Agglomerative.ipynb

{
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   "attachments": {},
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   "source": [
    "# <font color='orange'>Clustering: Agglomerative\n",
    "--- \n",
    "</font>\n",
    "\n",
    "*@j*"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este algoritmo es del tipo `Hierarchical`, es el más usado dentro de este tipo y funciona seleccionando clusters individuales (igual a la cantidad de elementos del DF) que se van juntanto con otros hasta llegar a 1 único cluster que contiene a todos los demás.\n",
    "\n",
    "Funciona a través de una `matriz de proximidad` (n x n) de cada uno de los clusters individuales, que a través de un proceso iterativo va fusionando los 2 más cercanos y actualizando y reduciendo su tamaño hasta al final generar un `dendograma`.\n",
    "\n",
    "Puede ser más lento que `KMeans` cuando el DataFrame es grande, pero siempre generará los mismos grupos.\n",
    "\n",
    "Problema: Agrupar a los clientes de una empresa según sus características."
   ]
  },
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   "source": [
    "Librerías:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>edad</th>\n",
       "      <th>genero</th>\n",
       "      <th>nivel_ingresos</th>\n",
       "      <th>estudios</th>\n",
       "      <th>total_empleos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   edad  genero  nivel_ingresos  estudios  total_empleos\n",
       "0    19       1               1         0              1\n",
       "1    18       0               0         0              0\n",
       "2    29       1               3         2              2\n",
       "3    25       1               2         1              1\n",
       "4    27       0               2         1              3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data.csv')\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crear nArray:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.values"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crear matriz de proximidad:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import euclidean_distances\n",
    "\n",
    "Mat = euclidean_distances(X,X)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crear modelo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "AGG = AgglomerativeClustering(n_clusters=4, linkage='ward')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Entrenar modelo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\JATJ\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\scipy\\cluster\\hierarchy.py:834: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n",
      "  return linkage(y, method='ward', metric='euclidean')\n"
     ]
    },
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      ],
      "text/plain": [
       "AgglomerativeClustering(n_clusters=4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AGG.fit(Mat)\n",
    "AGG"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtener las clasificaciones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = AGG.labels_"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agregar columna de clasificaciones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>edad</th>\n",
       "      <th>genero</th>\n",
       "      <th>nivel_ingresos</th>\n",
       "      <th>estudios</th>\n",
       "      <th>total_empleos</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
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       "      <td>2</td>\n",
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      "text/plain": [
       "   edad  genero  nivel_ingresos  estudios  total_empleos  labels\n",
       "0    19       1               1         0              1       2\n",
       "1    18       0               0         0              0       2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['labels'] = labels\n",
    "df.head(2)"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "Librerías para graficar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style(\"darkgrid\")   "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gráfica 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "sns.scatterplot(data=df, x='edad', y='total_empleos', hue='labels', palette='bright', s=70)\n",
    "\n",
    "plt.title(\"Total de empleos\", fontdict={'fontsize': 20}, pad=15)\n",
    "plt.show();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gráfica 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "sns.scatterplot(data=df, x='edad', y='nivel_ingresos', hue='labels', palette='bright', s=70)\n",
    "\n",
    "plt.title(\"Nivel de Ingresos\", fontdict={'fontsize': 20}, pad=15)\n",
    "plt.show();"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4 (tags/v3.9.4:1f2e308, Apr  6 2021, 13:40:21) [MSC v.1928 64 bit (AMD64)]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "b5f98589597bf928df7c3b4b7221661d9594090b96ffc8472b8fdbc1b88b3ff7"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}