## TRIMAP"
      ],
      "metadata": {
        "id": "KLIFc4s-rnaU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_trimap = trimap.TRIMAP(n_dims=2, n_inliers=10).fit_transform(X)\n",
        "\n",
        "X_trimap[1:4, :]\n",
        "all_data['x'] = X_trimap[:,0]\n",
        "all_data['y'] = X_trimap[:,1]\n",
        "all_data['Ingredients'] = data_ingredients\n",
        "\n",
        "fig = px.scatter(\n",
        "    all_data, \n",
        "    x='x', \n",
        "    y='y', \n",
        "    color='Classification', \n",
        "    template=\"simple_white\",\n",
        "    labels={ \n",
        "      \"Classification\": \"Classification (Label)\"\n",
        "    },\n",
        "    color_discrete_sequence= px.colors.qualitative.Plotly + px.colors.qualitative.Bold,\n",
        "    hover_name=\"Description\",\n",
        "    hover_data={'x':False,\n",
        "                'y':False,  \n",
        "                # 'Kcal ': [f' {x}' for x in data['Kcal']],\n",
        "                # 'Serving Size ': [f' {y}' for y in data['Serving Size']],\n",
        "                # 'Carbohydrate ': [f' {z}' for z in data['Carbohydrate']],\n",
        "                # 'Sugars ': [f' {a}' for a in data['Sugars']],\n",
        "                # 'Proteins ': [f' {b}' for b in data['Proteins']],\n",
        "                'Ingredients':True\n",
        "                }, \n",
        "    width=800)\n",
        "\n",
        "fig.update_traces(marker=dict(size=8),\n",
        "                  selector=dict(mode='markers'))\n",
        "\n",
        "fig.update_layout(xaxis={'visible': False},\n",
        "                  yaxis={'visible': False},\n",
        "                  legend=dict(font=dict(size=16)),\n",
        "                  margin=dict(l=0,r=0,b=0,t=0))\n",
        "\n",
        "plt.savefig('imgs/TRIMAP_ALL.eps', format='eps')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "6xwZGth5rqH0",
        "outputId": "5b47e876-0b9c-4a9f-9dfd-cd40696726c1"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                            \n",
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              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
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              "\n",
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              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation metrics"
      ],
      "metadata": {
        "id": "u9Nv_zI34URk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Trustworthiness"
      ],
      "metadata": {
        "id": "r-5tTvnT4hwr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kneigh = []\n",
        "y_tsne = []\n",
        "y_umap = []\n",
        "y_pca = []\n",
        "y_trimap = []\n",
        "\n",
        "for k in range(1,51):\n",
        "  kneigh.append(k)\n",
        "\n",
        "  precision = trustworthiness(X, X_tsne, n_neighbors=k, metric='euclidean')\n",
        "  y_tsne.append(precision)\n",
        "\n",
        "  precision = trustworthiness(X, X_umap, n_neighbors=k, metric='euclidean')\n",
        "  y_umap.append(precision)\n",
        "\n",
        "  precision = trustworthiness(X, X_pca, n_neighbors=k, metric='euclidean')\n",
        "  y_pca.append(precision)\n",
        "\n",
        "  precision = trustworthiness(X, X_trimap, n_neighbors=k, metric='euclidean')\n",
        "  y_trimap.append(precision)"
      ],
      "metadata": {
        "id": "ibt-sLx1MBAS"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(x=kneigh, y=y_tsne,\n",
        "                    mode='lines+markers',\n",
        "                    name='t-SNE',\n",
        "                    line=dict(color=px.colors.qualitative.Plotly[0])))\n",
        "\n",
        "fig.add_trace(go.Scatter(x=kneigh, y=y_umap,\n",
        "                    mode='lines+markers',\n",
        "                    name='UMAP',\n",
        "                    line=dict(color=px.colors.qualitative.Plotly[1])))\n",
        "\n",
        "fig.add_trace(go.Scatter(x=kneigh, y=y_pca,\n",
        "                    mode='lines+markers',\n",
        "                    name='PCA',\n",
        "                    line=dict(color=px.colors.qualitative.Plotly[2])))\n",
        "\n",
        "fig.add_trace(go.Scatter(x=kneigh, y=y_trimap,\n",
        "                    mode='lines+markers',\n",
        "                    name='TRIMAP',\n",
        "                    line=dict(color=px.colors.qualitative.Plotly[3])))\n",
        "\n",
        "fig.update_layout(width=1000, template=\"simple_white\",\n",
        "                  xaxis_title=\"K (number of neighbors)\",\n",
        "                  yaxis_title=\"Trustworthiness\",\n",
        "                  font=dict(size=18),\n",
        "                  margin=dict(l=0,r=0,b=0,t=0))\n",
        "\n",
        "fig.show()\n",
        "fig. write_image('imgs/neighborhoodpres.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "pRUOvepXO-5e",
        "outputId": "af6175cd-933a-484e-e19a-91b567929532"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Silhouette Coefficient"
      ],
      "metadata": {
        "id": "vDo1G0GCcJ7R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def coeficiente_silhueta(X_high,X_low):\n",
        "  silhouette_avg_high = []\n",
        "  silhouette_avg_low = []\n",
        "\n",
        "  for k in range(3,20):\n",
        "    clusterer = KMedoids(n_clusters=k, init='k-medoids++',random_state=10)\n",
        "    cluster_labels_high = clusterer.fit_predict(X_high)\n",
        "    cluster_labels_low = clusterer.fit_predict(X_low)\n",
        "    silhouette_avg_high.append(silhouette_score(X_high, cluster_labels_high))\n",
        "    silhouette_avg_low.append(silhouette_score(X_low, cluster_labels_low))\n",
        "\n",
        "  mean_silhouete_high = np.mean(np.array(silhouette_avg_high))\n",
        "  mean_silhouete_low = np.mean(np.array(silhouette_avg_low))\n",
        "  std_silhouete_high = np.std(np.array(silhouette_avg_high))\n",
        "  std_silhouete_low = np.std(np.array(silhouette_avg_low))\n",
        "    \n",
        "  return mean_silhouete_high, mean_silhouete_low, std_silhouete_high, std_silhouete_low"
      ],
      "metadata": {
        "id": "EEcbt83_GI4k"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "t-SNE"
      ],
      "metadata": {
        "id": "aS99avj-iga8"
      }
    },
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      "cell_type": "code",
      "source": [
        "mean_high,mean_low,std_high,std_low = coeficiente_silhueta(X,X_tsne)\n",
        "\n",
        "print(\"TSNE\")\n",
        "print(f\"Média Silhueta Original: {mean_high} Desvio Padrão Original: {std_high}\")\n",
        "print(f\"Média Silhueta Visualização: {mean_low:.10f} Desvio Padrão Visualização: {std_low:.10f}\\n\")"
      ],
      "metadata": {
        "id": "6WDSGstkcWa5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03d0f2c7-1e93-4103-a7d9-c11d82268fb4"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TSNE\n",
            "Média Silhueta Original: 0.0860648712739905 Desvio Padrão Original: 0.02080142865863332\n",
            "Média Silhueta Visualização: 0.4358619452 Desvio Padrão Visualização: 0.0584011637\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "UMAP"
      ],
      "metadata": {
        "id": "0GDI0CwojTGx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mean_high,mean_low,std_high,std_low = coeficiente_silhueta(X,X_umap)\n",
        "\n",
        "print(\"UMAP\")\n",
        "print(f\"Média Silhueta Original: {mean_high} Desvio Padrão Original: {std_high}\")\n",
        "print(f\"Média Silhueta Visualização: {mean_low:.10f} Desvio Padrão Visualização: {std_low:.10f}\\n\")"
      ],
      "metadata": {
        "id": "xm-j7RDcjSSz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "370701c1-0653-49d3-8117-951af87a6856"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "UMAP\n",
            "Média Silhueta Original: 0.0860648712739905 Desvio Padrão Original: 0.02080142865863332\n",
            "Média Silhueta Visualização: 0.5213357210 Desvio Padrão Visualização: 0.0776655748\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "PCA"
      ],
      "metadata": {
        "id": "PKr6Rtggjrof"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mean_high,mean_low,std_high,std_low = coeficiente_silhueta(X,X_pca)\n",
        "\n",
        "print(\"PCA\")\n",
        "print(f\"Média Silhueta Original: {mean_high} Desvio Padrão Original: {std_high}\")\n",
        "print(f\"Média Silhueta Visualização: {mean_low:.10f} Desvio Padrão Visualização: {std_low:.10f}\\n\")"
      ],
      "metadata": {
        "id": "lLzYtxa3jv3C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8a98bf1-3bd9-4b86-9123-9bb6efe66667"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PCA\n",
            "Média Silhueta Original: 0.0860648712739905 Desvio Padrão Original: 0.02080142865863332\n",
            "Média Silhueta Visualização: 0.4175589453 Desvio Padrão Visualização: 0.0468529850\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "TRIMAP"
      ],
      "metadata": {
        "id": "h1FjdURYjkY8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mean_high,mean_low,std_high,std_low = coeficiente_silhueta(X,X_trimap)\n",
        "\n",
        "print(\"TriMap\")\n",
        "print(f\"Média Silhueta Original: {mean_high} Desvio Padrão Original: {std_high}\")\n",
        "print(f\"Média Silhueta Visualização: {mean_low:.10f} Desvio Padrão Visualização: {std_low:.10f}\\n\")"
      ],
      "metadata": {
        "id": "5DvfQhtGjl7K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05b487c9-9bb7-4b78-90fc-8c3aee87fe0e"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TriMap\n",
            "Média Silhueta Original: 0.0860648712739905 Desvio Padrão Original: 0.02080142865863332\n",
            "Média Silhueta Visualização: 0.5328438878 Desvio Padrão Visualização: 0.0486698411\n",
            "\n"
          ]
        }
      ]
    }
  ]
}