practica_3_IA_C.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "41eab4c8-0882-435f-a652-0adb7d645e48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Para mas información, consulta el GitHub [https://github.com/Zygmut/Practica_3_IA]\n",
    "# Autores: Palmer Pérez, Rubén; Torres Torres, Marc; Orr, Kieran Donal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a60c2ad8",
   "metadata": {},
   "source": [
    "# Setup\n",
    "Importamos todos los paquetes necesarios para la práctica. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fc2ffd68-c543-49bd-9413-e0e84c0ffa93",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.linear_model import LogisticRegression, Perceptron\n",
    "from sklearn.ensemble import RandomForestClassifier "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d6bb1b",
   "metadata": {},
   "source": [
    "Creamos un conjunto de variables para facil acceso y cambio de parámetros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "99381966",
   "metadata": {},
   "outputs": [],
   "source": [
    "TEST_SIZE = 0.33             # Proportion for test size in relation to the dataset\n",
    "ALTERNATIVE_MODE = False     # Quality (0-10) or (\"bad\", \"average\", \"good\")\n",
    "QUALITY_THRESHOLD = 6        # Threshold that defines a range for each quality of wine (\"bad\", \"average\", \"good\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8625d1cb",
   "metadata": {},
   "source": [
    "Definimos una variable `DEBUG` (y su método de uso) que, en caso de estar activada, nos dará información adicional a lo largo de la ejecución del proyecto "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d6fbe7e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "DEBUG = True \n",
    "\n",
    "def debugPrint(string):\n",
    "    if DEBUG : print(f\"[🐛] {string}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30db9b88",
   "metadata": {},
   "source": [
    "# El dataset\n",
    "Cargamos los datasets y les añadimos la columna `type` que nos permitara reconocer de que tipo son una vez juntados los datasets. Si `type = 0` el vino será blanco, de lo contrario sera rojo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5ca37e25-3df9-436a-85d4-435b969ae0c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   type  fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "0     0            7.0              0.27         0.36            20.7   \n",
       "1     0            6.3              0.30         0.34             1.6   \n",
       "2     0            8.1              0.28         0.40             6.9   \n",
       "3     0            7.2              0.23         0.32             8.5   \n",
       "4     0            7.2              0.23         0.32             8.5   \n",
       "\n",
       "   chlorides  free sulfur dioxide  total sulfur dioxide  density    pH  \\\n",
       "0      0.045                 45.0                 170.0   1.0010  3.00   \n",
       "1      0.049                 14.0                 132.0   0.9940  3.30   \n",
       "2      0.050                 30.0                  97.0   0.9951  3.26   \n",
       "3      0.058                 47.0                 186.0   0.9956  3.19   \n",
       "4      0.058                 47.0                 186.0   0.9956  3.19   \n",
       "\n",
       "   sulphates  alcohol  quality  \n",
       "0       0.45      8.8        6  \n",
       "1       0.49      9.5        6  \n",
       "2       0.44     10.1        6  \n",
       "3       0.40      9.9        6  \n",
       "4       0.40      9.9        6  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white = pd.read_csv(\"dataset/winequality-white.csv\", sep=';')\n",
    "white.insert(0, 'type', 0)\n",
    "red = pd.read_csv(\"dataset/winequality-red.csv\", sep= ';')\n",
    "red.insert(0, 'type', 1)\n",
    "df = pd.concat([white, red])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26086f36",
   "metadata": {},
   "source": [
    "Preparamos los datos del dataset eliminando todos los posibles valores nulos y transformando los valores que no sean `np.float64` o `np.int64` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6f5728a5-6567-4f55-8219-eade476c8e37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparacion del dataset\n",
    "\n",
    "labelencoder = LabelEncoder()\n",
    "\n",
    "if not df[df.isna().any(axis=1)].shape[0] == 0 :\n",
    "    debugPrint(\"Valores nulos eliminados para el dataframe\")\n",
    "    \n",
    "df.dropna(inplace=True) # Eliminacion de valores nullos\n",
    "\n",
    "for column in df.columns: # Transformar posibles valores no numericos\n",
    "    if not df[column].dtype in (np.float64, np.int64):\n",
    "        df[column] = labelencoder.fit_transform(df[column])\n",
    "        debugPrint(f\"Los valores de {column} se han transformado a numericos\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a797e1c",
   "metadata": {},
   "source": [
    "## Visualización de datos\n",
    "En este apartado visualizaremos los datos para un entendimiento gráfico de nuestras carácteres.\n",
    "\n",
    "Primero visualizaremos la disposición de nuestros datos con respecto a la **calidad**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a547235f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='count'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.set(style=\"white\")\n",
    "sns.countplot(x=df[\"quality\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdd32ac0",
   "metadata": {},
   "source": [
    "Como podemos ver aqui los datos estan distribuidos como una distribución normal, hay muchos datos por el centro en los valores de 5, 6 y 7 y pocos datos en los extremos. Eso es de esperar con una muestra de tamaño tan grande pero, el hecho de que haya tan pocos datos en los extremos nos va a causar problemas luego a la hora de hacer las predicciones sino categorizamos.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e4df508",
   "metadata": {},
   "source": [
    "Otra representación que puede ser interesante sería poder ver la diferencia de calidad entre los vinos tintos y los vinos blancos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "259fb9ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='count'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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99dm+fXteeeWVVCqVwePjx4+v2jAAgOFmr8LsxhtvzM0335zDDz988FhdXV3uv//+qg0DABhu9irM7r333tx333054ogjqr0HAGDY2quPyzjyyCNFGQBAle3VGbOWlpYsWrQoZ5xxRkaNGjV43DVmAAD7zl6F2d13350k6ezsHDzmGjMAgH1rr8LsgQceqPYOAIBhb6/C7JZbbnnb45deeuk+HQMAMJztVZj96Ec/Gvz37t278/jjj6elpaVqowAAhqO9/oDZ/62rqyvz58+vyiAAgOFqrz4u45cdccQR2b59+77eAgAwrP3G15hVKpVs2rTpTX8FAACA395vfI1Z8vMPnL366qurMggAYLj6ja4x2759e/bs2ZOxY8dWdRQAwHC0V2G2devWfO5zn8uOHTsyMDCQQw89NN/4xjfy/ve/v9r7AACGjb26+P/LX/5yLr/88jz++OPZsGFDPvvZz+a6666r9jYAgGFlr8LshRdeyHnnnTd4+/zzz89LL71UtVEAAMPRXoVZf39/Xn755cHbL774YrX2AAAMW3t1jdmnPvWpfPKTn8y0adNSV1eXVatW5U//9E+rvQ0AYFjZqzNmEydOTJL09fXlmWeeSVdXV84666yqDgMAGG726ozZtddem4suuijt7e3p7e3Nv/zLv2TevHn5p3/6p2rvAwAYNvbqjNlLL72U9vb2JElDQ0MuueSSdHd3V3UYAMBws9cX/3d1dQ3e3rlzZyqVStVGAQAMR3v1VuYll1ySc889N3/yJ3+Surq6rF271p9kAgDYx/YqzGbOnJnjjjsujz76aEaMGJFPf/rT+cAHPlDtbQAAw8pehVmSjBs3LuPGjavmFgCAYW2vrjEDAKD6hBkAQCGEGQBAIYQZAEAhhBkAQCGEGQBAIYQZAEAhhBkAQCGEGQBAIYQZAEAhhBkAQCGEGQBAIYQZAEAhhBkAQCGEGQBAIYQZAEAhhBkAQCGEGQBAIYQZAEAhhBkAQCGEGQBAIYQZAEAhhBkAQCGqGmY33HBDzj777EyfPj233HJLkmTt2rWZMWNGJk+enCVLlgw+dvPmzWlra8uUKVMyf/787Nmzp5rTAACKU7UwW79+fR599NGsWLEi3/3ud3Prrbfmhz/8YebNm5elS5dm1apV2bRpU9asWZMkmTt3bhYsWJDVq1enUqlk2bJl1ZoGAFCkqoXZRz7ykXzrW99KfX19XnjhhfT39+fVV1/N2LFjM2bMmNTX12fGjBnp7OzM9u3b09PTkwkTJiRJ2tra0tnZWa1pAABFqupbmSNHjsyNN96Y6dOnp6WlJTt27EhjY+Pg/U1NTenq6nrL8cbGxnR1dVVzGgBAcap+8f+VV16ZdevW5fnnn8+zzz6burq6wfsqlUrq6uoyMDDwtscBAIaTqoXZM888k82bNydJDj744EyePDmPPfZYuru7Bx/T3d2dpqamNDc3v+n4zp0709TUVK1pAABFqlqYbdu2LR0dHdm9e3d2796d+++/PxdccEG2bNmSrVu3pr+/PytXrkxra2tGjx6dhoaGbNiwIUmyfPnytLa2VmsaAECR6qv1xBMnTszGjRtz7rnnZsSIEZk8eXKmT5+eww47LLNnz05vb28mTpyYqVOnJkkWL16cjo6O7Nq1K+PHj097e3u1pgEAFKlqYZYks2fPzuzZs990rKWlJStWrHjLY8eNG5e77rqrmnMAAIrmk/8BAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAAohzAAACiHMAAAKIcwAAApR1TC76aabMn369EyfPj2LFi1KkqxduzYzZszI5MmTs2TJksHHbt68OW1tbZkyZUrmz5+fPXv2VHMaAEBxqhZma9euzcMPP5x77rkn9957b5566qmsXLky8+bNy9KlS7Nq1aps2rQpa9asSZLMnTs3CxYsyOrVq1OpVLJs2bJqTQMAKFLVwqyxsTHXXnttDjrooIwcOTLvf//78+yzz2bs2LEZM2ZM6uvrM2PGjHR2dmb79u3p6enJhAkTkiRtbW3p7Oys1jQAgCJVLcyOPfbYwdB69tln86//+q+pq6tLY2Pj4GOamprS1dWVHTt2vOl4Y2Njurq6qjUNAKBIVb/4/7/+679y2WWX5eqrr86YMWNSV1c3eF+lUkldXV0GBgbe9jgA/LI9ff21nnDA8FodeOqr+eQbNmzIlVdemXnz5mX69OlZv359uru7B+/v7u5OU1NTmpub33R8586daWpqquY0AA5Q9SNH5Kvz76r1jAPCvL+bWesJ/Iaqdsbs+eefz1/8xV9k8eLFmT59epLk+OOPz5YtW7J169b09/dn5cqVaW1tzejRo9PQ0JANGzYkSZYvX57W1tZqTQMAKFLVzpjdfPPN6e3tzcKFCwePXXDBBVm4cGFmz56d3t7eTJw4MVOnTk2SLF68OB0dHdm1a1fGjx+f9vb2ak0DAChS1cKso6MjHR0db3vfihUr3nJs3Lhxuesup6YBgOHLJ/8DABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABRCmAEAFEKYAQAUQpgBABSiqmG2a9eufOxjH8u2bduSJGvXrs2MGTMyefLkLFmyZPBxmzdvTltbW6ZMmZL58+dnz5491ZwFAFCkqoXZk08+mQsvvDDPPvtskqSnpyfz5s3L0qVLs2rVqmzatClr1qxJksydOzcLFizI6tWrU6lUsmzZsmrNAgAoVtXCbNmyZfniF7+YpqamJMnGjRszduzYjBkzJvX19ZkxY0Y6Ozuzffv29PT0ZMKECUmStra2dHZ2VmsWAECx6qv1xH/3d3/3pts7duxIY2Pj4O2mpqZ0dXW95XhjY2O6urqqNQsAoFj77eL/gYGB1NXVDd6uVCqpq6t7x+MAAMPNfguz5ubmdHd3D97u7u5OU1PTW47v3Llz8O1PAIDhZL+F2fHHH58tW7Zk69at6e/vz8qVK9Pa2prRo0enoaEhGzZsSJIsX748ra2t+2sWAEAxqnaN2S9raGjIwoULM3v27PT29mbixImZOnVqkmTx4sXp6OjIrl27Mn78+LS3t++vWQAAxah6mD3wwAOD/25pacmKFSve8phx48blrrvuqvYUAICi+eR/AIBCCDMAgEIIMwCAQggzAIBCCDMAgEIIMwCAQggzAIBCCDMAgEIIMwCAQggzAIBCCDMAgEIIMwCAQggzAIBCCDMAgEIIMwCAQggzAIBCCDMAgEIIMwCAQggzAIBCCDMAgEIIMwCAQggzAIBCCDMAgEIIMwCAQggzAIBCCDMAgEIIMwCAQggzAIBCCDMAgEIIMwCAQggzqmZgT1+tJxwwvFYAJEl9rQcwdL2rfmQ2LLq81jMOCCde/X9qPQGAAjhjBgBQCGEGAFAIYQYAUAhhBgBQCGEGAFAIYQYAUAhhBgBQCGEGAFAIYQYAUAhhBgBQCGEGAFAIYQYAUAhhBgBQCGEGAFAIYQYAUAhhBgBQCGEGAFAIYQYAUAhhBgBQCGEGAFAIYQYAUAhhBgBQCGEGAFAIYQYAUAhhBgBQCGEGAPxKe/r6aj3hgPHbvlb1+2gHADBE1Y8cmb//mz+v9YwDwl9e/43f6uudMQMAKIQwAwAohDADACiEMAMAKMSwCbPdff21nnDA8FoBQG0Mm9/KPGjkiMy6+vZazzggfHvRRbWeAADD0rA5YwYAUDphBgBQiKLC7Hvf+17OPvvsTJ48Obff7m1HAGB4KeYas66urixZsiR33313DjrooFxwwQU5+eSTc8wxx9R6GgDAflFMmK1duzYf/ehH83u/93tJkilTpqSzszOf//znf+XX9ff//DcIf/rTn/7a79H7+su/7cxhYdu2bfvsubp/1rPPnmso25evec/Lr++z5xrK9uVr/mKv/873xr58zXe99tI+e66hbF++5j977Y199lxD2a97zX/RK7/ol19WTJjt2LEjjY2Ng7ebmpqycePGX/t13d3dSZKLLvKbhPvKGd+/sdYThp9lZ9R6wbBzxj94zfe3vz3Da76/ffdfF9d6wrDz7dV79995d3d3xo4d+5bjxYTZwMBA6urqBm9XKpU33X4nxx13XG6//fY0NjZmxIgR1ZwIAPBb6e/vT3d3d4477ri3vb+YMGtubs4TTzwxeLu7uztNTU2/9utGjRqVk046qZrTAAD2mbc7U/YLxfxW5h//8R9n3bp1efHFF/PGG2/kvvvuS2tra61nAQDsN8WcMTviiCMyZ86ctLe3p6+vLzNnzsyHP/zhWs8CANhv6iqVSqXWIwAAKOitTACA4U6YAQAUQpgBABRCmAEAFKKY38ocbm644YasXr06dXV1mTlzZi699NJaTxo2vva1r+Wll17KwoULaz1lyLv44ovz4osvpr7+5/9T8+UvfznHH398jVcNbQ888EBuuummvPHGGznllFPS0dFR60lD2p133pnbbrtt8Pa2bdtyzjnnZMGCBTVcNfQtX748//iP/5gkaW1tzTXXXFPjRftQhf3uscceq1xwwQWVvr6+yhtvvFGZNGlS5Zlnnqn1rGFh7dq1lZNPPrlyzTXX1HrKkDcwMFA59dRTK319fbWeMmz85Cc/qZx66qmV559/vrJ79+7KhRdeWPn3f//3Ws8aNn70ox9VzjrrrMoLL7xQ6ylD2uuvv175oz/6o8oLL7xQ6evrq8ycObPyyCOP1HrWPuOtzBr4yEc+km9961upr6/PCy+8kP7+/hxyyCG1njXkvfzyy1myZEmuuOKKWk8ZFn784x8nSS677LJ8/OMff9NZBarj+9//fs4+++w0Nzdn5MiRWbJkiTOU+9GXvvSlzJkzJ4cddlitpwxp/f39GRgYyBtvvJE9e/Zkz549aWhoqPWsfUaY1cjIkSNz4403Zvr06WlpackRRxxR60lD3oIFCzJnzpy85z3vqfWUYeHVV19NS0tLvv71r+eb3/xm7rjjjjzyyCO1njWkbd26Nf39/bniiityzjnn5Nvf/nZ+93d/t9azhoW1a9emp6cn06ZNq/WUIe/d7353vvCFL2TatGmZOHFiRo8enT/8wz+s9ax9RpjV0JVXXpl169bl+eefz7Jly2o9Z0i78847c+SRR6alpaXWU4aNE044IYsWLcrv/M7v5LDDDsvMmTOzZs2aWs8a0vr7+7Nu3bp89atfzXe+851s3Lgx99xzT61nDQt33HGHa4X3kx/+8If57ne/mwcffDAPPfRQ3vWud+Xmm2+u9ax9RpjVwDPPPJPNmzcnSQ4++OBMnjw5Tz/9dI1XDW2rVq3KI488knPOOSc33nhjHnjggXz1q1+t9awh7Yknnsi6desGb1cqlcFfAqA63vve96alpSWHHXZYRo0alTPPPDMbN26s9awhb/fu3Xn88cdz+umn13rKsPDwww+npaUlhx9+eA466KC0tbVl/fr1tZ61zwizGti2bVs6Ojqye/fu7N69O/fff39OPPHEWs8a0m655ZasXLkyy5cvz5VXXpnTTz898+bNq/WsIe1nP/tZFi1alN7e3uzatSv33HNPzjrrrFrPGtImTZqUhx9+OK+++mr6+/vz0EMPZfz48bWeNeQ9/fTTOfroo10rvJ+MGzcua9euzeuvv55KpZIHHnggH/rQh2o9a5/xf19rYOLEidm4cWPOPffcjBgxIpMnT8706dNrPQv2qUmTJuXJJ5/Mueeem4GBgcyaNSsnnHBCrWcNaccff3wuv/zyzJo1K319fTnllFNy/vnn13rWkPfcc8+lubm51jOGjVNPPTU/+MEP0tbWlpEjR+ZDH/pQ/uzP/qzWs/YZf8QcAKAQ3soEACiEMAMAKIQwAwAohDADACiEMAMAKIQwA/g1HnvssXzsYx9Lktxwww259957kyQ33XRT/u3f/q2Gy4ChxueYAfwGvvCFLwz++7HHHssxxxxTwzXAUCPMgCHrhhtuyPe+970ceuihOemkk7Jp06aMHj06xx57bD796U8nSa699trB2w8++GC+8Y1vZPfu3XnxxRdz7rnn5qqrrnrTc/7i8aNGjcqmTZuyaNGi7N69O3/7t3+bZcuW5X3ve1+S5JJLLsmnPvWpnHnmmfv7xwYOYMIMGJLuu+++3Hfffbn33nvT0NCQz33uc7/y8ZVKJf/8z/+chQsX5uijj05XV1cmTZqU9vb2t338RRddlM7Ozlx00UWZOnVq/vM//zN33nlnrr766vzkJz/Js88+m0mTJlXjRwOGMNeYAUPSo48+mrPOOivvfve7M3LkyHzyk5/8lY+vq6vLP/zDP+Spp57KTTfdlIULF6ZSqeSNN97Yq+83a9asLF++PH19ffnOd76TmTNnZsSIEfviRwGGEWEGDEkNDQ35339xbuTIkUl+HmD/+3hfX1+S5PXXX895552Xp556Kn/wB3+Qq6++OvX19dnbv1r3vve9Lx/84Adz//33Z+XKlfnEJz6xD38aYLgQZsCQdNppp6WzszOvvPJKBgYGBn+T8tBDD82mTZuSJF1dXVm/fn2SZOvWrdm1a1euuuqqnH766Xnssceye/fuDAwMvOP3GDFiRPbs2TN4e9asWVm0aFE+/OEP54gjjqjeDwcMWa4xA4akk08+Oe3t7Zk1a1YaGhoyevToJMnFF1+cv/7rv86UKVNy1FFH5aMf/WiS5IMf/GBOO+20TJs2LQcddFA+8IEP5JhjjsnWrVtz0EEHve33OP300/P3f//36evry3nnnZdJkyalo6MjF1xwwX77OYGhpa6yt+fpAQ5gnZ2duf3223PrrbdW7Xv8x3/8Rzo6OrJy5crU1dVV7fsAQ5czZgD7wDXXXJP169dnyZIlogz4/+aMGQBAIVz8DwBQCGEGAFAIYQYAUAhhBgBQCGEGAFAIYQYAUIj/C6Q3+ROB5DTTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.set(style=\"white\")\n",
    "sns.countplot(x=white[\"quality\"])\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.set(style=\"white\")\n",
    "sns.countplot(x=red[\"quality\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d47489",
   "metadata": {},
   "source": [
    "Podemos observar que, ademas de tener muchas mas muestras en el vino blanco, el vino tinto suele tener notas más bajas. Podemos calcular una media rápidamente para poder reasegurarlo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b70e6b87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Media de los vinos blancos: 5.87790935075541\t Media de los vinos tintos: 5.6360225140712945\n"
     ]
    }
   ],
   "source": [
    "print(\"Media de los vinos blancos: {}\\t Media de los vinos tintos: {}\".format(np.mean(white[\"quality\"]), np.mean(red[\"quality\"])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "426018c4",
   "metadata": {},
   "source": [
    "Podemos tambien prinetar todos los posibles valores de las caracteristicas para tener una idea grafica de como es nuestro dataset completo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aecb8aec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Histogramas\n",
    "df_show = df.drop(\"type\", axis = 1)\n",
    "df_show.hist(figsize=(18,12))\n",
    "\n",
    "# Densidad de datos \n",
    "df_show.plot(kind='density', subplots = True, layout=(4,3), sharex=False, figsize=(18,12))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44e716ce",
   "metadata": {},
   "source": [
    "## Matriz de correlación\n",
    "Como ya hemos visto en la visualización de los datos tenemos caracteristicas que, respecto a calidad, no nos ofrecen mucha variación. Para asegurarnos de esta independecia entre caracteristicas, analizaremos la matriz de correlación. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f7bec265",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "\n",
    "sns.heatmap(df.corr(), annot=True, square=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09ae1f79",
   "metadata": {},
   "source": [
    "En la matriz de correlación podemos ver como hay ciertos elememtos que influyen muy poco con su puntuación. Por ahora las vamos a seguir utilizando pero en el siguiente apartado no las vamos a tener en cuenta."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b43145",
   "metadata": {},
   "source": [
    "Adicionalmente, definiremos unos rangos de calidad de tal manera que :\n",
    "- baja &rarr; \"quality\" < `QUALITY_THRESHOLD` \n",
    "- media &rarr; \"quality\" = `QUALITY_THRESHOLD` \n",
    "- alta &rarr; \"quality\" > `QUALITY_THRESHOLD` \n",
    "\n",
    "Lo tomaremos tal que:\n",
    "- baja &rarr; 1 \n",
    "- media &rarr; 2\n",
    "- alta &rarr; 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "89a13c0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[🐛] [6 5 7 8 4 3 9]\n"
     ]
    }
   ],
   "source": [
    "if ALTERNATIVE_MODE :\n",
    "    debugPrint(\"Alternative mode detected\")\n",
    "    df[\"quality\"] = df[\"quality\"].apply(lambda y: 1 if y<QUALITY_THRESHOLD else y)\n",
    "    df[\"quality\"] = df[\"quality\"].apply(lambda y: 2 if y==QUALITY_THRESHOLD else y)\n",
    "    df[\"quality\"] = df[\"quality\"].apply(lambda y: 3 if y>QUALITY_THRESHOLD else y)\n",
    "\n",
    "debugPrint(df[\"quality\"].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54484e21",
   "metadata": {},
   "source": [
    "Poder juntar las puntuaciones en categorias nos ayudara mucho a la hora de hacer predicciones ya que puntuaciones como el 9 tiene solo 5 ejemplos, hacer predicciones con una muestra tan pequeña resulta ser difícil. Pero si los juntamos con lo 8 y 7 nos da mas data para hacer tests."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ec45bf8",
   "metadata": {},
   "source": [
    "## Escalado de datos\n",
    "Por último, escalamos los datos y separamos las \"features\" y etiquetas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7e0b4750",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_M_scaler = preprocessing.MinMaxScaler()\n",
    "\n",
    "# Separamos las features de las etiquetas \n",
    "y = df[\"quality\"]\n",
    "x = df.drop(\"quality\", axis=1)\n",
    "\n",
    "# Escalamos los datos\n",
    "x = m_M_scaler.fit_transform(x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b9936b3",
   "metadata": {},
   "source": [
    "### Separación del conjunto de entrenamiento, validación y testeo\n",
    "Una vez obtenido nuestro dataset con sus valores procesados, creamos los 2 subconjuntos de datos que nos permitirán entrenar nuestros modelos (entrenamiento y testeo). El subconjutno de validación se creará posteriormente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "000c84a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=TEST_SIZE, stratify=y , random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85a40bcd",
   "metadata": {},
   "source": [
    "# Modelos\n",
    "Una vez preparado nuestros datasets de entrenamiento y testeo, podemos usarlos para nuestros modelos de machine learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8dcf0ee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definimos las etiquetas\n",
    "targetType = [\"bad\", \"average\", \"good\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb921537",
   "metadata": {},
   "source": [
    "## Aplicación"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf84350b",
   "metadata": {},
   "source": [
    "Podemos observar que para la solución cuya salida es una nota tenemos varias `zero_division`. Esto ocurre debido a una desproporción de datos. Esto se podría solucionar si tuviesemos más datos para todas las posibles calidades de nuestro dataset. Para esta solución, en vez de generar datos a partir de los actuales mediante algún tipo de interpolación, trataremos estas `zero_division` como 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42f4d45f",
   "metadata": {},
   "source": [
    "### Regresión logística\n",
    "Debemos aumentar las `max_iter` ya que el número de iteraciónes base (100) no es suficiente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c80f090c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           3       1.00      0.00      0.00        10\n",
      "           4       1.00      0.00      0.00        71\n",
      "           5       0.58      0.61      0.60       706\n",
      "           6       0.53      0.72      0.61       936\n",
      "           7       0.50      0.19      0.28       356\n",
      "           8       1.00      0.00      0.00        64\n",
      "           9       1.00      0.00      0.00         2\n",
      "\n",
      "    accuracy                           0.55      2145\n",
      "   macro avg       0.80      0.22      0.21      2145\n",
      "weighted avg       0.57      0.55      0.51      2145\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clr = LogisticRegression(random_state=0, max_iter=200).fit(x_train, y_train) # Aumentamos las iteraciónes \n",
    "y_hat = clr.predict(x_test)\n",
    "\n",
    "if ALTERNATIVE_MODE:\n",
    "    print(classification_report(y_test, y_hat, target_names=targetType))\n",
    "else: \n",
    "    print(classification_report(y_test, y_hat, zero_division=1)) # Evitamos el error de \"división por zero\" "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ae444af",
   "metadata": {},
   "source": [
    "### Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b2226096",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           3       1.00      0.00      0.00        10\n",
      "           4       1.00      0.00      0.00        71\n",
      "           5       0.49      0.75      0.59       706\n",
      "           6       0.50      0.58      0.54       936\n",
      "           7       1.00      0.00      0.00       356\n",
      "           8       1.00      0.02      0.03        64\n",
      "           9       1.00      0.00      0.00         2\n",
      "\n",
      "    accuracy                           0.50      2145\n",
      "   macro avg       0.86      0.19      0.17      2145\n",
      "weighted avg       0.62      0.50      0.43      2145\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cp = Perceptron(random_state=0).fit(x_train, y_train)\n",
    "y_hat = cp.predict(x_test)\n",
    "\n",
    "if ALTERNATIVE_MODE:\n",
    "    print(classification_report(y_test, y_hat, zero_division=1, target_names=targetType))\n",
    "else: \n",
    "    print(classification_report(y_test, y_hat, zero_division=1)) # Evitamos el error de \"división por zero\" "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f95c8613",
   "metadata": {},
   "source": [
    "### Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "94b5bed3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           3       1.00      0.00      0.00        10\n",
      "           4       1.00      0.00      0.00        71\n",
      "           5       0.49      0.75      0.59       706\n",
      "           6       0.50      0.58      0.54       936\n",
      "           7       1.00      0.00      0.00       356\n",
      "           8       1.00      0.02      0.03        64\n",
      "           9       1.00      0.00      0.00         2\n",
      "\n",
      "    accuracy                           0.50      2145\n",
      "   macro avg       0.86      0.19      0.17      2145\n",
      "weighted avg       0.62      0.50      0.43      2145\n",
      "\n"
     ]
    }
   ],
   "source": [
    "crf = RandomForestClassifier(random_state=0).fit(x_train, y_train)\n",
    "y_hat = cp.predict(x_test)\n",
    "\n",
    "if ALTERNATIVE_MODE:\n",
    "    print(classification_report(y_test, y_hat , zero_division=1, target_names=targetType))\n",
    "else: \n",
    "    print(classification_report(y_test, y_hat, zero_division=1)) # Evitamos el error de \"división por zero\" "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eefe1801",
   "metadata": {},
   "source": [
    "## Analisis de los resultados"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99c6f188",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "250e1e28",
   "metadata": {},
   "source": [
    "En los tres modelos podemos ver resultados similares cuando utilizamos la classificación por numeros.\n",
    "\n",
    " En regressión logística vemos que solo predice puntuaciones de 5, 6 y 7. Esto es debido a que son las muestras mas grandes por tanto son las puntuaciones con los que hemos entrenado mas, en cambio el total de muestras de las otras es mas pequeño que las muestras de la puntuación 7 esto hace que se realice poco entrenamiento con esos valores. Por culpa de esto en la regression lineal, no se classifica ningún valor en 3, 4, 8 y 9. El hecho de que no se predice ningun valor para estas puntaciones nos va a resultar en una abundancia de falsos positivos, que lo podemos ver en nuestra precision baja en general (Aparte de los de 3, 4, 8 y 9 que es 1 porque se hace una división de 0/0). Pero en cambio no esta tan baja el recall al menos para las puntuaciones de 6 y 5. Con estas dos eran los que tenian la mayor cantidad de datos con diferencia nos han dado pocos falsos negativos resultando en un recall alto. Para las puntuaciones de 3, 4, 7, 8 y 9 podemos ver un recal muy baja debido a que son de datasets pequeños y no se esta prediciendo ningun valor paras ellos.\n",
    "\n",
    " Para el perceptron y el random forest los resultados son identicos solo predice valores para las puntuaciones de 5 y 6 (Los dos sets mas grandes). Ocurre lo mismo que en la regresion lineal hay valores que no los percibe esos son por lo mismo que hemos dicho antes, son de las muestras mas pequeñas, por eso no predice nada para esas puntuaciones. Los que si que predice 5 y 6 si que predice pero tiene una precision muy baja por lo dicho antes, se le estan asignandod muchos falsos positivos. Lo interesante es ver que los recals si que han cambiado respecto a la regression lineal ahora el 5 ha augmentado en su recal y el 6 ha disminuido. Eso sera porque se le esta dando menos falsos negativos para 5 y esta dando mas para 6.\n",
    "\n",
    "Cuando realizamos la classificacion por average, bad y good vemos que los 3 modelos cambian:\n",
    "\n",
    " Para la regression logística podemos ver que los resultados siguen sin ser muy precisos pero esta vez se predicen de todas las clases pero seguimos con una baja precisiion el que lo tiene mas alto es la clase de baja y solo tiene un 64% los otros dos estan alrededor del 50%. El recall tiene el mismo problema es relativamente baja sobretodo en el caso de los buenos que es de un 30%. Pero aun asi es mejor que cuando hacemos la regresion logística con los numeros como clases. Ya que agrupando las clases ha resultado en muestras para cada uno de las classes mas grandes.\n",
    "\n",
    " Otra vez por perceptron y random forest volvemos a ver que tienen los mismos resultados pero esta vez ha pasado una cosa diferente al respecto de separar las clases por numeros, no predice ningún valor para la clase de average. Esta vez no podemos decir que es porque la muestra de la clase es demasiada pequeña, de hecho es la más grande. Esta vez no esta poniendo nada en la categoria de average porque las variables de average los esta poniendo en las otras dos. Esto podria ser resultado variables que esta teniendo en cuenta a la hora de predecir que no deberia porque influyen demasiado poco."
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  }
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