student-performance/performance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, explained_variance_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Genera dos DataFrames para entrenar y testear\n",
    "# El primero tiene todas las columnas de características\n",
    "# El segundo solo tiene la columna de la variable objetivo\n",
    "def split_objective(input_df, dims):\n",
    "    features = input_df.loc[:, input_df.columns != list(dims.keys())[-1]]\n",
    "    objective = input_df[input_df.columns[-1:]]\n",
    "    return (features, objective)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# campos a utilizar\n",
    "# Extrae los datos del CSV\n",
    "# el DataFrame generado convierte los datos categóricos en valores\n",
    "# numéricos que pueden ser operados por cualquier algoritmo\n",
    "def preparar_datos(dimensiones, archivo='6to.csv'):\n",
    "    df = pd.read_csv(\n",
    "        archivo,\n",
    "        encoding = \"ISO-8859-1\",\n",
    "        usecols=list(dimensiones.keys()),\n",
    "        dtype=dimensiones)\n",
    "    # eliminar valores nulos\n",
    "    df.replace('', 0, regex=True)\n",
    "    \n",
    "    factores = [x for x, y in df.dtypes.items() if str(y) == 'category']\n",
    "    for factor in factores:\n",
    "        le = preprocessing.LabelEncoder()\n",
    "        le.fit(df[factor])\n",
    "        df[factor] = le.transform(df[factor])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF: 0.5781829140915602 (0.01866626582487565)\n",
      "SVM: 0.6084786982007726 (0.034273654502426615)\n",
      "LR: 0.6046237793862916 (0.03279576642250505)\n",
      "LDA: 0.6047323275305342 (0.03225641939240576)\n",
      "CART: 0.5488108360595696 (0.022054262221462055)\n",
      "KNN: 0.5614055171120356 (0.023765897728856063)\n"
     ]
    }
   ],
   "source": [
    "def comparar_modelos():\n",
    "    dimensiones = {\n",
    "        'Edad': 'int',\n",
    "        'Etnia_Recode': 'category',\n",
    "        'P61_Te_Han_Esenado_Utilizar_Calculadora': 'category',\n",
    "        'P63_Ensayo_Y_Error': 'category',\n",
    "        'P63_Buscar_Un_Patron': 'category',\n",
    "        'P59_Gusta_Resolver_Ejercicios_Matematicas': 'category',\n",
    "        'P63_Ensayo_Y_Error': 'category',\n",
    "        'P63_Buscar_Un_Patron': 'category',\n",
    "        'P63_Rosolver_Un_Problema_Similar_Pero_Mas_Simple': 'category',\n",
    "        'P63_Hacer_Un_Diagrama': 'category',\n",
    "        'P63_Usar_Razonamiento_Indirecto': 'category',\n",
    "        'P63_Usar_Casos': 'category',\n",
    "        'P63_Rosolver_Un_Problema_Equivalente': 'category',\n",
    "        'P63_Buscar_Una_Formula': 'category',\n",
    "        'P63_Usar_Analisis_Dimensionales': 'category',\n",
    "        'P63_Usar_Coordenadas': 'category',\n",
    "        'P63_Usar_Una_Variable': 'category',\n",
    "        'P63_Usar_Una_Lista': 'category',\n",
    "        'P63_Hacer_Una_Figura': 'category',\n",
    "        'P63_Usar_Razonamiento_Directo': 'category',\n",
    "        'P63_Usar_Las_Propiedades_De_Los_Numeros': 'category',\n",
    "        'P63_Trabajar_Hacia_Atras': 'category',\n",
    "        'P63_Resolver_Una_Educacion': 'category',\n",
    "        'P63_Usar_Un_Modelo': 'category',\n",
    "        'P63_Identificar_Submeta': 'category',\n",
    "        'P63_Usar_Simetria': 'category',\n",
    "        'LOGRO_MATE': 'category'\n",
    "    }\n",
    "    # Leer del CSV\n",
    "    df = pd.read_csv(\n",
    "        '6to.csv',\n",
    "        encoding = \"ISO-8859-1\",\n",
    "        usecols=list(dimensiones.keys()),\n",
    "        dtype=dimensiones)\n",
    "    # eliminar valores nulos\n",
    "    df.replace('', 0, regex=True)\n",
    "\n",
    "    # Convertir factores (como strings) en numeros\n",
    "    factores = [x for x, y in df.dtypes.items() if str(y) == 'category']\n",
    "    for factor in factores:\n",
    "        le = preprocessing.LabelEncoder()\n",
    "        le.fit(df[factor])\n",
    "        df[factor] = le.transform(df[factor])\n",
    "\n",
    "    df_train, df_test = train_test_split(df, test_size=0.2)\n",
    "    seed = 7\n",
    "    \n",
    "    models = [\n",
    "        ('RF', RandomForestClassifier(n_estimators=1000)),\n",
    "        ('SVM', SVC()),\n",
    "        ('LR', LogisticRegression()),\n",
    "        ('LDA', LinearDiscriminantAnalysis()),\n",
    "        ('CART', DecisionTreeClassifier()),\n",
    "        ('KNN', KNeighborsClassifier())\n",
    "    ]\n",
    "    resultado = []\n",
    "    for name, modelo in models:\n",
    "        kfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
    "        try:\n",
    "\n",
    "            cv_results = model_selection.cross_val_score(\n",
    "                modelo,\n",
    "                df.loc[:, df.columns != list(dimensiones.keys())[-1]].values,\n",
    "                df[df.columns[-1:]].values.ravel(),\n",
    "                cv=kfold,\n",
    "                scoring='accuracy')\n",
    "            resultado.append(cv_results)\n",
    "            mensaje = '{}: {} ({})'.format(name, cv_results.mean(), cv_results.std())\n",
    "            print(mensaje)\n",
    "        except ValueError as e:\n",
    "            print(e)\n",
    "comparar_modelos()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# RANDOM FOREST\n",
    "# separar datos de prueba y de entrenamiento\n",
    "def crear_random_forest(input_df, dimensiones):\n",
    "\n",
    "    df_train, df_test = train_test_split(input_df, test_size=0.2)\n",
    "\n",
    "    train_obj = df_train[df_train.columns[-1:]]\n",
    "\n",
    "    train_features, train_objective = split_objective(df_train, dimensiones)\n",
    "    test_features, test_objective = split_objective(df_train, dimensiones)\n",
    "\n",
    "    clf = RandomForestClassifier(n_estimators=1000)\n",
    "    print('Entrenando...')\n",
    "    clf.fit(\n",
    "        train_features.values,\n",
    "        train_objective.values.ravel())\n",
    "    print('Entrenamiento finalizado')\n",
    "    predict = clf.predict(test_features.values)\n",
    "    accuracy = accuracy_score(test_objective.values, predict)\n",
    "    print('Punteo: {}'.format(clf.score(test_features, test_objective)))\n",
    "    feats = {}\n",
    "    for feature, importance in zip(train_features.columns, clf.feature_importances_):\n",
    "        feats[feature] = importance * 100\n",
    "    return clf, accuracy, feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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Atqmk20+STwLfBHZNcmmSFyd5WZKXjW9yHHARcCFwOPDHAONFPt/MqAh8GvCmmYU/bwun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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1519be12940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entrenando...\n",
      "Entrenamiento finalizado\n",
      "Punteo: 0.9645038686032307\n"
     ]
    }
   ],
   "source": [
    "def probar_random_forest():\n",
    "    rf_dimensiones = {\n",
    "        'Area': 'category',\n",
    "        'Sexo_Recode': 'category',\n",
    "        'Edad': 'int',\n",
    "        'Etnia_Recode': 'category',\n",
    "        'P9': 'category',\n",
    "        'P24_Computadora': 'category',\n",
    "        'P28_Maestro_Deja_Deberes_Hacer_Casa': 'category',\n",
    "        'P31_Clases_Computación': 'category',\n",
    "        'P37_Te_Tratan_Mal_Escuela': 'category',\n",
    "        'P61_Te_Han_Esenado_Utilizar_Calculadora': 'category',\n",
    "        'P62_Utilizas_Calculadora_Clase_Matematicas': 'category',\n",
    "        'P59_Gusta_Resolver_Ejercicios_Matematicas': 'category',\n",
    "        'P63_Ensayo_Y_Error': 'category',\n",
    "        'P63_Buscar_Un_Patron': 'category',\n",
    "        'P63_Rosolver_Un_Problema_Similar_Pero_Mas_Simple': 'category',\n",
    "        'P63_Hacer_Un_Diagrama': 'category',\n",
    "        'P63_Usar_Razonamiento_Indirecto': 'category',\n",
    "        'P63_Usar_Casos': 'category',\n",
    "        'P63_Rosolver_Un_Problema_Equivalente': 'category',\n",
    "        'P63_Buscar_Una_Formula': 'category',\n",
    "        'P63_Usar_Analisis_Dimensionales': 'category',\n",
    "        'P63_Usar_Coordenadas': 'category',\n",
    "        'P63_Usar_Una_Variable': 'category',\n",
    "        'P63_Usar_Una_Lista': 'category',\n",
    "        'P63_Hacer_Una_Figura': 'category',\n",
    "        'P63_Usar_Razonamiento_Directo': 'category',\n",
    "        'P63_Usar_Las_Propiedades_De_Los_Numeros': 'category',\n",
    "        'P63_Trabajar_Hacia_Atras': 'category',\n",
    "        'P63_Resolver_Una_Educacion': 'category',\n",
    "        'P63_Usar_Un_Modelo': 'category',\n",
    "        'P63_Identificar_Submeta': 'category',\n",
    "        'P63_Usar_Simetria': 'category',\n",
    "        'DESEMPENO_MATE': 'category',\n",
    "    }\n",
    "    \n",
    "    rf_3ro = {\n",
    "        'Cod_Area': 'category',\n",
    "        'Sexo_Recode': 'category',\n",
    "        'Edad': 'int',\n",
    "        'Etnia_Recode': 'category',\n",
    "        'Repetido_Grado': 'category',\n",
    "        'P_6': 'category',\n",
    "        'P_19_Computadora': 'category',\n",
    "        'S4_4_Maestro_Deja_Deberes_Hacer_Casa': 'category',\n",
    "        'S4_5_Maestro_Devuelve_Deberes_Calificaciones': 'category',\n",
    "        'S4_6_Maestro_Devuelve_Trabajos_Revisados': 'category',\n",
    "        'S4_9_Alguien_Molesta_Mucho': 'category',\n",
    "        'S4_10_Maestro_Llamado_Atencion_Alguna_Vez': 'category',\n",
    "        'S4_11_Maestro_Castigado_Alguna_Vez': 'category',\n",
    "        'S4_12_Lastimado_Alguien_Escuela': 'category',\n",
    "        'S4_13_Te_Tratan_Mal_Escuela': 'category',\n",
    "        'DESEMPENO_MATE': 'category'\n",
    "    }\n",
    "    rf_df = preparar_datos(rf_dimensiones)\n",
    "    # rf_df = preparar_datos(rf_3ro, '3RO2014.csv')\n",
    "    \n",
    "    # Preparar gráfico\n",
    "    correlations = rf_df.corr()\n",
    "    fig = plt.figure(figsize=(20,20))\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(correlations, vmin=-1, vmax=1)\n",
    "    fig.colorbar(cax)\n",
    "    ax.set_xticks(range(0, len(rf_dimensiones.keys())), rf_dimensiones.keys())\n",
    "    ax.set_yticks(range(len(rf_dimensiones.keys())), rf_dimensiones.keys())\n",
    "    ax.set_xticklabels(rf_dimensiones.keys(), rotation=90)\n",
    "    # ax.set_yticklabels(rf_dimensiones.keys())\n",
    "    plt.show()\n",
    "    \n",
    "    # Separar los datos\n",
    "    train_features, train_objective = split_objective(rf_df, rf_dimensiones)\n",
    "    # crear y entrenar random forest\n",
    "    rf_clf, rf_accuracy, rf_importances = crear_random_forest(rf_df, rf_dimensiones)\n",
    "    \n",
    "    return rf_clf, rf_accuracy, rf_importances\n",
    "\n",
    "rf_clf, rf_accuracy, rf_dimensiones = probar_random_forest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.9645038686032307\n"
     ]
    },
    {
     "data": {
      "image/png": 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74A0Rcd87/IOI/SNiaUQsve6660ZokiRJkjQ7RkmYVwBb913eCvjZVLep5Rd3A24A9gW+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TOSiWWmYFSSMcIICf1S3JKWm+gjYK7tlTL390nbuY5xNiQtktK477D9pYLtlyOuFX8bcBy9lOWc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\nA1+tSq+9Fnib7d0bD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LQsHoXbXTcDl0j6JnB/a6ftYxuLKCIiepZVohEjQNI8SUdLulbS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\nIiIiIqaRAXNERERExDT+P2BviNrp3K/QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1519c343d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "print('Precision: {}'.format(rf_accuracy))\n",
    "importance = rf_clf.feature_importances_\n",
    "importance = pd.DataFrame(\n",
    "    importance,\n",
    "    index=list(rf_dimensiones.keys()),\n",
    "    columns=['Importance']\n",
    ")\n",
    "importance['Std'] = np.std(\n",
    "    [tree.feature_importances_ for tree in rf_clf.estimators_],\n",
    "    axis=0\n",
    ")\n",
    "\n",
    "x = range(importance.shape[0])\n",
    "y = importance.iloc[:, 0]\n",
    "yerr = importance.iloc[:, 1]\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Importancia de las preguntas')\n",
    "plt.bar(x, y, yerr=yerr, align=\"center\")\n",
    "plt.xticks(x, rf_dimensiones.keys(), rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}