hackaton.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background-color: #EDF7FF; border-color: #7C9DBF; border-left: 5px solid #7C9DBF; padding: 0.5em;\">\n",
    "<strong>Nombre y apellidos:</strong>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Daniel Bagan Martínez"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, f1_score, make_scorer\n",
    "from sklearn.model_selection import cross_val_score, train_test_split, validation_curve, GridSearchCV\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Carga del conjunto de datos\n",
    "\n",
    "Features: El dataset contiene 8 features en 8 columnas que son los parámetros medidos por los diferentes sensores. Estos corresponden a las diferentes interacciones que han tenido los haces de los láseres al travesar las partículas del aire.\n",
    "\n",
    "Target: El target corresponde al 'label' que clasifica la calidad del aire.\n",
    "- Target 0 corresponde a una calidad del aire Buena\n",
    "- Target 1 corresponde a una calidad del aire Moderada\n",
    "- Target 2 corresponde a una calidad del aire Peligrosa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filas train: 2100 Cols train: 9\n",
      "Filas test: 900 Cols test: 8\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature1</th>\n",
       "      <th>feature2</th>\n",
       "      <th>feature3</th>\n",
       "      <th>feature4</th>\n",
       "      <th>feature5</th>\n",
       "      <th>feature6</th>\n",
       "      <th>feature7</th>\n",
       "      <th>feature8</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.458258</td>\n",
       "      <td>-0.579012</td>\n",
       "      <td>2.886009</td>\n",
       "      <td>-0.075516</td>\n",
       "      <td>1.674060</td>\n",
       "      <td>-2.431912</td>\n",
       "      <td>0.534850</td>\n",
       "      <td>-0.846473</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.626615</td>\n",
       "      <td>-0.028332</td>\n",
       "      <td>-1.443184</td>\n",
       "      <td>-1.007447</td>\n",
       "      <td>-0.989093</td>\n",
       "      <td>1.202627</td>\n",
       "      <td>-0.038211</td>\n",
       "      <td>-1.298616</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.721356</td>\n",
       "      <td>-0.137676</td>\n",
       "      <td>-1.322593</td>\n",
       "      <td>-1.333187</td>\n",
       "      <td>-0.723713</td>\n",
       "      <td>0.843285</td>\n",
       "      <td>-0.588495</td>\n",
       "      <td>0.066682</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.715103</td>\n",
       "      <td>3.265915</td>\n",
       "      <td>-0.791030</td>\n",
       "      <td>-2.967881</td>\n",
       "      <td>2.497628</td>\n",
       "      <td>-0.277387</td>\n",
       "      <td>-0.551611</td>\n",
       "      <td>0.668354</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.944098</td>\n",
       "      <td>0.197871</td>\n",
       "      <td>-2.257025</td>\n",
       "      <td>1.032789</td>\n",
       "      <td>-4.104693</td>\n",
       "      <td>4.716115</td>\n",
       "      <td>-0.380813</td>\n",
       "      <td>-1.393825</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feature1  feature2  feature3  feature4  feature5  feature6  feature7  \\\n",
       "0 -0.458258 -0.579012  2.886009 -0.075516  1.674060 -2.431912  0.534850   \n",
       "1  1.626615 -0.028332 -1.443184 -1.007447 -0.989093  1.202627 -0.038211   \n",
       "2  1.721356 -0.137676 -1.322593 -1.333187 -0.723713  0.843285 -0.588495   \n",
       "3 -0.715103  3.265915 -0.791030 -2.967881  2.497628 -0.277387 -0.551611   \n",
       "4  2.944098  0.197871 -2.257025  1.032789 -4.104693  4.716115 -0.380813   \n",
       "\n",
       "   feature8  target  \n",
       "0 -0.846473       2  \n",
       "1 -1.298616       0  \n",
       "2  0.066682       0  \n",
       "3  0.668354       2  \n",
       "4 -1.393825       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creamos el dataframe a partir de .csv\n",
    "df = pd.read_csv('uoc_train.csv')\n",
    "df_test = pd.read_csv('uoc_X_test.csv')\n",
    "\n",
    "# Mostramos número de filas y columnas\n",
    "print(\"Filas train: \" + str(df.shape[0]) + \" Cols train: \" + str(df.shape[1]))\n",
    "print(\"Filas test: \" + str(df_test.shape[0]) + \" Cols test: \" + str(df_test.shape[1]))\n",
    "\n",
    "# Mostramos las primeras filas\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature1</th>\n",
       "      <th>feature2</th>\n",
       "      <th>feature3</th>\n",
       "      <th>feature4</th>\n",
       "      <th>feature5</th>\n",
       "      <th>feature6</th>\n",
       "      <th>feature7</th>\n",
       "      <th>feature8</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.318846</td>\n",
       "      <td>0.005348</td>\n",
       "      <td>-0.327567</td>\n",
       "      <td>0.024042</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>-0.065000</td>\n",
       "      <td>-0.025603</td>\n",
       "      <td>-0.004437</td>\n",
       "      <td>0.985238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.575061</td>\n",
       "      <td>1.605428</td>\n",
       "      <td>1.479745</td>\n",
       "      <td>1.548154</td>\n",
       "      <td>1.788543</td>\n",
       "      <td>2.124337</td>\n",
       "      <td>1.015619</td>\n",
       "      <td>1.003342</td>\n",
       "      <td>0.815098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-5.031501</td>\n",
       "      <td>-6.004578</td>\n",
       "      <td>-5.470794</td>\n",
       "      <td>-5.296172</td>\n",
       "      <td>-5.719499</td>\n",
       "      <td>-4.910575</td>\n",
       "      <td>-3.850552</td>\n",
       "      <td>-3.541608</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.406249</td>\n",
       "      <td>-1.119150</td>\n",
       "      <td>-1.368208</td>\n",
       "      <td>-1.083716</td>\n",
       "      <td>-1.136441</td>\n",
       "      <td>-1.613267</td>\n",
       "      <td>-0.701442</td>\n",
       "      <td>-0.659096</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.431219</td>\n",
       "      <td>-0.162577</td>\n",
       "      <td>-0.532158</td>\n",
       "      <td>0.152545</td>\n",
       "      <td>0.102788</td>\n",
       "      <td>-0.431328</td>\n",
       "      <td>-0.007598</td>\n",
       "      <td>-0.019797</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.773896</td>\n",
       "      <td>1.127450</td>\n",
       "      <td>0.573801</td>\n",
       "      <td>1.173172</td>\n",
       "      <td>1.507492</td>\n",
       "      <td>1.162813</td>\n",
       "      <td>0.665932</td>\n",
       "      <td>0.667530</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.388387</td>\n",
       "      <td>7.663112</td>\n",
       "      <td>4.869581</td>\n",
       "      <td>4.318758</td>\n",
       "      <td>6.033273</td>\n",
       "      <td>7.053420</td>\n",
       "      <td>4.400697</td>\n",
       "      <td>3.579874</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          feature1     feature2     feature3     feature4     feature5  \\\n",
       "count  2100.000000  2100.000000  2100.000000  2100.000000  2100.000000   \n",
       "mean     -0.318846     0.005348    -0.327567     0.024042     0.085851   \n",
       "std       1.575061     1.605428     1.479745     1.548154     1.788543   \n",
       "min      -5.031501    -6.004578    -5.470794    -5.296172    -5.719499   \n",
       "25%      -1.406249    -1.119150    -1.368208    -1.083716    -1.136441   \n",
       "50%      -0.431219    -0.162577    -0.532158     0.152545     0.102788   \n",
       "75%       0.773896     1.127450     0.573801     1.173172     1.507492   \n",
       "max       5.388387     7.663112     4.869581     4.318758     6.033273   \n",
       "\n",
       "          feature6     feature7     feature8       target  \n",
       "count  2100.000000  2100.000000  2100.000000  2100.000000  \n",
       "mean     -0.065000    -0.025603    -0.004437     0.985238  \n",
       "std       2.124337     1.015619     1.003342     0.815098  \n",
       "min      -4.910575    -3.850552    -3.541608     0.000000  \n",
       "25%      -1.613267    -0.701442    -0.659096     0.000000  \n",
       "50%      -0.431328    -0.007598    -0.019797     1.000000  \n",
       "75%       1.162813     0.665932     0.667530     2.000000  \n",
       "max       7.053420     4.400697     3.579874     2.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descripción analítica básica del dataframe\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- ## Representación de variables y detección de outliers -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualización y detección de outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El siguiente histograma muestra que cada una de las tres posibles etiquetas contiene aproximadamente 1/3 del total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oaUk+CBwHPgB8KUnvV578ztp0tXZ8WmbAkvw7sLOq/ndB/S7gdFWNrk1nWq0k56vq/rXuQ4tL8kXgLVX1YpJtwOPAn1XVR5J8vqoeXNsOby+nZQbvO8APA19dUN/U7dPLWJIvLLUL2Hg7e9EtW/fSVExVPZ/kbcDjSV7H4l+J0jTDffA+DEwleZbvfnna/cCPAO9fq6bUt43AQ8A3FtQD/PPtb0e34GtJHqiqZwC6O/h3AR8HfmJNO1sDhvuAVdUTSX6U+a893sx8KMwCn62q62vanPrxaeA1LwVEryRP3vZudCveC1zrLVTVNeC9Sf5obVpaO865S1KDfFpGkhpkuEtSgwx3SWqQ4S5JDTLcJalB/we0UuB0nJgwnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograma con la etiqueta \"target\"\n",
    "df['target'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target 0: 33.95%\n",
      "Target 1: 33.57%\n",
      "Target 2: 32.48%\n"
     ]
    }
   ],
   "source": [
    "print(\"Target 0: {0}%\".format(round(len(df[df['target']==0])/len(df)*100,2)))\n",
    "print(\"Target 1: {0}%\".format(round(len(df[df['target']==1])/len(df)*100,2)))\n",
    "print(\"Target 2: {0}%\".format(round(len(df[df['target']==2])/len(df)*100,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los siguientes diagramas de cajas muestran la distribución de los valores de cada atributo. Aparentemente, no existen outliers en ninguno de los 8 atributos de entrada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "keys = df_test.keys()\n",
    "f, ax = plt.subplots(2, 4,figsize=(16, 10))\n",
    "for f in range(len(keys)):\n",
    "    j=f%4\n",
    "    i=f//4\n",
    "    ax[i][j].boxplot(df[keys[f]])\n",
    "    ax[i][j].set_title(keys[f])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los siguientes histogramas muestran la distribución de la frecuencia valores de cada atributo. Las 8 atributos contienen muestras cercanas a una distribución normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(2, 4,figsize=(16, 10))\n",
    "for f in range(len(keys)):\n",
    "    j=f%4\n",
    "    i=f//4\n",
    "    ax[i][j].hist(df[keys[f]])\n",
    "    ax[i][j].set_title(keys[f])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De la siguiente matriz de correlaciones podemos extraer las siguientes conclusiones:\n",
    "- Las correlación de las **features 4, 7 y 8** con la etiqueta es muy débil, casi inexistente, por lo que **se puede valorar eliminar estas columnas**.\n",
    "- La **feature 6** tiene casi un **-0.7** de correlación con la etiqueta, mientras la de las **features 3 y 5** es cercana a **0.6** y la **feature 5** es de aproximadamente **-0.5**, por tanto se tratan de **columnas con una correlación fuerte con la etiqueta**.\n",
    "- Otras correlaciones que llaman la atención son el **-0.85** entre las **features 5 y 6**, o otras correlaciones fuertes como las existentes entre la **feature 1 con la 5 y la 6**. \n",
    "- En general podemos encontrar **correlaciones bastante fuertes** entre los atributos, por lo que quizás un **PCA** pueda reducir el número de variables sin perder varianza.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr().round(2),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación, representaremos algunos gráficos en función de la etiqueta, para buscar posibles correlaciones menos evidentes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los siguientes diagramas de cajas añaden la diferenciación por clases, y cada caja representa al conjunto de valores con una etiqueta.\n",
    "- Al igual que en la matriz de correlaciones, las features **4, 7 y 8** parecen ser las **menos útiles** a la hora de distinguir entre las etiquetas\n",
    "- En las **features 1 y 6** se observa que el **aire con buena calidad** contiene, en general, **valores mayores**. \n",
    "- Para la **feature 3**, el aire de mala calidad tiene, en general, **valores mayores**.\n",
    "- Los valores de la **feature 5** tienden a crecer conforme mejora la calidad del aire, al contrario que los de la **feature 6**, que tienden a decrecer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(2, 4,figsize=(16, 10))\n",
    "for f in range(len(keys)):\n",
    "    j=f%4\n",
    "    i=f//4\n",
    "    data = [df[df['target']==0][keys[f]],df[df['target']==1][keys[f]],df[df['target']==2][keys[f]]]\n",
    "    ax[i][j].boxplot(data)\n",
    "    ax[i][j].set_title(keys[f])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los siguientes histogramas y muestran la distribución de frecuencias de cada feature según la clase a la que pertenecen. Estos histogramas no permiten sacar ninguna conclusión adicional a las ya extraídas, más allá de que los registros pertenecientes a cada clase de cada feature también parecen seguir una distribución normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(2, 4,figsize=(16, 10))\n",
    "for f in range(len(keys)):\n",
    "    j=f%4\n",
    "    i=f//4\n",
    "    for t in range(3):\n",
    "        df_class = df[df[\"target\"]==t]\n",
    "        ax[i][j].hist(df_class[keys[f]], alpha=0.6, label=\"class \" + str(t))\n",
    "    ax[i][j].set_title(keys[f]+\" by class\")\n",
    "    ax[i][j].legend(loc='upper right')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creación del modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación, procedemos a crear el modelo *Random Forest*. No se aplicará normalización, ya que no procede para la creación de los árboles de decisión.\n",
    "\n",
    "En primer lugar se creará un modelo con todas las variables, en segundo lugar se creará eliminando aquellas que no parecen tener efecto alguna sobre la etiqueta a predecir, por su baja correlación, y finalmente se utilizará PCA para reducir el conjunto de datos, debido a las altas correlaciones entre sus variables.\n",
    "\n",
    "Para cada modelo, se aplicará la función *GridSearch* para encontrar los mejores hiperparámetros. Finalmente, se seleccionará la opción que mejor *F1 score* ha obtenido.\n",
    "\n",
    "Para estas pruebas, no se utilizará un conjunto de prueba y entrenamiento, si no que se utilizará *Cross Validation*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creamos el conjunto de entrenamiento y de prueba, aunque no será necesario utilizarlo por el momento.\n",
    "X = df.drop(\"target\",axis=1)\n",
    "y = df[\"target\"]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelo con todas las variables\n",
    "En primer lugar, probaremos el modelo sin eliminar columnas ni modificar el conjunto de datos. \n",
    "\n",
    "Se ha obtenido un **F1 score de 0.917**, con los **hiperparámetros** *max_depth=11* y *n_estimators=200*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creamos nuestro propio f1 score para poder utilizarlo junto con GridSearchCV\n",
    "f1 = make_scorer(f1_score , average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Usamos GridSearch para encontrar la mejor combinación de parámetros, indicamos f1 como métrica\n",
    "grid_params = {'n_estimators': [20,30,50,100,200],\n",
    "               'max_depth': [3,4,5,6,7,8,9,10,11,12]}\n",
    "\n",
    "gs = GridSearchCV(\n",
    "    RandomForestClassifier(),\n",
    "    grid_params,\n",
    "    cv=4,\n",
    "    n_jobs= -1,\n",
    "    scoring=f1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs_results = gs.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score: 0.9169852008776329\n",
      "RandomForestClassifier(max_depth=11, n_estimators=200)\n",
      "{'max_depth': 11, 'n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 score: \" + str(gs_results.best_score_))\n",
    "print(gs_results.best_estimator_)\n",
    "print(gs_results.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='param_n_estimators', ylabel='param_max_depth'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat = pd.pivot_table(pd.DataFrame(gs_results.cv_results_),\n",
    "                    values='mean_test_score', index='param_max_depth', columns='param_n_estimators')\n",
    "sns.heatmap(mat, cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelo sin las features 4, 7 y 8\n",
    "A continuación, pasamos a eliminar las features que cuya correlación con el target es casi inexistente.\n",
    "\n",
    "El F1 score del modelo no ha mejorado, de hecho, ha empeorado ligeramente a 0.908, con los mismo hiperparámetros que el modelo anterior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creamos el conjunto de entrenamiento y de prueba\n",
    "X2 = df.drop([\"target\",\"feature4\",\"feature7\",\"feature8\"],axis=1)\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs_results = gs.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score: 0.908263415502417\n",
      "RandomForestClassifier(max_depth=11, n_estimators=200)\n",
      "{'max_depth': 11, 'n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 score: \" + str(gs_results.best_score_))\n",
    "print(gs_results.best_estimator_)\n",
    "print(gs_results.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='param_n_estimators', ylabel='param_max_depth'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat = pd.pivot_table(pd.DataFrame(gs_results.cv_results_),\n",
    "                    values='mean_test_score', index='param_max_depth', columns='param_n_estimators')\n",
    "sns.heatmap(mat, cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelo con todas las features y PCA\n",
    "Finalmente, procedemos a aplicar PCA para reducir la dimensionalidad del conjunto.\n",
    "\n",
    "El modelo tampoco ha mejorado respecto al primero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Antes de aplicar PCA, se escalan todos los atributos usando StandardScaler:\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X)\n",
    "X_scaled = scaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original shape: (2100, 8)\n",
      "Reduced shape: (2100, 6)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Cogeremos los 6 primeros componentes principales\n",
    "pca = PCA(n_components=6)\n",
    "\n",
    "pca.fit(X_scaled)\n",
    "\n",
    "# se tranforman los datos en los dos primeros componentes principales\n",
    "X_pca = pca.transform(X_scaled)\n",
    "\n",
    "print(\"Original shape: {}\".format(str(X_scaled.shape)))\n",
    "print(\"Reduced shape: {}\".format(str(X_pca.shape)))\n",
    "\n",
    "#X_train_pca, X_test_pca, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=24)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs_results = gs.fit(X_pca,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score: 0.9032153684923663\n",
      "RandomForestClassifier(max_depth=11, n_estimators=20)\n",
      "{'max_depth': 11, 'n_estimators': 20}\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 score: \" + str(gs_results.best_score_))\n",
    "print(gs_results.best_estimator_)\n",
    "print(gs_results.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='param_n_estimators', ylabel='param_max_depth'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat = pd.pivot_table(pd.DataFrame(gs_results.cv_results_),\n",
    "                    values='mean_test_score', index='param_max_depth', columns='param_n_estimators')\n",
    "sns.heatmap(mat, cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelo final\n",
    "\n",
    "Debido a que los mejores resultado que hemos obtenido los hemos conseguido con todos los atributos y unos hiperparámetros de max_depth=11, y n_estimators=200, procedemos a crear el modelo final."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"target\",axis=1)\n",
    "y = df[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelo = RandomForestClassifier(max_depth=11, n_estimators=200)\n",
    "modelo.fit(X, y)\n",
    "y_pred = modelo.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       1, 0, 0, 1, 1, 1, 1, 2, 0, 1, 2, 1, 2, 0, 0, 2, 1, 0, 1, 1, 2, 1,\n",
       "       2, 0, 0, 0, 1, 2, 2, 0, 1, 0, 0, 2, 0, 2, 2, 0, 1, 0, 2, 0, 1, 0,\n",
       "       1, 2, 1, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 1, 2, 2, 2, 1, 2, 0, 0, 2,\n",
       "       0, 1, 0, 0, 0, 0, 0, 2, 2, 1, 1, 2, 0, 0, 0, 2, 0, 2, 2, 1, 0, 1,\n",
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       "       2, 0, 0, 1, 2, 0, 0, 2, 0, 2, 2, 1, 0, 2, 1, 2, 1, 2, 1, 1, 1, 2,\n",
       "       0, 2, 1, 1, 2, 1, 0, 1, 2, 1, 0, 2, 0, 2, 1, 0, 1, 1, 0, 2, 0, 2,\n",
       "       2, 0, 1, 2, 1, 1, 0, 1, 0, 1, 0, 2, 1, 1, 0, 0, 1, 2, 2, 0, 2, 2,\n",
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       "       1, 2, 0, 0, 2, 1, 0, 0, 0, 0, 1, 0, 2, 1, 0, 2, 1, 0, 0, 2, 2, 0,\n",
       "       0, 1, 2, 2, 2, 2, 2, 1, 0, 2, 1, 1, 1, 2, 2, 2, 2, 2, 0, 0, 1, 1,\n",
       "       0, 0, 2, 0, 1, 2, 2, 0, 0, 2, 1, 1, 0, 2, 2, 2, 0, 2, 0, 1, 1, 2,\n",
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       "       1, 2, 0, 2, 1, 2, 0, 2, 1, 0, 0, 0, 2, 0, 0, 1, 2, 2, 2, 2, 2, 0,\n",
       "       2, 2, 0, 2, 0, 2, 1, 0, 1, 0, 2, 1, 2, 1, 0, 2, 0, 1, 0, 0, 1, 2,\n",
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       "       2, 1, 1, 0, 2, 0, 2, 2, 0, 0, 1, 2, 2, 0, 2, 2, 1, 1, 1, 1, 1, 2,\n",
       "       2, 1, 0, 2, 0, 2, 2, 1, 1, 1, 0, 2, 2, 1, 0, 1, 0, 0, 1, 1, 0, 2,\n",
       "       1, 0, 1, 2, 0, 0, 1, 2, 0, 0, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 2, 0,\n",
       "       2, 2, 0, 0, 1, 1, 2, 1, 0, 2, 0, 2, 0, 1, 2, 2, 1, 2, 2, 2, 2, 0,\n",
       "       2, 1, 1, 1, 1, 1, 1, 2, 0, 2, 1, 0, 2, 0, 2, 1, 2, 1, 0, 2, 0, 1,\n",
       "       2, 2, 2, 1, 2, 0, 2, 2, 2, 1, 2, 0, 1, 2, 0, 1, 1, 0, 2, 0, 2, 2,\n",
       "       2, 2, 0, 1, 2, 0, 0, 2, 2, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 2, 0,\n",
       "       2, 0, 2, 0, 2, 2, 1, 0, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 1, 2, 0, 1,\n",
       "       1, 1, 2, 2, 1, 1, 2, 1, 0, 1, 1, 1, 2, 0, 0, 0, 2, 2, 2, 1, 2, 0,\n",
       "       2, 2, 0, 2, 0, 1, 2, 0, 1, 2, 1, 0, 1, 0, 1, 1, 2, 2, 2, 2],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(y_pred).to_csv(\"predicciones.csv\",index=False, header=['final_status'])"
   ]
  }
 ],
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