Tarea1_Alberto_y_Yu_Hung.ipynb

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    "# Aprendizaje automático\n",
    "# Tarea 1\n",
    "\n",
    "Prof. Felipe Meza Obando,\n",
    "Instituto Tecnológico de Costa Rica, \n",
    "\n",
    "Medio de entrega: Link de GitHub\n",
    "\n",
    "Entregables: Un archivo jupyter ( .IPYNB ). \n",
    "\n",
    "Modo de trabajo - Parejas:\n",
    "\n",
    "Estudiantes:\n",
    "\n",
    "Yu Hung Yeh Chai\n",
    "\n",
    "José Alberto Raygada Agüero\n",
    "\n",
    "---\n",
    "\n",
    "Objetivo\n",
    "El objetivo de esta Tarea 1 consiste en normalizar y estandarizar el set\n",
    "de datos de daily-min-temperatures.csv de forma manual y automática a partir de scikit-learn\n",
    "o la librería que considere apropiada.\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23107090",
   "metadata": {},
   "source": [
    "### Primero cargamos las librerías y funciones propias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c1799847",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install torch\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import sklearn.preprocessing\n",
    "import torch as torch\n",
    "\n",
    "\n",
    "#Funciones\n",
    "def bold(t):\n",
    "    \"\"\"\n",
    "    Funcion para formatear en negrita el texto.\n",
    "    t = cualquier dato\n",
    "    \"\"\"\n",
    "    return \"\\033[1m\" + str(t) + \"\\033[0m\"\n",
    "\n",
    "def newline():\n",
    "    \"\"\"\n",
    "    Funcion para imprimir cambio de linea\n",
    "    \"\"\"\n",
    "    print(\"\\r\\n\")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7654529d",
   "metadata": {},
   "source": [
    "### Luego cargamos el archivo como un dataframe de csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e066b9b3",
   "metadata": {
    "scrolled": true
   },
   "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>Date</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1981-01-01</td>\n",
       "      <td>20.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981-01-02</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1981-01-03</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1981-01-04</td>\n",
       "      <td>14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1981-01-05</td>\n",
       "      <td>15.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Temp\n",
       "0  1981-01-01  20.7\n",
       "1  1981-01-02  17.9\n",
       "2  1981-01-03  18.8\n",
       "3  1981-01-04  14.6\n",
       "4  1981-01-05  15.8"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"daily-min-temperatures.txt\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd418edd",
   "metadata": {},
   "source": [
    "### Se selecciona únicamente la columna de temperatura que es el dato que podemos estandarizar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "11b555e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mFeatures\u001b[0m\n",
      "----------------------------------------------\n",
      "\r\n",
      "\n",
      "      Temp\n",
      "0     20.7\n",
      "1     17.9\n",
      "2     18.8\n",
      "3     14.6\n",
      "4     15.8\n",
      "...    ...\n",
      "3645  14.0\n",
      "3646  13.6\n",
      "3647  13.5\n",
      "3648  15.7\n",
      "3649  13.0\n",
      "\n",
      "[3650 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(bold(\"Features\"))\n",
    "print(\"----------------------------------------------\")\n",
    "newline()    \n",
    "\n",
    "feature = df[[\"Temp\"]]\n",
    "\n",
    "print(feature)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3facdaea",
   "metadata": {},
   "source": [
    "### Normalización y estandarización (Feature Scaling)\n",
    "\n",
    "Una de las transformaciones más importantes que deben ser aplicadas a los datos es el escalamiento de características (feature scaling). En la mayoría de los casos, los algorítmos de aprendizaje automático no se desempeñan bien cuando la entrada de atributos numéricos presentan diferentes escalas. Existen dos formas comunes para conseguir que todos los datos de los atributos tengan la misma escala:\n",
    "\n",
    "1. **Normalización (min-max scaling)**: es la más simple; los valores son cambiados y re-escalados de tal forma que se presenten en un rango de 0 a 1. Lo anterior se logra restando el valor mínimo y dividiendo por el máximo menos el mínimo. \n",
    "\n",
    "$$Scaler(x_i)=\\frac{x_i-min(x)}{max(x) - min(x)}$$\n",
    "\n",
    "      \n",
    "2. **Estandarización:** La estandarización requiere primero restar el valor medio (mean value ${\\mu}$), por lo tanto los valores estandarizados siempre presentan una media igual a cero, y después dividir por la desviación estandar (standard deviation ${\\sigma}$), de tal forma que la distribución resultante presentará varianza igual a 1.\n",
    "\n",
    "\n",
    "$$Scaler(x_i)=\\frac{x_i-\\mu}{\\sigma}$$\n",
    "\n",
    "\n",
    "\n",
    "_____\n",
    "**Referencias:**\n",
    "\n",
    "***Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron.*** 2019, second edition. Prepare the Data for Machine Learning Algorithms, Feature Scaling, pág. 69-70."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5addf1a0",
   "metadata": {},
   "source": [
    "### Normalización y estandarización automática"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b78e975f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mResultado automático\u001b[0m\n",
      "----------------------------------------------\n",
      "\r\n",
      "\n",
      "          Temp\n",
      "0     0.787072\n",
      "1     0.680608\n",
      "2     0.714829\n",
      "3     0.555133\n",
      "4     0.600760\n",
      "...        ...\n",
      "3645  0.532319\n",
      "3646  0.517110\n",
      "3647  0.513308\n",
      "3648  0.596958\n",
      "3649  0.494297\n",
      "\n",
      "[3650 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(bold(\"Resultado automático\"))\n",
    "print(\"----------------------------------------------\")\n",
    "newline()\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "nombreColumnas = feature.columns.to_numpy()\n",
    "datosEscalados = pd.DataFrame(scaler.fit_transform(feature), columns = nombreColumnas)\n",
    "\n",
    "print(datosEscalados)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f04e7b9",
   "metadata": {},
   "source": [
    "### Normalización y estandarización manual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "739e4151",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Funciones manuales de normalizar y estandarizar\n",
    "def normalizar(datos):\n",
    "    \"\"\"\n",
    "    Funcion para normalizar set de datos\n",
    "    datos = set de datos\n",
    "    \"\"\"\n",
    "    minimo = datos.min()\n",
    "    maximo = datos.max()\n",
    "\n",
    "    normalizado = (datos - minimo) / (maximo - minimo)\n",
    "    return normalizado\n",
    "\n",
    "def estandarizar(datos):\n",
    "    \"\"\"\n",
    "    Funcion para estandarizar set de datos\n",
    "    datos = set de datos\n",
    "    \"\"\"\n",
    "    promedio = datos.mean()\n",
    "    desv_std = datos.std()\n",
    "    estandarizado = (datos - promedio) / desv_std\n",
    "    return estandarizado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "26a389f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mResultado manual\u001b[0m\n",
      "----------------------------------------------\n",
      "\r\n",
      "\n",
      "          Temp\n",
      "0     0.787072\n",
      "1     0.680608\n",
      "2     0.714829\n",
      "3     0.555133\n",
      "4     0.600760\n",
      "...        ...\n",
      "3645  0.532319\n",
      "3646  0.517110\n",
      "3647  0.513308\n",
      "3648  0.596958\n",
      "3649  0.494297\n",
      "\n",
      "[3650 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(bold(\"Resultado manual\"))\n",
    "print(\"----------------------------------------------\")\n",
    "newline()  \n",
    "\n",
    "print(normalizar(estandarizar(feature)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2f6d7ed",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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