{
 "nbformat": 4,
 "nbformat_minor": 5,
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3"
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clusterização com K-Means — Wine\n",
    "\n",
    "Material introdutório — Prof. Alex Souza\n",
    "\n",
    "Pergunta: quais vinhos têm perfis químicos semelhantes? Agrupamento procura estrutura sem uma resposta-alvo no treinamento. K-Means aproxima cada amostra do centro mais próximo, buscando reduzir distâncias ao quadrado. É simples, mas depende de escala, número de grupos e formato dos dados. Aqui não estamos segmentando clientes; esse é apenas um possível uso empresarial da mesma tarefa."
   ],
   "id": "celula-000"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Como executar\n",
    "Abra no Jupyter ou Google Colab e execute as células na ordem. As bases vêm no scikit-learn; não há download externo de dados. O CSV correspondente também acompanha este material. Instale as bibliotecas apenas se necessário."
   ],
   "id": "celula-001"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Execute esta linha se as bibliotecas não estiverem instaladas.\n",
    "# %pip install scikit-learn pandas matplotlib\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "print(\"Versão do scikit-learn:\", sklearn.__version__)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Versão do scikit-learn: 1.8.0\n"
     ]
    }
   ],
   "execution_count": 1,
   "id": "celula-002"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fonte e dicionário de dados\n",
    "\n",
    "178 amostras de vinho, 13 atributos químicos. A base original tem três cultivares, mas os rótulos serão excluídos do agrupamento. As escalas variam; consulte a descrição original para contexto, sem presumir unidades ausentes.\n",
    "\n",
    "| Coluna | Significado |\n",
    "|---|---|\n",
    "| alcohol | Álcool |\n",
    "| malic_acid | Ácido málico |\n",
    "| ash | Cinzas |\n",
    "| alcalinity_of_ash | Alcalinidade das cinzas |\n",
    "| magnesium | Magnésio |\n",
    "| total_phenols | Fenóis totais |\n",
    "| flavanoids | Flavonoides |\n",
    "| nonflavanoid_phenols | Fenóis não flavonoides |\n",
    "| proanthocyanins | Proantocianinas |\n",
    "| color_intensity | Intensidade da cor |\n",
    "| hue | Tonalidade |\n",
    "| od280/od315_of_diluted_wines | Razão OD280/OD315 de vinhos diluídos |\n",
    "| proline | Prolina |\n",
    "| target | Cultivar original (0, 1, 2); não usado para agrupar |"
   ],
   "id": "celula-003"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "from sklearn.datasets import load_wine\n",
    "# Carrega os dados locais distribuídos com o scikit-learn.\n",
    "base = load_wine(as_frame=True)\n",
    "dados = base.frame.copy()\n",
    "# Consulte também o CSV incluído: pd.read_csv(\"03_clusterizacao_wine.csv\")\n",
    "print(\"Linhas e colunas:\", dados.shape)\n",
    "print(dados.head().to_string())\n",
    "print(\"Ausentes por coluna:\\n\", dados.isna().sum())"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Linhas e colunas: (178, 14)\n",
      "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  od280/od315_of_diluted_wines  proline  target\n",
      "0    14.23        1.71  2.43               15.6      127.0           2.80        3.06                  0.28             2.29             5.64  1.04                          3.92   1065.0       0\n",
      "1    13.20        1.78  2.14               11.2      100.0           2.65        2.76                  0.26             1.28             4.38  1.05                          3.40   1050.0       0\n",
      "2    13.16        2.36  2.67               18.6      101.0           2.80        3.24                  0.30             2.81             5.68  1.03                          3.17   1185.0       0\n",
      "3    14.37        1.95  2.50               16.8      113.0           3.85        3.49                  0.24             2.18             7.80  0.86                          3.45   1480.0       0\n",
      "4    13.24        2.59  2.87               21.0      118.0           2.80        2.69                  0.39             1.82             4.32  1.04                          2.93    735.0       0\n",
      "Ausentes por coluna:\n",
      " alcohol                         0\n",
      "malic_acid                      0\n",
      "ash                             0\n",
      "alcalinity_of_ash               0\n",
      "magnesium                       0\n",
      "total_phenols                   0\n",
      "flavanoids                      0\n",
      "nonflavanoid_phenols            0\n",
      "proanthocyanins                 0\n",
      "color_intensity                 0\n",
      "hue                             0\n",
      "od280/od315_of_diluted_wines    0\n",
      "proline                         0\n",
      "target                          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "execution_count": 2,
   "id": "celula-004"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descrição original da fonte\n",
    "A descrição abaixo preserva as referências e observações distribuídas com a base."
   ],
   "id": "celula-005"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "print(base.DESCR)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      ".. _wine_dataset:\n",
      "\n",
      "Wine recognition dataset\n",
      "------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      ":Number of Instances: 178\n",
      ":Number of Attributes: 13 numeric, predictive attributes and the class\n",
      ":Attribute Information:\n",
      "    - Alcohol\n",
      "    - Malic acid\n",
      "    - Ash\n",
      "    - Alcalinity of ash\n",
      "    - Magnesium\n",
      "    - Total phenols\n",
      "    - Flavanoids\n",
      "    - Nonflavanoid phenols\n",
      "    - Proanthocyanins\n",
      "    - Color intensity\n",
      "    - Hue\n",
      "    - OD280/OD315 of diluted wines\n",
      "    - Proline\n",
      "    - class:\n",
      "        - class_0\n",
      "        - class_1\n",
      "        - class_2\n",
      "\n",
      ":Summary Statistics:\n",
      "\n",
      "============================= ==== ===== ======= =====\n",
      "                                Min   Max   Mean     SD\n",
      "============================= ==== ===== ======= =====\n",
      "Alcohol:                      11.0  14.8    13.0   0.8\n",
      "Malic Acid:                   0.74  5.80    2.34  1.12\n",
      "Ash:                          1.36  3.23    2.36  0.27\n",
      "Alcalinity of Ash:            10.6  30.0    19.5   3.3\n",
      "Magnesium:                    70.0 162.0    99.7  14.3\n",
      "Total Phenols:                0.98  3.88    2.29  0.63\n",
      "Flavanoids:                   0.34  5.08    2.03  1.00\n",
      "Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\n",
      "Proanthocyanins:              0.41  3.58    1.59  0.57\n",
      "Colour Intensity:              1.3  13.0     5.1   2.3\n",
      "Hue:                          0.48  1.71    0.96  0.23\n",
      "OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\n",
      "Proline:                       278  1680     746   315\n",
      "============================= ==== ===== ======= =====\n",
      "\n",
      ":Missing Attribute Values: None\n",
      ":Class Distribution: class_0 (59), class_1 (71), class_2 (48)\n",
      ":Creator: R.A. Fisher\n",
      ":Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      ":Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML Wine recognition datasets.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n",
      "\n",
      "The data is the results of a chemical analysis of wines grown in the same\n",
      "region in Italy by three different cultivators. There are thirteen different\n",
      "measurements taken for different constituents found in the three types of\n",
      "wine.\n",
      "\n",
      "Original Owners:\n",
      "\n",
      "Forina, M. et al, PARVUS -\n",
      "An Extendible Package for Data Exploration, Classification and Correlation.\n",
      "Institute of Pharmaceutical and Food Analysis and Technologies,\n",
      "Via Brigata Salerno, 16147 Genoa, Italy.\n",
      "\n",
      "Citation:\n",
      "\n",
      "Lichman, M. (2013). UCI Machine Learning Repository\n",
      "[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\n",
      "School of Information and Computer Science.\n",
      "\n",
      ".. dropdown:: References\n",
      "\n",
      "    (1) S. Aeberhard, D. Coomans and O. de Vel,\n",
      "    Comparison of Classifiers in High Dimensional Settings,\n",
      "    Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of\n",
      "    Mathematics and Statistics, James Cook University of North Queensland.\n",
      "    (Also submitted to Technometrics).\n",
      "\n",
      "    The data was used with many others for comparing various\n",
      "    classifiers. The classes are separable, though only RDA\n",
      "    has achieved 100% correct classification.\n",
      "    (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))\n",
      "    (All results using the leave-one-out technique)\n",
      "\n",
      "    (2) S. Aeberhard, D. Coomans and O. de Vel,\n",
      "    \"THE CLASSIFICATION PERFORMANCE OF RDA\"\n",
      "    Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of\n",
      "    Mathematics and Statistics, James Cook University of North Queensland.\n",
      "    (Also submitted to Journal of Chemometrics).\n",
      "\n"
     ]
    }
   ],
   "execution_count": 3,
   "id": "celula-006"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Padronizar e explorar o número de grupos\n",
    "Cada atributo deve ter influência comparável na distância. StandardScaler transforma as escalas. Exploramos k de 2 a 6 pela silhueta: valores mais altos indicam melhor separação relativa nesse critério. Isso não comprova utilidade de negócio. Não escolhemos k com base no target."
   ],
   "id": "celula-007"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "# Remove explicitamente qualquer alvo: só atributos químicos entram no modelo.\n",
    "X = dados.drop(columns='target')\n",
    "escalador = StandardScaler()\n",
    "X_padrao = escalador.fit_transform(X)\n",
    "resultados = []\n",
    "for k in range(2,7):\n",
    "    # Várias inicializações reduzem a dependência de um ponto de partida ruim.\n",
    "    candidato = KMeans(n_clusters=k,n_init=10,random_state=42)\n",
    "    grupos = candidato.fit_predict(X_padrao)\n",
    "    resultados.append({'k':k,'silhueta':silhouette_score(X_padrao,grupos)})\n",
    "avaliacao = pd.DataFrame(resultados)\n",
    "print(avaliacao.to_string(index=False))\n",
    "k_escolhido = int(avaliacao.loc[avaliacao['silhueta'].idxmax(),'k'])\n",
    "modelo = KMeans(n_clusters=k_escolhido,n_init=10,random_state=42)\n",
    "grupos = modelo.fit_predict(X_padrao)\n",
    "print('k selecionado neste intervalo:',k_escolhido)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " k  silhueta\n",
      " 2  0.259317\n",
      " 3  0.284859\n",
      " 4  0.260170\n",
      " 5  0.201619\n",
      " 6  0.237167\n",
      "k selecionado neste intervalo: 3\n"
     ]
    }
   ],
   "execution_count": 4,
   "id": "celula-008"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpretar os grupos\n",
    "Os números dos clusters são identificadores arbitrários, sem ordem de qualidade. A tabela de médias ajuda a caracterizar perfis. A projeção PCA usa duas dimensões só para visualizar; K-Means foi ajustado nos 13 atributos padronizados. Sobreposição no gráfico não resume toda a separação.\n",
    "\n",
    "Este exercício é exploratório e descreve a base inteira, por isso não usa treino/teste. Para atribuir novos vinhos, reutilize o mesmo escalador e modelo. Avalie estabilidade e utilidade antes de decisões reais."
   ],
   "id": "celula-009"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "from sklearn.decomposition import PCA\n",
    "# Cria uma cópia para análise descritiva dos grupos.\n",
    "perfil = X.copy()\n",
    "perfil['cluster'] = grupos\n",
    "print('Quantidade por grupo:\\n',perfil['cluster'].value_counts().sort_index())\n",
    "print('Médias nas escalas originais:\\n',perfil.groupby('cluster').mean().round(2).to_string())\n",
    "pca = PCA(n_components=2)\n",
    "projecao = pca.fit_transform(X_padrao)\n",
    "plt.figure(figsize=(8,5))\n",
    "for grupo in sorted(set(grupos)):\n",
    "    mascara = grupos == grupo\n",
    "    plt.scatter(projecao[mascara,0],projecao[mascara,1],label=f'Grupo {grupo}',alpha=0.7)\n",
    "plt.xlabel('Componente 1'); plt.ylabel('Componente 2'); plt.legend()\n",
    "plt.title('Visualização dos grupos em duas dimensões')\n",
    "print('Variação representada no gráfico:',pca.explained_variance_ratio_.sum())"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Quantidade por grupo:\n",
      " cluster\n",
      "0    65\n",
      "1    51\n",
      "2    62\n",
      "Name: count, dtype: int64\n",
      "Médias nas escalas originais:\n",
      "          alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  od280/od315_of_diluted_wines  proline\n",
      "cluster                                                                                                                                                                                         \n",
      "0          12.25        1.90  2.23              20.06      92.74           2.25        2.05                  0.36             1.62             2.97  1.06                          2.80   510.17\n",
      "1          13.13        3.31  2.42              21.24      98.67           1.68        0.82                  0.45             1.15             7.23  0.69                          1.70   619.06\n",
      "2          13.68        2.00  2.47              17.46     107.97           2.85        3.00                  0.29             1.92             5.45  1.07                          3.16  1100.23\n",
      "Variação representada no gráfico: 0.5540633835693527\n"
     ]
    },
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "image/png": 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"
     }
    }
   ],
   "execution_count": 5,
   "id": "celula-010"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussão em sala\n",
    "Explique com suas palavras a pergunta respondida, as entradas utilizadas e o significado da saída. Que informação adicional seria necessária para usar esse modelo em uma organização?\n",
    "\n",
    "## Referências\n",
    "- [Base e documentação oficial](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_wine.html)\n",
    "- [Guia de aprendizado de máquina](https://scikit-learn.org/stable/user_guide.html)\n",
    "\n",
    "Dados pequenos servem para ensino; resultados não garantem desempenho em produção."
   ],
   "id": "celula-011"
  }
 ]
}