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ejercicio_corto.ipynb
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"text": [
"Warning message:\n",
"\"package 'caret' was built under R version 3.3.3\"Loading required package: lattice\n",
"Loading required package: ggplot2\n",
"Warning message:\n",
"\"package 'ggplot2' was built under R version 3.3.3\"Warning message:\n",
"\"package 'PerformanceAnalytics' was built under R version 3.3.3\"Loading required package: xts\n",
"Warning message:\n",
"\"package 'xts' was built under R version 3.3.3\"Loading required package: zoo\n",
"Warning message:\n",
"\"package 'zoo' was built under R version 3.3.3\"\n",
"Attaching package: 'zoo'\n",
"\n",
"The following objects are masked from 'package:base':\n",
"\n",
" as.Date, as.Date.numeric\n",
"\n",
"\n",
"Attaching package: 'PerformanceAnalytics'\n",
"\n",
"The following object is masked from 'package:graphics':\n",
"\n",
" legend\n",
"\n",
"Loaded lars 1.2\n",
"\n"
]
}
],
"source": [
"library(caret)\n",
"library(PerformanceAnalytics)\n",
"library(MASS)\n",
"library(lars)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Lectura de los datos\n",
"Leemos los datos de entrada (almacenados como fichero *.csv* previamente para ser de más fácil manejo) y establecemos la semilla generadora de números aleatorios a un valor fijo para que nos permita rehacer los experimentos."
]
},
{
"cell_type": "code",
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"data": {
"text/plain": [
" q1 q2 q3 q4 q5 q6 q7 q8 q9 q10 \n",
" A : 1 A: 1 A: 3 A : 2 A:8 A:10 A: 1 A : 2 B: 3 A:3 \n",
" B : 5 B:22 B: 1 B : 5 B:3 B: 1 C: 1 C : 8 C: 9 B:9 \n",
" C :10 C:12 C : 2 C:5 C: 1 D: 2 E :12 D:10 C:4 \n",
" D : 1 E: 7 D :11 D:6 D:10 E:19 NA's: 1 E: 1 D:1 \n",
" E : 5 E : 2 E:1 E: 1 E:6 \n",
" NA's: 1 NA's: 1 \n",
" q11 q12 q13 q14 q15 q16 q17 q18 q19 q20 \n",
" A:10 A:9 A: 1 A : 1 A : 3 A :11 A:5 A:2 A: 1 A :4 \n",
" B: 2 B:1 B:20 B :12 B :10 B : 8 B:8 B:7 B: 2 B :3 \n",
" C: 3 D:6 E: 2 C : 4 C : 6 C : 1 D:8 C:5 C:16 C :3 \n",
" D: 6 E:7 E : 5 D : 3 E : 1 E:2 D:5 D: 1 D :4 \n",
" E: 2 NA's: 1 NA's: 1 NA's: 2 E:4 E: 3 E :8 \n",
" NA's:1 \n",
" q21 q22 q23 q24 q25 total \n",
" A: 5 A: 2 A : 2 A : 2 A: 4 Min. : 4.0 \n",
" B: 1 B: 4 B : 5 B : 2 B:11 1st Qu.: 9.5 \n",
" C: 4 C: 2 D : 1 C :15 C: 4 Median :11.0 \n",
" D:12 D: 3 E :14 E : 3 D: 3 Mean :11.0 \n",
" E: 1 E:12 NA's: 1 NA's: 1 E: 1 3rd Qu.:12.0 \n",
" Max. :17.0 "
]
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"source": [
"# Set a random seed to reproduce the experiments\n",
"seed = 12345\n",
"set.seed(seed)\n",
"\n",
"# Read the solution found with the evolution strategies approach and bind it to the data\n",
"sol = read.csv('data/exam_sol.csv')\n",
"d = read.csv('data/data.csv')\n",
"\n",
"# Visualize a summary of the data\n",
"summary(d)\n",
"\n",
"# Helper functions\n",
"substrLeft <- function(x, n){\n",
" substr(x, 2, nchar(x)-n)\n",
"}\n",
"substrRight <- function(x, n){\n",
" substr(x, nchar(x)-n+1, nchar(x))\n",
"}\n",
"\n",
"scores_real = unlist(unname(d['total'])) # the real score for each student\n",
"get_scores = function(correct_answers){\n",
" # correct_answers is a vector of 25 A, B, etc.\n",
" # returns the scores all students would get\n",
" \n",
" # for each student, check what score they would get if correct_answers were the correct answers\n",
" scores = NULL \n",
" for (i in 1:23){\n",
" student_answers = t(d)[1:25,i]\n",
" scores = c(scores, sum( student_answers == correct_answers, na.rm = T ) )\n",
" }\n",
" return(scores)\n",
"}\n",
"\n",
"# Obtain a solution based on the letters with more answers in each question\n",
"solmod <- rapply(lapply(lapply(apply(d[,1:25],2, table), sort),tail,n=1), names)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Resumen de los datos\n",
"Al mostrar el resumen de los datos, vemos que cada pregunta tiene respuestas que varían de la *A-E* conteniendo *NAs* en algunas de ellas. Tambien observamos como no todas preguntas contienen a todas las respuestas, por ejemplo *q23* no tiene la respuesta *C*."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Primera aproximación\n",
"## Creación de variables dummy\n",
"Dado que las preguntas tienen como respuestas un conjunto de factores, la mejor forma de manipularlos será creando variables *dummy*. Dado que es la primera aproximación al problema, las observaciones con NAs se eliminaran (teniendo en mente que podrán ser de ayuda en un futuro)."
]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"[1] \"Columns with all their elements = 0 : q6B\" \n",
"[2] \"Columns with all their elements = 0 : q6C\" \n",
"[3] \"Columns with all their elements = 0 : q11E\"\n",
"[1] \"dummy cols: 82 rows: 20\"\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>q1A</th><th scope=col>q1B</th><th scope=col>q1C</th><th scope=col>q1D</th><th scope=col>q1E</th><th scope=col>q2B</th><th scope=col>q3B</th><th scope=col>q3C</th><th scope=col>q3E</th><th scope=col>q4B</th><th scope=col>q4C</th><th scope=col>q4D</th><th scope=col>q4E</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>1</th><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
"\t<tr><th scope=row>2</th><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>\n",
"\t<tr><th scope=row>4</th><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
"\t<tr><th scope=row>5</th><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
"\t<tr><th scope=row>6</th><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
"\t<tr><th scope=row>7</th><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllllllllllll}\n",
" & q1A & q1B & q1C & q1D & q1E & q2B & q3B & q3C & q3E & q4B & q4C & q4D & q4E\\\\\n",
"\\hline\n",
"\t1 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 0\\\\\n",
"\t2 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 1 & 0 & 0\\\\\n",
"\t4 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0\\\\\n",
"\t5 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 0 & 0\\\\\n",
"\t6 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 0\\\\\n",
"\t7 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 0\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| <!--/--> | q1A | q1B | q1C | q1D | q1E | q2B | q3B | q3C | q3E | q4B | q4C | q4D | q4E | \n",
"|---|---|---|---|---|---|\n",
"| 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | \n",
"| 2 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | \n",
"| 4 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | \n",
"| 5 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | \n",
"| 6 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | \n",
"| 7 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | \n",
"\n",
"\n"
],
"text/plain": [
" q1A q1B q1C q1D q1E q2B q3B q3C q3E q4B q4C q4D q4E\n",
"1 0 1 0 0 0 1 0 1 0 0 0 1 0 \n",
"2 0 0 1 0 0 1 0 0 1 0 1 0 0 \n",
"4 0 1 0 0 0 1 0 0 1 0 0 0 0 \n",
"5 0 0 1 0 0 1 0 1 0 1 0 0 0 \n",
"6 0 1 0 0 0 1 0 1 0 0 0 1 0 \n",
"7 0 0 0 0 1 1 0 0 1 0 0 1 0 "
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"source": [
"# Create dummy vars for each answer\n",
"options(na.action='na.pass')\n",
"d_dummy <- as.data.frame(model.matrix( ~ . - 1, data=d))\n",
"data <- d_dummy[complete.cases(d_dummy),]\n",
"\n",
"# Visualize the dimensions of the data\n",
"print (paste0(\"Columns with all their elements = 0 : \",\n",
" names(data[, colSums(data!=0)<=0])))\n",
"\n",
"# Delete columns with all the answers = 0\n",
"data <- data[, colSums(data != 0) > 0]\n",
"\n",
"# Read the dimensions of the dummy var with no NAs\n",
"n_rows <- dim(data)[1]\n",
"n_cols <- dim(data)[2]\n",
"print(paste0(\"dummy cols: \", n_cols, \" rows: \", n_rows))\n",
"\n",
"# Create the input matrix and the response vector\n",
"x <- as.matrix(data[1:n_rows, 1:n_cols-1])\n",
"y <- data[1:n_rows, n_cols]\n",
"\n",
"# Show the dummy vars, making the table smaller for convenience\n",
"head(data[,1:13])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Los resultados tras crear las variables dummy y eliminar las columnas cuyos valores eran todos cero (recordemos que en el enunciado se dice que por lo menos un alumno ha acertado, lo que implica que si todo son ceros, esa respuesta no será la correcta) nos devuelve una nueva matriz con 81 variables de entrada correspondientes a las variables dummy generadas y una variable respuesta. Además de un total de 20 observaciones de las 23 originales, por lo tanto, tres alumnos han sido eliminados dado que sus respuestas contenian *NAs*"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Feature selection \n",
"\n",
"Para intentar obtener un patrón de respuestas correcto, planteamos el uso de algún tipo de regresión penalizada que reduzca el valor de los coeficientes. Por esta vía se podrían emplear la regresión *Ridge* que penaliza usando la norma $L_2$ o la regresión *Lasso* que penaliza la norma $L_1$. Para este particular problema será de interés la regresión Lasso, dado que la Ridge no \"elimina\" coeficientes, resultando en que todos se incluyan en el modelo o ninguno este incluido (si la penalización es realmente grande). Como era de esperar, se ha explorado la regresión Ridge y el resultado ha sido insatisfactorio."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Uso de Lasso\n",
"Finalmente, decidimos hacer uso de la regresión Lasso que, recordemos, usa la norma $L_1$. Para la selección del modelo hacemos uso de validación cruzada, con tantos folds como número de observaciones (alumnos) tenemos en nuestros datos. "
]
},
{
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"execution_count": 36,
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{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, :\n",
"\"There were missing values in resampled performance measures.\""
]
},
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"text": [
"[1] 0.998989\n"
]
},
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iVVOnYc+cLnjzbKFUekGPBLbCmVjh5EofDZg41yxdm0GgN+mS2i0vEjaBQ+d6xR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\n",
"text/plain": [
"plot without title"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Especify the parameters to do cross-validation\n",
"trainctr<-trainControl(method=\"cv\", number=n_rows)\n",
"\n",
"# Start the CV process\n",
"larsTune<-train(x, y, method=\"lars\",\n",
" tuneGrid=data.frame(.fraction=seq(0.00001, 0.99999, length=1000)),\n",
" trControl=trainctr, normalize=FALSE, intercept=FALSE,\n",
" type=\"lasso\")\n",
"\n",
"# Plot a graph with the obtained results\n",
"plot(larsTune)\n",
"\n",
"# Find the best fraction\n",
"bestfraction = larsTune$bestTune$fraction\n",
"print (bestfraction)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"El resultado obtenido muestra como cuando el valor de *Fraction* (s) es proximo a 1 (0.998989) el error del modelo es el mínimo.\n",
"\n",
"Creamos el modelo Lasso con todos los datos, y mostramos el error obtenido en forma de suma de cuadrados residuales (RSS)."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>Df</th><th scope=col>Rss</th><th scope=col>Cp</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>26</th><td>18 </td><td>3.496775e+00</td><td>NaN </td></tr>\n",
"\t<tr><th scope=row>27</th><td>19 </td><td>3.325350e+00</td><td>NaN </td></tr>\n",
"\t<tr><th scope=row>28</th><td>20 </td><td>1.145819e+00</td><td>NaN </td></tr>\n",
"\t<tr><th scope=row>29</th><td>19 </td><td>5.388520e-01</td><td>NaN </td></tr>\n",
"\t<tr><th scope=row>30</th><td>20 </td><td>3.266912e-01</td><td>NaN </td></tr>\n",
"\t<tr><th scope=row>31</th><td>19 </td><td>2.134229e-01</td><td>NaN </td></tr>\n",
"\t<tr><th scope=row>32</th><td>20 </td><td>1.344535e-26</td><td>NaN </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lll}\n",
" & Df & Rss & Cp\\\\\n",
"\\hline\n",
"\t26 & 18 & 3.496775e+00 & NaN \\\\\n",
"\t27 & 19 & 3.325350e+00 & NaN \\\\\n",
"\t28 & 20 & 1.145819e+00 & NaN \\\\\n",
"\t29 & 19 & 5.388520e-01 & NaN \\\\\n",
"\t30 & 20 & 3.266912e-01 & NaN \\\\\n",
"\t31 & 19 & 2.134229e-01 & NaN \\\\\n",
"\t32 & 20 & 1.344535e-26 & NaN \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| <!--/--> | Df | Rss | Cp | \n",
"|---|---|---|---|---|---|---|\n",
"| 26 | 18 | 3.496775e+00 | NaN | \n",
"| 27 | 19 | 3.325350e+00 | NaN | \n",
"| 28 | 20 | 1.145819e+00 | NaN | \n",
"| 29 | 19 | 5.388520e-01 | NaN | \n",
"| 30 | 20 | 3.266912e-01 | NaN | \n",
"| 31 | 19 | 2.134229e-01 | NaN | \n",
"| 32 | 20 | 1.344535e-26 | NaN | \n",
"\n",
"\n"
],
"text/plain": [
" Df Rss Cp \n",
"26 18 3.496775e+00 NaN\n",
"27 19 3.325350e+00 NaN\n",
"28 20 1.145819e+00 NaN\n",
"29 19 5.388520e-01 NaN\n",
"30 20 3.266912e-01 NaN\n",
"31 19 2.134229e-01 NaN\n",
"32 20 1.344535e-26 NaN"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Fit the model and show its last iterations\n",
"lars.model<-lars(x, y,\n",
" type=\"lasso\",\n",
" normalize=F, \n",
" intercept=F, \n",
" max.steps=300)\n",
"tail(summary(lars.model), 7)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Obtenemos los coeficientes del modelo ajustado con menor error, en este caso se trata de la iteración 32 que tiene un total de 20 variables no nulas. Mostramos los coeficientes y el valor del *fraction* del modelo."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"get.exam.answers <- function(solution, sol){\n",
" names <- as.numeric(substrLeft(solution,1))\n",
" answer <- substrRight(solution,1)\n",
" exam.answers <- NULL\n",
" j <- 1\n",
" for (i in 1:25){\n",
" if (i %in% names){\n",
" exam.answers <- c(exam.answers, answer[j])\n",
" j <- j + 1\n",
" }\n",
" else{\n",
" exam.answers <- c(exam.answers, sol[i])\n",
" }\n",
" }\n",
" names(exam.answers) = paste0('q',1:25)\n",
" exam.answers\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" q1 q2 q3 q4 q5 q6 q7 q8 q9 q10 q11 q12 q13 q14 q15 q16 q17 q18 q19 q20 \n",
"\"C\" \"B\" \"C\" \"D\" \"A\" \"D\" \"E\" \"C\" \"D\" \"E\" \"A\" \"A\" \"B\" \"B\" \"B\" \"A\" \"D\" \"B\" \"C\" \"B\" \n",
"q21 q22 q23 q24 q25 \n",
"\"C\" \"E\" \"B\" \"C\" \"B\" \n",
" [1] \"q1C\" \"q2B\" \"q3C\" \"q4D\" \"q7E\" \"q8C\" \"q9D\" \"q10E\" \"q13B\" \"q14B\"\n",
"[11] \"q15B\" \"q17D\" \"q19C\" \"q20B\" \"q21C\" \"q22E\" \"q23B\" \"q24C\" \"q25B\"\n"
]
},
{
"data": {
"text/html": [
"'Diferencia de resultado por estudiante:'"
],
"text/latex": [
"'Diferencia de resultado por estudiante:'"
],
"text/markdown": [
"'Diferencia de resultado por estudiante:'"
],
"text/plain": [
"[1] \"Diferencia de resultado por estudiante:\""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>0</li>\n",
"\t<li>-1</li>\n",
"\t<li>-1</li>\n",
"\t<li>-1</li>\n",
"\t<li>-1</li>\n",
"\t<li>-2</li>\n",
"\t<li>-1</li>\n",
"\t<li>1</li>\n",
"\t<li>-2</li>\n",
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"\t<li>0</li>\n",
"\t<li>-1</li>\n",
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"\t<li>-1</li>\n",
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"\t<li>-1</li>\n",
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"\t<li>-1</li>\n",
"\t<li>0</li>\n",
"\t<li>-1</li>\n",
"\t<li>-1</li>\n",
"\t<li>-2</li>\n",
"</ol>\n"
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"\\begin{enumerate*}\n",
"\\item 0\n",
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"\\item -2\n",
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"\\item -1\n",
"\\item -1\n",
"\\item -2\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 0\n",
"2. -1\n",
"3. -1\n",
"4. -1\n",
"5. -1\n",
"6. -2\n",
"7. -1\n",
"8. 1\n",
"9. -2\n",
"10. -1\n",
"11. 1\n",
"12. 0\n",
"13. -1\n",
"14. 0\n",
"15. -1\n",
"16. 0\n",
"17. -1\n",
"18. 0\n",
"19. -1\n",
"20. 0\n",
"21. -1\n",
"22. -1\n",
"23. -2\n",
"\n",
"\n"
],
"text/plain": [
" [1] 0 -1 -1 -1 -1 -2 -1 1 -2 -1 1 0 -1 0 -1 0 -1 0 -1 0 -1 -1 -2"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"'Diferencia total:'"
],
"text/latex": [
"'Diferencia total:'"
],
"text/markdown": [
"'Diferencia total:'"
],
"text/plain": [
"[1] \"Diferencia total:\""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"20"
],
"text/latex": [
"20"
],
"text/markdown": [
"20"
],
"text/plain": [
"[1] 20"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Obtain the coefficients of the fitted model together with its fraction value.\n",
"val <- predict(lars.model, x, type=\"coefficients\")\n",
"fraction <- val$fraction[32]\n",
"coef.na.deleted <- val$coefficients[32,]\n",
"answer.na.deleted <- names(coef.na.deleted)[coef.na.deleted>0]\n",
"na.deleted <- get.exam.answers(answer.na.deleted,\n",
" solmod)\n",
"print(na.deleted)\n",
"\n",
"print(answer.na.deleted)\n",
"scores_guess = get_scores(correct_answers = na.deleted)\n",
"\"Diferencia de resultado por estudiante:\"\n",
"scores_real - scores_guess\n",
"\"Diferencia total:\"\n",
"sum(abs(scores_real - scores_guess))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Obteniendo solo los coeficientes que tienen valor positivo somos capaces de obtener un patrón con 19 posibles respuestas al examen. Sustituyendo los valores faltantes por la respuesta con mayor frecuencia en cada pregunta obtenemos un patrón que da como máximo dos respuestas de error. A priori, podría pensarse que este modelo acierta 23 de las 25 preguntas."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Uso de maquinas de vector-soporte\n",
"Uso de máquinas de vector soporte para encontrar el patrón de respuestas correcto"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"\n",
"Parameter tuning of 'svm':\n",
"\n",
"- sampling method: 3-fold cross validation \n",
"\n",
"- best parameters:\n",
" gamma cost epsilon kernel\n",
" 0.0078125 1 0 linear\n",
"\n",
"- best performance: 3.692778 \n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"library(e1071)\n",
"obj <- tune(svm, total ~ ., data = data, \n",
" ranges = list(gamma = 2^(-7:3),\n",
" cost = 2^(-3:5),\n",
" epsilon = seq(0, 1, length=15),\n",
" kernel = \"linear\",\n",
" type=\"eps-regression\"),\n",
" tunecontrol = tune.control(sampling = \"cross\", cross=3),\n",
" seed = seed,\n",
" scale=F\n",
" )\n",
"obj\n"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [1] \"q1C\" \"q3C\" \"q4B\" \"q4C\" \"q4D\" \"q6B\" \"q6D\" \"q6E\" \"q7C\" \"q7E\" \n",
"[11] \"q8C\" \"q9D\" \"q10B\" \"q10D\" \"q10E\" \"q13B\" \"q14B\" \"q15B\" \"q15D\" \"q16C\"\n",
"[21] \"q16E\" \"q17D\" \"q18B\" \"q18E\" \"q19C\" \"q19E\" \"q20B\" \"q20C\" \"q20E\" \"q21B\"\n",
"[31] \"q21C\" \"q22B\" \"q22E\" \"q23B\" \"q23E\" \"q24B\" \"q24C\" \"q24E\" \"q25B\"\n"
]
}
],
"source": [
"model <- svm(total ~ ., data=data, \n",
" kernel=\"linear\",\n",
" gamma = 0.0078125,\n",
" cost = 1,\n",
" epsilon = 0,\n",
" scale=F,\n",
" type=\"eps-regression\")\n",
"coef.svm <- t(model$coefs) %*%model$SV\n",
"print (colnames(coef.svm)[coef.svm>0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Random Forest\n"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"\n",
"Call:\n",
" randomForest(formula = total ~ ., data = data, importance = TRUE, nPerm = 2, mtry = 25, ntree = 5000) \n",
" Type of random forest: regression\n",
" Number of trees: 5000\n",
"No. of variables tried at each split: 25\n",
"\n",
" Mean of squared residuals: 6.773305\n",
" % Var explained: 25.82"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>'q10E'</li>\n",
"\t<li>'q22E'</li>\n",
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"\t<li>'q10C'</li>\n",
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"</ol>\n"
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"\\begin{enumerate*}\n",
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"\\item 'q20E'\n",
"\\item 'q10B'\n",
"\\item 'q4D'\n",
"\\item 'q17D'\n",
"\\item 'q21D'\n",
"\\end{enumerate*}\n"
],
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"1. 'q10E'\n",
"2. 'q22E'\n",
"3. 'q4E'\n",
"4. 'q10C'\n",
"5. 'q18C'\n",
"6. 'q8C'\n",
"7. 'q24C'\n",
"8. 'q21C'\n",
"9. 'q16C'\n",
"10. 'q3C'\n",
"11. 'q2B'\n",
"12. 'q19C'\n",
"13. 'q14B'\n",
"14. 'q11D'\n",
"15. 'q11E'\n",
"16. 'q1B'\n",
"17. 'q23B'\n",
"18. 'q15B'\n",
"19. 'q8E'\n",
"20. 'q18B'\n",
"21. 'q20E'\n",
"22. 'q10B'\n",
"23. 'q4D'\n",
"24. 'q17D'\n",
"25. 'q21D'\n",
"\n",
"\n"
],
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" [1] \"q10E\" \"q22E\" \"q4E\" \"q10C\" \"q18C\" \"q8C\" \"q24C\" \"q21C\" \"q16C\" \"q3C\" \n",
"[11] \"q2B\" \"q19C\" \"q14B\" \"q11D\" \"q11E\" \"q1B\" \"q23B\" \"q15B\" \"q8E\" \"q18B\"\n",
"[21] \"q20E\" \"q10B\" \"q4D\" \"q17D\" \"q21D\""
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
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"1. 'q10E'\n",
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"8. 'q8C'\n",
"9. 'q24C'\n",
"10. 'q11D'\n",
"11. 'q10B'\n",
"12. 'q18B'\n",
"13. 'q8E'\n",
"14. 'q15B'\n",
"15. 'q4D'\n",
"16. 'q21D'\n",
"17. 'q14B'\n",
"18. 'q16B'\n",
"19. 'q25D'\n",
"20. 'q25C'\n",
"21. 'q9C'\n",
"22. 'q17B'\n",
"23. 'q12D'\n",
"24. 'q18D'\n",
"25. 'q13B'\n",
"\n",
"\n"
],
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" [1] \"q10E\" \"q22E\" \"q10C\" \"q4E\" \"q21C\" \"q3C\" \"q19C\" \"q8C\" \"q24C\" \"q11D\"\n",
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"[21] \"q9C\" \"q17B\" \"q12D\" \"q18D\" \"q13B\""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"library(randomForest)\n",
"fit <- randomForest(total ~ .,\n",
" data=data, \n",
" importance=TRUE,\n",
" nPerm=2,\n",
" mtry=25,\n",
" ntree=5000)\n",
"fit\n",
"imp <- as.data.frame(fit$importance)\n",
"\n",
"imp.sorted <- imp[order(-imp['IncNodePurity']),]\n",
"rownames(imp.sorted[1:25,])\n",
"imp.sorted <- imp[order(-imp['%IncMSE']),]\n",
"rownames(imp.sorted[1:25,])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Boosting methods"
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>'call'</li>\n",
"\t<li>'params'</li>\n",
"\t<li>'callbacks'</li>\n",
"\t<li>'evaluation_log'</li>\n",
"\t<li>'niter'</li>\n",
"\t<li>'folds'</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 'call'\n",
"\\item 'params'\n",
"\\item 'callbacks'\n",
"\\item 'evaluation\\_log'\n",
"\\item 'niter'\n",
"\\item 'folds'\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 'call'\n",
"2. 'params'\n",
"3. 'callbacks'\n",
"4. 'evaluation_log'\n",
"5. 'niter'\n",
"6. 'folds'\n",
"\n",
"\n"
],
"text/plain": [
"[1] \"call\" \"params\" \"callbacks\" \"evaluation_log\"\n",
"[5] \"niter\" \"folds\" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>iter</th><th scope=col>train_rmse_mean</th><th scope=col>train_rmse_std</th><th scope=col>test_rmse_mean</th><th scope=col>test_rmse_std</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>20 </td><td>0.00386060 </td><td>2.537191e-03</td><td>2.048255 </td><td>1.126467 </td></tr>\n",
"\t<tr><td>21 </td><td>0.00283360 </td><td>1.943153e-03</td><td>2.048228 </td><td>1.126368 </td></tr>\n",
"\t<tr><td>22 </td><td>0.00210150 </td><td>1.477531e-03</td><td>2.048224 </td><td>1.126370 </td></tr>\n",
"\t<tr><td>23 </td><td>0.00158265 </td><td>1.115964e-03</td><td>2.048224 </td><td>1.126406 </td></tr>\n",
"\t<tr><td>24 </td><td>0.00121800 </td><td>8.258347e-04</td><td>2.048239 </td><td>1.126381 </td></tr>\n",
"\t<tr><td>25 </td><td>0.00095550 </td><td>5.973987e-04</td><td>2.048265 </td><td>1.126388 </td></tr>\n",
"\t<tr><td>26 </td><td>0.00078915 </td><td>4.240827e-04</td><td>2.048268 </td><td>1.126395 </td></tr>\n",
"\t<tr><td>27 </td><td>0.00068490 </td><td>2.920000e-04</td><td>2.048243 </td><td>1.126376 </td></tr>\n",
"\t<tr><td>28 </td><td>0.00060925 </td><td>1.980209e-04</td><td>2.048201 </td><td>1.126373 </td></tr>\n",
"\t<tr><td>29 </td><td>0.00057450 </td><td>1.480708e-04</td><td>2.048216 </td><td>1.126385 </td></tr>\n",
"\t<tr><td>30 </td><td>0.00054005 </td><td>1.109723e-04</td><td>2.048205 </td><td>1.126383 </td></tr>\n",
"\t<tr><td>31 </td><td>0.00052640 </td><td>8.095950e-05</td><td>2.048169 </td><td>1.126380 </td></tr>\n",
"\t<tr><td>32 </td><td>0.00051395 </td><td>5.771263e-05</td><td>2.048139 </td><td>1.126339 </td></tr>\n",
"\t<tr><td>33 </td><td>0.00051360 </td><td>5.732748e-05</td><td>2.048136 </td><td>1.126337 </td></tr>\n",
"\t<tr><td>34 </td><td>0.00051345 </td><td>5.719657e-05</td><td>2.048135 </td><td>1.126336 </td></tr>\n",
"\t<tr><td>35 </td><td>0.00051350 </td><td>5.721145e-05</td><td>2.048134 </td><td>1.126336 </td></tr>\n",
"\t<tr><td>36 </td><td>0.00051345 </td><td>5.721755e-05</td><td>2.048134 </td><td>1.126336 </td></tr>\n",
"\t<tr><td>37 </td><td>0.00051345 </td><td>5.720007e-05</td><td>2.048134 </td><td>1.126336 </td></tr>\n",
"\t<tr><td>38 </td><td>0.00051350 </td><td>5.721058e-05</td><td>2.048134 </td><td>1.126335 </td></tr>\n",
"\t<tr><td>39 </td><td>0.00051350 </td><td>5.721058e-05</td><td>2.048134 </td><td>1.126335 </td></tr>\n",
"\t<tr><td>40 </td><td>0.00051350 </td><td>5.721058e-05</td><td>2.048134 </td><td>1.126335 </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" iter & train\\_rmse\\_mean & train\\_rmse\\_std & test\\_rmse\\_mean & test\\_rmse\\_std\\\\\n",
"\\hline\n",
"\t 20 & 0.00386060 & 2.537191e-03 & 2.048255 & 1.126467 \\\\\n",
"\t 21 & 0.00283360 & 1.943153e-03 & 2.048228 & 1.126368 \\\\\n",
"\t 22 & 0.00210150 & 1.477531e-03 & 2.048224 & 1.126370 \\\\\n",
"\t 23 & 0.00158265 & 1.115964e-03 & 2.048224 & 1.126406 \\\\\n",
"\t 24 & 0.00121800 & 8.258347e-04 & 2.048239 & 1.126381 \\\\\n",
"\t 25 & 0.00095550 & 5.973987e-04 & 2.048265 & 1.126388 \\\\\n",
"\t 26 & 0.00078915 & 4.240827e-04 & 2.048268 & 1.126395 \\\\\n",
"\t 27 & 0.00068490 & 2.920000e-04 & 2.048243 & 1.126376 \\\\\n",
"\t 28 & 0.00060925 & 1.980209e-04 & 2.048201 & 1.126373 \\\\\n",
"\t 29 & 0.00057450 & 1.480708e-04 & 2.048216 & 1.126385 \\\\\n",
"\t 30 & 0.00054005 & 1.109723e-04 & 2.048205 & 1.126383 \\\\\n",
"\t 31 & 0.00052640 & 8.095950e-05 & 2.048169 & 1.126380 \\\\\n",
"\t 32 & 0.00051395 & 5.771263e-05 & 2.048139 & 1.126339 \\\\\n",
"\t 33 & 0.00051360 & 5.732748e-05 & 2.048136 & 1.126337 \\\\\n",
"\t 34 & 0.00051345 & 5.719657e-05 & 2.048135 & 1.126336 \\\\\n",
"\t 35 & 0.00051350 & 5.721145e-05 & 2.048134 & 1.126336 \\\\\n",
"\t 36 & 0.00051345 & 5.721755e-05 & 2.048134 & 1.126336 \\\\\n",
"\t 37 & 0.00051345 & 5.720007e-05 & 2.048134 & 1.126336 \\\\\n",
"\t 38 & 0.00051350 & 5.721058e-05 & 2.048134 & 1.126335 \\\\\n",
"\t 39 & 0.00051350 & 5.721058e-05 & 2.048134 & 1.126335 \\\\\n",
"\t 40 & 0.00051350 & 5.721058e-05 & 2.048134 & 1.126335 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"iter | train_rmse_mean | train_rmse_std | test_rmse_mean | test_rmse_std | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| 20 | 0.00386060 | 2.537191e-03 | 2.048255 | 1.126467 | \n",
"| 21 | 0.00283360 | 1.943153e-03 | 2.048228 | 1.126368 | \n",
"| 22 | 0.00210150 | 1.477531e-03 | 2.048224 | 1.126370 | \n",
"| 23 | 0.00158265 | 1.115964e-03 | 2.048224 | 1.126406 | \n",
"| 24 | 0.00121800 | 8.258347e-04 | 2.048239 | 1.126381 | \n",
"| 25 | 0.00095550 | 5.973987e-04 | 2.048265 | 1.126388 | \n",
"| 26 | 0.00078915 | 4.240827e-04 | 2.048268 | 1.126395 | \n",
"| 27 | 0.00068490 | 2.920000e-04 | 2.048243 | 1.126376 | \n",
"| 28 | 0.00060925 | 1.980209e-04 | 2.048201 | 1.126373 | \n",
"| 29 | 0.00057450 | 1.480708e-04 | 2.048216 | 1.126385 | \n",
"| 30 | 0.00054005 | 1.109723e-04 | 2.048205 | 1.126383 | \n",
"| 31 | 0.00052640 | 8.095950e-05 | 2.048169 | 1.126380 | \n",
"| 32 | 0.00051395 | 5.771263e-05 | 2.048139 | 1.126339 | \n",
"| 33 | 0.00051360 | 5.732748e-05 | 2.048136 | 1.126337 | \n",
"| 34 | 0.00051345 | 5.719657e-05 | 2.048135 | 1.126336 | \n",
"| 35 | 0.00051350 | 5.721145e-05 | 2.048134 | 1.126336 | \n",
"| 36 | 0.00051345 | 5.721755e-05 | 2.048134 | 1.126336 | \n",
"| 37 | 0.00051345 | 5.720007e-05 | 2.048134 | 1.126336 | \n",
"| 38 | 0.00051350 | 5.721058e-05 | 2.048134 | 1.126335 | \n",
"| 39 | 0.00051350 | 5.721058e-05 | 2.048134 | 1.126335 | \n",
"| 40 | 0.00051350 | 5.721058e-05 | 2.048134 | 1.126335 | \n",
"\n",
"\n"
],
"text/plain": [
" iter train_rmse_mean train_rmse_std test_rmse_mean test_rmse_std\n",
"1 20 0.00386060 2.537191e-03 2.048255 1.126467 \n",
"2 21 0.00283360 1.943153e-03 2.048228 1.126368 \n",
"3 22 0.00210150 1.477531e-03 2.048224 1.126370 \n",
"4 23 0.00158265 1.115964e-03 2.048224 1.126406 \n",
"5 24 0.00121800 8.258347e-04 2.048239 1.126381 \n",
"6 25 0.00095550 5.973987e-04 2.048265 1.126388 \n",
"7 26 0.00078915 4.240827e-04 2.048268 1.126395 \n",
"8 27 0.00068490 2.920000e-04 2.048243 1.126376 \n",
"9 28 0.00060925 1.980209e-04 2.048201 1.126373 \n",
"10 29 0.00057450 1.480708e-04 2.048216 1.126385 \n",
"11 30 0.00054005 1.109723e-04 2.048205 1.126383 \n",
"12 31 0.00052640 8.095950e-05 2.048169 1.126380 \n",
"13 32 0.00051395 5.771263e-05 2.048139 1.126339 \n",
"14 33 0.00051360 5.732748e-05 2.048136 1.126337 \n",
"15 34 0.00051345 5.719657e-05 2.048135 1.126336 \n",
"16 35 0.00051350 5.721145e-05 2.048134 1.126336 \n",
"17 36 0.00051345 5.721755e-05 2.048134 1.126336 \n",
"18 37 0.00051345 5.720007e-05 2.048134 1.126336 \n",
"19 38 0.00051350 5.721058e-05 2.048134 1.126335 \n",
"20 39 0.00051350 5.721058e-05 2.048134 1.126335 \n",
"21 40 0.00051350 5.721058e-05 2.048134 1.126335 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"library(xgboost)\n",
"# seq(0, 1, length=15)\n",
"cv <- xgb.cv(data = x, label=y, nrounds = 40, nfold = 20, \n",
" metrics = list(\"rmse\"),\n",
" max_depth = 10, \n",
" eta = 0.5, \n",
" objective = \"reg:linear\",\n",
" verbose = F)\n",
"# print(cv)\n",
"names(cv)\n",
"cv$evaluation_log[20:40]"
]
},
{
"cell_type": "code",
"execution_count": 221,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1]\ttrain-rmse:6.126499 \n",
"[40]\ttrain-rmse:0.000522 \n"
]
},
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>'q4E'</li>\n",
"\t<li>'q10C'</li>\n",
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"\t<li>'q10E'</li>\n",
"\t<li>'q18D'</li>\n",
"\t<li>'q8C'</li>\n",
"\t<li>'q21C'</li>\n",
"\t<li>'q13B'</li>\n",
"\t<li>'q14B'</li>\n",
"\t<li>'q11C'</li>\n",
"\t<li>'q4D'</li>\n",
"\t<li>'q6D'</li>\n",
"\t<li>'q9C'</li>\n",
"\t<li>'q4C'</li>\n",
"\t<li>'q3E'</li>\n",
"\t<li>'q17B'</li>\n",
"\t<li>'q1B'</li>\n",
"\t<li>'q1E'</li>\n",
"\t<li>'q5C'</li>\n",
"\t<li>'q3C'</li>\n",
"\t<li>'q1D'</li>\n",
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"\\begin{enumerate*}\n",
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"\\item 'q1D'\n",
"\\item 'q5B'\n",
"\\item 'q19E'\n",
"\\item 'q19C'\n",
"\\item 'q20B'\n",
"\\end{enumerate*}\n"
],
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"1. 'q4E'\n",
"2. 'q10C'\n",
"3. 'q22E'\n",
"4. 'q10E'\n",
"5. 'q18D'\n",
"6. 'q8C'\n",
"7. 'q21C'\n",
"8. 'q13B'\n",
"9. 'q14B'\n",
"10. 'q11C'\n",
"11. 'q4D'\n",
"12. 'q6D'\n",
"13. 'q9C'\n",
"14. 'q4C'\n",
"15. 'q3E'\n",
"16. 'q17B'\n",
"17. 'q1B'\n",
"18. 'q1E'\n",
"19. 'q5C'\n",
"20. 'q3C'\n",
"21. 'q1D'\n",
"22. 'q5B'\n",
"23. 'q19E'\n",
"24. 'q19C'\n",
"25. 'q20B'\n",
"\n",
"\n"
],
"text/plain": [
" [1] \"q4E\" \"q10C\" \"q22E\" \"q10E\" \"q18D\" \"q8C\" \"q21C\" \"q13B\" \"q14B\" \"q11C\"\n",
"[11] \"q4D\" \"q6D\" \"q9C\" \"q4C\" \"q3E\" \"q17B\" \"q1B\" \"q1E\" \"q5C\" \"q3C\" \n",
"[21] \"q1D\" \"q5B\" \"q19E\" \"q19C\" \"q20B\""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xgb <- xgboost(data = x, \n",
" label = y,\n",
" eta = 0.5,\n",
" max_depth = 10, \n",
" nround=40, \n",
" eval_metric = \"rmse\",\n",
" objective = \"reg:linear\",\n",
" print_every_n = 500,\n",
")\n",
"coef <- xgb.importance(colnames(x), model = xgb)\n",
"zz <- (coef[1:25])\n",
"zz$Feature"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Cambio de valores nulos\n",
"Hasta ahora hemos ajustado el modelo ignorando las observaciones que tenían alguna respuesta con valor nulo. Sin embargo, estos valores pueden ser de utilidad en nuestro modelo.\n",
"\n",
"### Sustituir nulos por su valor esperado\n",
"Podemos sustituir los valores nulos por el valor esperado de respuesta. Considerando que el patrón de respuesta correcto corresponde a las letras más respondidas en cada pregunta. Dado que asumimos que el alumno esta respondiendo correctamente por cada NA que ha olvidado, el número total de respuestas acertadas variará en función de los valores nulos."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"d.noNA <- d\n",
"\n",
"# Get the number of NAs by row and add them to the total\n",
"d.noNA$total <- d.noNA$total + rowSums(is.na(d.noNA))\n",
"\n",
"# Change the NA values for its expected answer according to solmod\n",
"for (i in 1:(ncol(d.noNA)-1)){\n",
" d.noNA[,i][is.na(d.noNA[,i])] <- solmod[i]\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"#### Variables dummy\n",
"\n",
"De nuevo creamos las variables dummy necesarias con el nuevo dataframe."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1] \"Columns with all their elements = 0 : \"\n",
"[1] \"dummy cols: 85 rows: 23\"\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>q1A</th><th scope=col>q1B</th><th scope=col>q1C</th><th scope=col>q1D</th><th scope=col>q1E</th><th scope=col>q2B</th><th scope=col>q3B</th><th scope=col>q3C</th><th scope=col>q3E</th><th scope=col>q4B</th><th scope=col>q4C</th><th scope=col>q4D</th><th scope=col>q4E</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
"\t<tr><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>\n",
"\t<tr><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
"\t<tr><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
"\t<tr><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
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"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllllllllllll}\n",
" q1A & q1B & q1C & q1D & q1E & q2B & q3B & q3C & q3E & q4B & q4C & q4D & q4E\\\\\n",
"\\hline\n",
"\t 0 & 1 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 0\\\\\n",
"\t 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 1 & 0 & 0\\\\\n",
"\t 0 & 1 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 0\\\\\n",
"\t 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0\\\\\n",
"\t 0 & 0 & 1 & 0 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 0 & 0\\\\\n",
"\t 0 & 1 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 0\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"q1A | q1B | q1C | q1D | q1E | q2B | q3B | q3C | q3E | q4B | q4C | q4D | q4E | \n",
"|---|---|---|---|---|---|\n",
"| 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | \n",
"| 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | \n",
"| 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | \n",
"| 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | \n",
"| 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | \n",
"| 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | \n",
"\n",
"\n"
],
"text/plain": [
" q1A q1B q1C q1D q1E q2B q3B q3C q3E q4B q4C q4D q4E\n",
"1 0 1 0 0 0 1 0 1 0 0 0 1 0 \n",
"2 0 0 1 0 0 1 0 0 1 0 1 0 0 \n",
"3 0 1 0 0 0 1 0 1 0 0 0 1 0 \n",
"4 0 1 0 0 0 1 0 0 1 0 0 0 0 \n",
"5 0 0 1 0 0 1 0 1 0 1 0 0 0 \n",
"6 0 1 0 0 0 1 0 1 0 0 0 1 0 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create dummy vars for each answer\n",
"options(na.action='na.pass')\n",
"d_dummy <- as.data.frame(model.matrix( ~ . - 1, data=d.noNA))\n",
"data <- d_dummy[complete.cases(d_dummy),]\n",
"\n",
"# Visualize the dimensions of the data\n",
"print (paste0(\"Columns with all their elements = 0 : \",\n",
" names(data[, colSums(data!=0)<=0])))\n",
"\n",
"# Delete columns with all the answers = 0\n",
"data <- data[, colSums(data != 0) > 0]\n",
"\n",
"# Read the dimensions of the dummy var with no NAs\n",
"n_rows <- dim(data)[1]\n",
"n_cols <- dim(data)[2]\n",
"print(paste0(\"dummy cols: \", n_cols, \" rows: \", n_rows))\n",
"\n",
"# Create the input matrix and the response vector\n",
"x <- as.matrix(data[1:n_rows, 1:n_cols-1])\n",
"y <- data[1:n_rows, n_cols]\n",
"\n",
"# Show the dummy vars, making the table smaller for convenience\n",
"head(data[,1:13])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Debido a los cambios, vemos como ningúna columna tiene todos sus elementos igual a 0. Ahora tenemos un total de 85 columnas y 23 filas correspondientes al total de alumnos.\n",
"\n",
"#### Entrenar el nuevo modelo\n",
"Con los nuevos datos obtenidos habrá que entrenar un modelo siguiendo los pasos explicados anteriormente."
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, :\n",
"\"There were missing values in resampled performance measures.\""
]
},
{
"data": {},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1] 0.842836\n"
]
},
{
"data": {
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ItdaUyqX7h/OCLFgH+z/94nVYBXSZSfOCLFgP+JVGFIclC4ezgixYD/smuY5KJw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\nkNYN7f4qAAAAAElFTkSuQmCC",
"text/plain": [
"plot without title"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Especify the parameters to do cross-validation\n",
"trainctr<-trainControl(method=\"cv\", number=n_rows)\n",
"\n",
"# Start the CV process\n",
"larsTune<-train(x, y, method=\"lars\",\n",
" tuneGrid=data.frame(.fraction=seq(0.00001, 0.99999, length=1000)),\n",
" trControl=trainctr, normalize=FALSE, intercept=FALSE,\n",
" type=\"lasso\")\n",
"\n",
"# Plot a graph with the obtained results\n",
"plot(larsTune)\n",
"\n",
"# Find the best fraction\n",
"bestfraction = larsTune$bestTune$fraction\n",
"print (bestfraction)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>Df</th><th scope=col>Rss</th><th scope=col>Cp</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>31</th><td>21 </td><td>9.774118e-01</td><td>NaN </td></tr>\n",
"\t<tr><th scope=row>32</th><td>22 </td><td>2.832410e-01</td><td>NaN </td></tr>\n",
"\t<tr><th scope=row>33</th><td>23 </td><td>2.267655e-02</td><td>NaN </td></tr>\n",
"\t<tr><th scope=row>34</th><td>22 </td><td>1.493227e-02</td><td>NaN </td></tr>\n",
"\t<tr><th scope=row>35</th><td>23 </td><td>3.851946e-03</td><td>NaN </td></tr>\n",
"\t<tr><th scope=row>36</th><td>22 </td><td>2.718915e-03</td><td>NaN </td></tr>\n",
"\t<tr><th scope=row>37</th><td>23 </td><td>1.569044e-27</td><td>NaN </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lll}\n",
" & Df & Rss & Cp\\\\\n",
"\\hline\n",
"\t31 & 21 & 9.774118e-01 & NaN \\\\\n",
"\t32 & 22 & 2.832410e-01 & NaN \\\\\n",
"\t33 & 23 & 2.267655e-02 & NaN \\\\\n",
"\t34 & 22 & 1.493227e-02 & NaN \\\\\n",
"\t35 & 23 & 3.851946e-03 & NaN \\\\\n",
"\t36 & 22 & 2.718915e-03 & NaN \\\\\n",
"\t37 & 23 & 1.569044e-27 & NaN \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| <!--/--> | Df | Rss | Cp | \n",
"|---|---|---|---|---|---|---|\n",
"| 31 | 21 | 9.774118e-01 | NaN | \n",
"| 32 | 22 | 2.832410e-01 | NaN | \n",
"| 33 | 23 | 2.267655e-02 | NaN | \n",
"| 34 | 22 | 1.493227e-02 | NaN | \n",
"| 35 | 23 | 3.851946e-03 | NaN | \n",
"| 36 | 22 | 2.718915e-03 | NaN | \n",
"| 37 | 23 | 1.569044e-27 | NaN | \n",
"\n",
"\n"
],
"text/plain": [
" Df Rss Cp \n",
"31 21 9.774118e-01 NaN\n",
"32 22 2.832410e-01 NaN\n",
"33 23 2.267655e-02 NaN\n",
"34 22 1.493227e-02 NaN\n",
"35 23 3.851946e-03 NaN\n",
"36 22 2.718915e-03 NaN\n",
"37 23 1.569044e-27 NaN"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Fit the model and show its last iterations\n",
"lars.model<-lars(x, y,\n",
" type=\"lasso\",\n",
" normalize=F, \n",
" intercept=F, \n",
" max.steps=300)\n",
"tail(summary(lars.model), 7)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"'Penalizacion: 0.864864864864865'"
],
"text/latex": [
"'Penalizacion: 0.864864864864865'"
],
"text/markdown": [
"'Penalizacion: 0.864864864864865'"
],
"text/plain": [
"[1] \"Penalizacion: 0.864864864864865\""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<dl class=dl-horizontal>\n",
"\t<dt>q23B</dt>\n",
"\t\t<dd>2.17168912768249</dd>\n",
"\t<dt>q23E</dt>\n",
"\t\t<dd>1.78097245706881</dd>\n",
"</dl>\n"
],
"text/latex": [
"\\begin{description*}\n",
"\\item[q23B] 2.17168912768249\n",
"\\item[q23E] 1.78097245706881\n",
"\\end{description*}\n"
],
"text/markdown": [
"q23B\n",
": 2.17168912768249q23E\n",
": 1.78097245706881\n",
"\n"
],
"text/plain": [
" q23B q23E \n",
"2.171689 1.780972 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>'q1C'</li>\n",
"\t<li>'q2B'</li>\n",
"\t<li>'q3C'</li>\n",
"\t<li>'q4D'</li>\n",
"\t<li>'q7E'</li>\n",
"\t<li>'q8C'</li>\n",
"\t<li>'q9D'</li>\n",
"\t<li>'q10E'</li>\n",
"\t<li>'q13B'</li>\n",
"\t<li>'q14B'</li>\n",
"\t<li>'q15B'</li>\n",
"\t<li>'q17D'</li>\n",
"\t<li>'q18C'</li>\n",
"\t<li>'q20B'</li>\n",
"\t<li>'q21C'</li>\n",
"\t<li>'q22E'</li>\n",
"\t<li>'q23B'</li>\n",
"\t<li>'q24C'</li>\n",
"\t<li>'q25B'</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 'q1C'\n",
"\\item 'q2B'\n",
"\\item 'q3C'\n",
"\\item 'q4D'\n",
"\\item 'q7E'\n",
"\\item 'q8C'\n",
"\\item 'q9D'\n",
"\\item 'q10E'\n",
"\\item 'q13B'\n",
"\\item 'q14B'\n",
"\\item 'q15B'\n",
"\\item 'q17D'\n",
"\\item 'q18C'\n",
"\\item 'q20B'\n",
"\\item 'q21C'\n",
"\\item 'q22E'\n",
"\\item 'q23B'\n",
"\\item 'q24C'\n",
"\\item 'q25B'\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 'q1C'\n",
"2. 'q2B'\n",
"3. 'q3C'\n",
"4. 'q4D'\n",
"5. 'q7E'\n",
"6. 'q8C'\n",
"7. 'q9D'\n",
"8. 'q10E'\n",
"9. 'q13B'\n",
"10. 'q14B'\n",
"11. 'q15B'\n",
"12. 'q17D'\n",
"13. 'q18C'\n",
"14. 'q20B'\n",
"15. 'q21C'\n",
"16. 'q22E'\n",
"17. 'q23B'\n",
"18. 'q24C'\n",
"19. 'q25B'\n",
"\n",
"\n"
],
"text/plain": [
" [1] \"q1C\" \"q2B\" \"q3C\" \"q4D\" \"q7E\" \"q8C\" \"q9D\" \"q10E\" \"q13B\" \"q14B\"\n",
"[11] \"q15B\" \"q17D\" \"q18C\" \"q20B\" \"q21C\" \"q22E\" \"q23B\" \"q24C\" \"q25B\""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Obtain the coefficients of the fitted model together with its fraction value.\n",
"val <- predict(lars.model, x, type=\"coefficients\")\n",
"fraction <- val$fraction[33]\n",
"coef.na.correct <- val$coefficients[33,]\n",
"\n",
"paste0(\"Penalizacion: \", fraction)\n",
"# Find duplicate answer; get the one with the best coef.\n",
"coef.na.correct[coef.na.correct > 0][c(17, 18)]\n",
"coef.na.correct[coef.na.correct > 0][18] <- 0\n",
"answer.na.correct <- names(coef.na.correct)[coef.na.correct > 0]\n",
"answer.na.correct"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" q1 q2 q3 q4 q5 q6 q7 q8 q9 q10 q11 q12 q13 q14 q15 q16 q17 q18 q19 q20 \n",
"\"C\" \"B\" \"C\" \"D\" \"A\" \"D\" \"E\" \"C\" \"D\" \"E\" \"A\" \"A\" \"B\" \"B\" \"B\" \"A\" \"D\" \"C\" \"C\" \"B\" \n",
"q21 q22 q23 q24 q25 \n",
"\"C\" \"E\" \"B\" \"C\" \"B\" \n"
]
},
{
"data": {
"text/html": [
"'Diferencia de resultado por estudiante:'"
],
"text/latex": [
"'Diferencia de resultado por estudiante:'"
],
"text/markdown": [
"'Diferencia de resultado por estudiante:'"
],
"text/plain": [
"[1] \"Diferencia de resultado por estudiante:\""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>1</li>\n",
"\t<li>-1</li>\n",
"\t<li>-1</li>\n",
"\t<li>-1</li>\n",
"\t<li>-1</li>\n",
"\t<li>-1</li>\n",
"\t<li>-1</li>\n",
"\t<li>1</li>\n",
"\t<li>-3</li>\n",
"\t<li>-1</li>\n",
"\t<li>1</li>\n",
"\t<li>0</li>\n",
"\t<li>-1</li>\n",
"\t<li>-1</li>\n",
"\t<li>-2</li>\n",
"\t<li>1</li>\n",
"\t<li>0</li>\n",
"\t<li>0</li>\n",
"\t<li>-2</li>\n",
"\t<li>1</li>\n",
"\t<li>0</li>\n",
"\t<li>0</li>\n",
"\t<li>-3</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 1\n",
"\\item -1\n",
"\\item -1\n",
"\\item -1\n",
"\\item -1\n",
"\\item -1\n",
"\\item -1\n",
"\\item 1\n",
"\\item -3\n",
"\\item -1\n",
"\\item 1\n",
"\\item 0\n",
"\\item -1\n",
"\\item -1\n",
"\\item -2\n",
"\\item 1\n",
"\\item 0\n",
"\\item 0\n",
"\\item -2\n",
"\\item 1\n",
"\\item 0\n",
"\\item 0\n",
"\\item -3\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 1\n",
"2. -1\n",
"3. -1\n",
"4. -1\n",
"5. -1\n",
"6. -1\n",
"7. -1\n",
"8. 1\n",
"9. -3\n",
"10. -1\n",
"11. 1\n",
"12. 0\n",
"13. -1\n",
"14. -1\n",
"15. -2\n",
"16. 1\n",
"17. 0\n",
"18. 0\n",
"19. -2\n",
"20. 1\n",
"21. 0\n",
"22. 0\n",
"23. -3\n",
"\n",
"\n"
],
"text/plain": [
" [1] 1 -1 -1 -1 -1 -1 -1 1 -3 -1 1 0 -1 -1 -2 1 0 0 -2 1 0 0 -3"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"'Diferencia total:'"
],
"text/latex": [
"'Diferencia total:'"
],
"text/markdown": [
"'Diferencia total:'"
],
"text/plain": [
"[1] \"Diferencia total:\""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"24"
],
"text/latex": [
"24"
],
"text/markdown": [
"24"
],
"text/plain": [
"[1] 24"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"na.correct <- get.exam.answers(answer.na.correct,\n",
" solmod)\n",
"print(na.correct)\n",
"\n",
"scores_guess = get_scores(correct_answers = na.correct)\n",
"\"Diferencia de resultado por estudiante:\"\n",
"scores_real - scores_guess\n",
"\"Diferencia total:\"\n",
"sum(abs(scores_real - scores_guess))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Sustituir nulos por 0 en variable dummy\n",
"Otra posible propuesta para hacer uso de los valores nulos sería el asumir que los NAs equivalen a una no respuesta en la variable dummy (a un 0). Lo que mantendría el total como al principio."
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1] \"Columns with all their elements = 0 : \"\n",
"[1] \"dummy cols: 85 rows: 23\"\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>q1A</th><th scope=col>q1B</th><th scope=col>q1C</th><th scope=col>q1D</th><th scope=col>q1E</th><th scope=col>q2B</th><th scope=col>q3B</th><th scope=col>q3C</th><th scope=col>q3E</th><th scope=col>q4B</th><th scope=col>q4C</th><th scope=col>q4D</th><th scope=col>q4E</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
"\t<tr><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>\n",
"\t<tr><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
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"\t<tr><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
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"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllllllllllll}\n",
" q1A & q1B & q1C & q1D & q1E & q2B & q3B & q3C & q3E & q4B & q4C & q4D & q4E\\\\\n",
"\\hline\n",
"\t 0 & 1 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 0\\\\\n",
"\t 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 1 & 0 & 0\\\\\n",
"\t 0 & 1 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 0\\\\\n",
"\t 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0\\\\\n",
"\t 0 & 0 & 1 & 0 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 0 & 0\\\\\n",
"\t 0 & 1 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 0\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"q1A | q1B | q1C | q1D | q1E | q2B | q3B | q3C | q3E | q4B | q4C | q4D | q4E | \n",
"|---|---|---|---|---|---|\n",
"| 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | \n",
"| 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | \n",
"| 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | \n",
"| 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | \n",
"| 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | \n",
"| 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | \n",
"\n",
"\n"
],
"text/plain": [
" q1A q1B q1C q1D q1E q2B q3B q3C q3E q4B q4C q4D q4E\n",
"1 0 1 0 0 0 1 0 1 0 0 0 1 0 \n",
"2 0 0 1 0 0 1 0 0 1 0 1 0 0 \n",
"3 0 1 0 0 0 1 0 1 0 0 0 1 0 \n",
"4 0 1 0 0 0 1 0 0 1 0 0 0 0 \n",
"5 0 0 1 0 0 1 0 1 0 1 0 0 0 \n",
"6 0 1 0 0 0 1 0 1 0 0 0 1 0 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create dummy vars for each answer\n",
"options(na.action='na.pass')\n",
"d_dummy <- as.data.frame(model.matrix( ~ . - 1, data=d))\n",
"# Change NA values to 0\n",
"d_dummy[is.na(d_dummy)] <- 0\n",
"data <- d_dummy[complete.cases(d_dummy),]\n",
"\n",
"# Visualize the dimensions of the data\n",
"print (paste0(\"Columns with all their elements = 0 : \",\n",
" names(data[, colSums(data!=0)<=0])))\n",
"\n",
"# Delete columns with all the answers = 0\n",
"data <- data[, colSums(data != 0) > 0]\n",
"\n",
"# Read the dimensions of the dummy var with no NAs\n",
"n_rows <- dim(data)[1]\n",
"n_cols <- dim(data)[2]\n",
"print(paste0(\"dummy cols: \", n_cols, \" rows: \", n_rows))\n",
"\n",
"# Create the input matrix and the response vector\n",
"x <- as.matrix(data[1:n_rows, 1:n_cols-1])\n",
"y <- data[1:n_rows, n_cols]\n",
"\n",
"# Show the dummy vars, making the table smaller for convenience\n",
"head(data[,1:13])"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, :\n",
"\"There were missing values in resampled performance measures.\""
]
},
{
"data": {},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1] 0.9149066\n"
]
},
{
"data": {
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UckDfgk9uhJH7nOvN66wZQHNiMqrzgiacCnsX+G/Oagj15hXm/ebNIjmxyVVxyR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\nEImQAkEkQgoEkQgpkP8CTZ7Yn4ekSAAAAABJRU5ErkJggg==",
"text/plain": [
"plot without title"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Especify the parameters to do cross-validation\n",
"trainctr<-trainControl(method=\"cv\", number=n_rows)\n",
"\n",
"# Start the CV process\n",
"larsTune<-train(x, y, method=\"lars\",\n",
" tuneGrid=data.frame(.fraction=seq(0.00001, 0.99999, length=1000)),\n",
" trControl=trainctr, normalize=FALSE, intercept=FALSE,\n",
" type=\"lasso\")\n",
"\n",
"# Plot a graph with the obtained results\n",
"plot(larsTune)\n",
"\n",
"# Find the best fraction\n",
"bestfraction = larsTune$bestTune$fraction\n",
"print (bestfraction)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>Df</th><th scope=col>Rss</th><th scope=col>Cp</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>23</th><td>19 </td><td>1.397585e+00</td><td>NaN </td></tr>\n",
"\t<tr><th scope=row>24</th><td>20 </td><td>1.188700e+00</td><td>NaN </td></tr>\n",
"\t<tr><th scope=row>25</th><td>21 </td><td>1.763949e-01</td><td>NaN </td></tr>\n",
"\t<tr><th scope=row>26</th><td>22 </td><td>2.565588e-02</td><td>NaN </td></tr>\n",
"\t<tr><th scope=row>27</th><td>21 </td><td>2.124146e-02</td><td>NaN </td></tr>\n",
"\t<tr><th scope=row>28</th><td>22 </td><td>7.217103e-03</td><td>NaN </td></tr>\n",
"\t<tr><th scope=row>29</th><td>23 </td><td>2.210388e-27</td><td>NaN </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lll}\n",
" & Df & Rss & Cp\\\\\n",
"\\hline\n",
"\t23 & 19 & 1.397585e+00 & NaN \\\\\n",
"\t24 & 20 & 1.188700e+00 & NaN \\\\\n",
"\t25 & 21 & 1.763949e-01 & NaN \\\\\n",
"\t26 & 22 & 2.565588e-02 & NaN \\\\\n",
"\t27 & 21 & 2.124146e-02 & NaN \\\\\n",
"\t28 & 22 & 7.217103e-03 & NaN \\\\\n",
"\t29 & 23 & 2.210388e-27 & NaN \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| <!--/--> | Df | Rss | Cp | \n",
"|---|---|---|---|---|---|---|\n",
"| 23 | 19 | 1.397585e+00 | NaN | \n",
"| 24 | 20 | 1.188700e+00 | NaN | \n",
"| 25 | 21 | 1.763949e-01 | NaN | \n",
"| 26 | 22 | 2.565588e-02 | NaN | \n",
"| 27 | 21 | 2.124146e-02 | NaN | \n",
"| 28 | 22 | 7.217103e-03 | NaN | \n",
"| 29 | 23 | 2.210388e-27 | NaN | \n",
"\n",
"\n"
],
"text/plain": [
" Df Rss Cp \n",
"23 19 1.397585e+00 NaN\n",
"24 20 1.188700e+00 NaN\n",
"25 21 1.763949e-01 NaN\n",
"26 22 2.565588e-02 NaN\n",
"27 21 2.124146e-02 NaN\n",
"28 22 7.217103e-03 NaN\n",
"29 23 2.210388e-27 NaN"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Fit the model and show its last iterations\n",
"lars.model<-lars(x, y,\n",
" type=\"lasso\",\n",
" normalize=F, \n",
" intercept=F, \n",
" max.steps=300)\n",
"tail(summary(lars.model), 7)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"'Penalizacion: 0.96551724137931'"
],
"text/latex": [
"'Penalizacion: 0.96551724137931'"
],
"text/markdown": [
"'Penalizacion: 0.96551724137931'"
],
"text/plain": [
"[1] \"Penalizacion: 0.96551724137931\""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<dl class=dl-horizontal>\n",
"\t<dt>q23B</dt>\n",
"\t\t<dd>1.3164236272091</dd>\n",
"\t<dt>q23E</dt>\n",
"\t\t<dd>1.95764120665057</dd>\n",
"</dl>\n"
],
"text/latex": [
"\\begin{description*}\n",
"\\item[q23B] 1.3164236272091\n",
"\\item[q23E] 1.95764120665057\n",
"\\end{description*}\n"
],
"text/markdown": [
"q23B\n",
": 1.3164236272091q23E\n",
": 1.95764120665057\n",
"\n"
],
"text/plain": [
" q23B q23E \n",
"1.316424 1.957641 "
]
},
"metadata": {},
"output_type": "display_data"
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{
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"<ol class=list-inline>\n",
"\t<li>'q1C'</li>\n",
"\t<li>'q2B'</li>\n",
"\t<li>'q3C'</li>\n",
"\t<li>'q4D'</li>\n",
"\t<li>'q6D'</li>\n",
"\t<li>'q7E'</li>\n",
"\t<li>'q8C'</li>\n",
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"\\begin{enumerate*}\n",
"\\item 'q1C'\n",
"\\item 'q2B'\n",
"\\item 'q3C'\n",
"\\item 'q4D'\n",
"\\item 'q6D'\n",
"\\item 'q7E'\n",
"\\item 'q8C'\n",
"\\item 'q9D'\n",
"\\item 'q10E'\n",
"\\item 'q13B'\n",
"\\item 'q14B'\n",
"\\item 'q15B'\n",
"\\item 'q18E'\n",
"\\item 'q20B'\n",
"\\item 'q21C'\n",
"\\item 'q22E'\n",
"\\item 'q23E'\n",
"\\item 'q24C'\n",
"\\item 'q25C'\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 'q1C'\n",
"2. 'q2B'\n",
"3. 'q3C'\n",
"4. 'q4D'\n",
"5. 'q6D'\n",
"6. 'q7E'\n",
"7. 'q8C'\n",
"8. 'q9D'\n",
"9. 'q10E'\n",
"10. 'q13B'\n",
"11. 'q14B'\n",
"12. 'q15B'\n",
"13. 'q18E'\n",
"14. 'q20B'\n",
"15. 'q21C'\n",
"16. 'q22E'\n",
"17. 'q23E'\n",
"18. 'q24C'\n",
"19. 'q25C'\n",
"\n",
"\n"
],
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" [1] \"q1C\" \"q2B\" \"q3C\" \"q4D\" \"q6D\" \"q7E\" \"q8C\" \"q9D\" \"q10E\" \"q13B\"\n",
"[11] \"q14B\" \"q15B\" \"q18E\" \"q20B\" \"q21C\" \"q22E\" \"q23E\" \"q24C\" \"q25C\""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Obtain the coefficients of the fitted model together with its fraction value.\n",
"val <- predict(lars.model, x, type=\"coefficients\")\n",
"fraction <- val$fraction[29]\n",
"coef.na.noanswer <- val$coefficients[29,]\n",
"\n",
"# fraction\n",
"# answers.na.noanswer\n",
"# length(answers.na.noanswer)\n",
"# coef.na.noanswer\n",
"\n",
"paste0(\"Penalizacion: \", fraction)\n",
"# Find duplicate answer; get the one with the best coef.\n",
"coef.na.noanswer[coef.na.noanswer > 0][c(17, 18)]\n",
"coef.na.noanswer[coef.na.noanswer > 0][17] <- 0\n",
"answer.na.noanswer <- names(coef.na.noanswer)[coef.na.noanswer > 0]\n",
"answer.na.noanswer\n"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" q1 q2 q3 q4 q5 q6 q7 q8 q9 q10 q11 q12 q13 q14 q15 q16 q17 q18 q19 q20 \n",
"\"C\" \"B\" \"C\" \"D\" \"A\" \"D\" \"E\" \"C\" \"D\" \"E\" \"A\" \"A\" \"B\" \"B\" \"B\" \"A\" \"D\" \"E\" \"C\" \"B\" \n",
"q21 q22 q23 q24 q25 \n",
"\"C\" \"E\" \"E\" \"C\" \"C\" \n"
]
},
{
"data": {
"text/html": [
"'Diferencia de resultado por estudiante:'"
],
"text/latex": [
"'Diferencia de resultado por estudiante:'"
],
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"'Diferencia de resultado por estudiante:'"
],
"text/plain": [
"[1] \"Diferencia de resultado por estudiante:\""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>1</li>\n",
"\t<li>-1</li>\n",
"\t<li>-1</li>\n",
"\t<li>-1</li>\n",
"\t<li>-2</li>\n",
"\t<li>1</li>\n",
"\t<li>-1</li>\n",
"\t<li>0</li>\n",
"\t<li>-1</li>\n",
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"\t<li>-1</li>\n",
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"text/latex": [
"\\begin{enumerate*}\n",
"\\item 1\n",
"\\item -1\n",
"\\item -1\n",
"\\item -1\n",
"\\item -2\n",
"\\item 1\n",
"\\item -1\n",
"\\item 0\n",
"\\item -1\n",
"\\item 0\n",
"\\item 1\n",
"\\item -1\n",
"\\item -2\n",
"\\item -2\n",
"\\item 0\n",
"\\item 1\n",
"\\item -1\n",
"\\item 1\n",
"\\item -2\n",
"\\item 0\n",
"\\item -1\n",
"\\item -2\n",
"\\item -2\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 1\n",
"2. -1\n",
"3. -1\n",
"4. -1\n",
"5. -2\n",
"6. 1\n",
"7. -1\n",
"8. 0\n",
"9. -1\n",
"10. 0\n",
"11. 1\n",
"12. -1\n",
"13. -2\n",
"14. -2\n",
"15. 0\n",
"16. 1\n",
"17. -1\n",
"18. 1\n",
"19. -2\n",
"20. 0\n",
"21. -1\n",
"22. -2\n",
"23. -2\n",
"\n",
"\n"
],
"text/plain": [
" [1] 1 -1 -1 -1 -2 1 -1 0 -1 0 1 -1 -2 -2 0 1 -1 1 -2 0 -1 -2 -2"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"'Diferencia total:'"
],
"text/latex": [
"'Diferencia total:'"
],
"text/markdown": [
"'Diferencia total:'"
],
"text/plain": [
"[1] \"Diferencia total:\""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"25"
],
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"25"
],
"text/markdown": [
"25"
],
"text/plain": [
"[1] 25"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"na.noanswer <- get.exam.answers(answer.na.noanswe,\n",
" solmod)\n",
"print(na.noanswer)\n",
"\n",
"scores_guess = get_scores(correct_answers = na.noanswer)\n",
"\"Diferencia de resultado por estudiante:\"\n",
"scores_real - scores_guess\n",
"\"Diferencia total:\"\n",
"sum(abs(scores_real - scores_guess))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [
{
"ename": "ERROR",
"evalue": "Error in coef > 0: comparison (6) is possible only for atomic and list types\n",
"output_type": "error",
"traceback": [
"Error in coef > 0: comparison (6) is possible only for atomic and list types\nTraceback:\n"
]
}
],
"source": [
"val<-coef.lars(lars.model,s=0.9149066,mode=\"lambda\")\n",
"length(names(val)[val > 0])\n",
"val"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Combinacion de las tres respuestas"
]
},
{
"cell_type": "code",
"execution_count": 294,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<ol class=list-inline>\n",
"\t<li>'q1C'</li>\n",
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"\\begin{enumerate*}\n",
"\\item 'q1C'\n",
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"\\item 'q7E'\n",
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"\\item 'q22E'\n",
"\\item 'q23B'\n",
"\\item 'q24C'\n",
"\\item 'q25B'\n",
"\\end{enumerate*}\n"
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"1. 'q1C'\n",
"2. 'q2B'\n",
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"4. 'q4D'\n",
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"6. 'q8C'\n",
"7. 'q9D'\n",
"8. 'q10E'\n",
"9. 'q13B'\n",
"10. 'q14B'\n",
"11. 'q15B'\n",
"12. 'q17D'\n",
"13. 'q19C'\n",
"14. 'q20B'\n",
"15. 'q21C'\n",
"16. 'q22E'\n",
"17. 'q23B'\n",
"18. 'q24C'\n",
"19. 'q25B'\n",
"\n",
"\n"
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" [1] \"q1C\" \"q2B\" \"q3C\" \"q4D\" \"q7E\" \"q8C\" \"q9D\" \"q10E\" \"q13B\" \"q14B\"\n",
"[11] \"q15B\" \"q17D\" \"q19C\" \"q20B\" \"q21C\" \"q22E\" \"q23B\" \"q24C\" \"q25B\""
]
},
"metadata": {},
"output_type": "display_data"
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],
"source": [
"answer.na.deleted"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>NA deleted</th><th scope=col>NA correct</th><th scope=col>NA noanswer</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>q1C </td><td>q1C </td><td>q1C </td></tr>\n",
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"\t<tr><td>q9D </td><td>q9D </td><td>q8C </td></tr>\n",
"\t<tr><td>q10E</td><td>q10E</td><td>q9D </td></tr>\n",
"\t<tr><td>q13B</td><td>q13B</td><td>q10E</td></tr>\n",
"\t<tr><td>q14B</td><td>q14B</td><td>q13B</td></tr>\n",
"\t<tr><td>q15B</td><td>q15B</td><td>q14B</td></tr>\n",
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"\t<tr><td>q20B</td><td>q20B</td><td>q20B</td></tr>\n",
"\t<tr><td>q21C</td><td>q21C</td><td>q21C</td></tr>\n",
"\t<tr><td>q22E</td><td>q22E</td><td>q22E</td></tr>\n",
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"\t<tr><td>q25B</td><td>q25B</td><td>q25C</td></tr>\n",
"</tbody>\n",
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"\\begin{tabular}{lll}\n",
" NA deleted & NA correct & NA noanswer\\\\\n",
"\\hline\n",
"\t q1C & q1C & q1C \\\\\n",
"\t q2B & q2B & q2B \\\\\n",
"\t q3C & q3C & q3C \\\\\n",
"\t q4D & q4D & q4D \\\\\n",
"\t q7E & q7E & q6D \\\\\n",
"\t q8C & q8C & q7E \\\\\n",
"\t q9D & q9D & q8C \\\\\n",
"\t q10E & q10E & q9D \\\\\n",
"\t q13B & q13B & q10E\\\\\n",
"\t q14B & q14B & q13B\\\\\n",
"\t q15B & q15B & q14B\\\\\n",
"\t q17D & q17D & q15B\\\\\n",
"\t q19C & q18C & q18E\\\\\n",
"\t q20B & q20B & q20B\\\\\n",
"\t q21C & q21C & q21C\\\\\n",
"\t q22E & q22E & q22E\\\\\n",
"\t q23B & q23B & q23E\\\\\n",
"\t q24C & q24C & q24C\\\\\n",
"\t q25B & q25B & q25C\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"NA deleted | NA correct | NA noanswer | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| q1C | q1C | q1C | \n",
"| q2B | q2B | q2B | \n",
"| q3C | q3C | q3C | \n",
"| q4D | q4D | q4D | \n",
"| q7E | q7E | q6D | \n",
"| q8C | q8C | q7E | \n",
"| q9D | q9D | q8C | \n",
"| q10E | q10E | q9D | \n",
"| q13B | q13B | q10E | \n",
"| q14B | q14B | q13B | \n",
"| q15B | q15B | q14B | \n",
"| q17D | q17D | q15B | \n",
"| q19C | q18C | q18E | \n",
"| q20B | q20B | q20B | \n",
"| q21C | q21C | q21C | \n",
"| q22E | q22E | q22E | \n",
"| q23B | q23B | q23E | \n",
"| q24C | q24C | q24C | \n",
"| q25B | q25B | q25C | \n",
"\n",
"\n"
],
"text/plain": [
" NA deleted NA correct NA noanswer\n",
" [1,] q1C q1C q1C \n",
" [2,] q2B q2B q2B \n",
" [3,] q3C q3C q3C \n",
" [4,] q4D q4D q4D \n",
" [5,] q7E q7E q6D \n",
" [6,] q8C q8C q7E \n",
" [7,] q9D q9D q8C \n",
" [8,] q10E q10E q9D \n",
" [9,] q13B q13B q10E \n",
"[10,] q14B q14B q13B \n",
"[11,] q15B q15B q14B \n",
"[12,] q17D q17D q15B \n",
"[13,] q19C q18C q18E \n",
"[14,] q20B q20B q20B \n",
"[15,] q21C q21C q21C \n",
"[16,] q22E q22E q22E \n",
"[17,] q23B q23B q23E \n",
"[18,] q24C q24C q24C \n",
"[19,] q25B q25B q25C "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"<dl class=dl-horizontal>\n",
"\t<dt>q15B</dt>\n",
"\t\t<dd>0.405000252976477</dd>\n",
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"\t\t<dd>0.650966134792978</dd>\n",
"\t<dt>q18C</dt>\n",
"\t\t<dd>0.883282362550575</dd>\n",
"\t<dt>q20B</dt>\n",
"\t\t<dd>0.852396321261187</dd>\n",
"</dl>\n"
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"\\begin{description*}\n",
"\\item[q15B] 0.405000252976477\n",
"\\item[q17D] 0.650966134792978\n",
"\\item[q18C] 0.883282362550575\n",
"\\item[q20B] 0.852396321261187\n",
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"\t\t<dd>0.792679359533573</dd>\n",
"\t<dt>q18E</dt>\n",
"\t\t<dd>0.258439612400519</dd>\n",
"\t<dt>q20B</dt>\n",
"\t\t<dd>0.881126596533996</dd>\n",
"</dl>\n"
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"\\begin{description*}\n",
"\\item[q14B] 0.317515831930991\n",
"\\item[q15B] 0.792679359533573\n",
"\\item[q18E] 0.258439612400519\n",
"\\item[q20B] 0.881126596533996\n",
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"q14B\n",
": 0.317515831930991q15B\n",
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": 0.258439612400519q20B\n",
": 0.881126596533996\n",
"\n"
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" q14B q15B q18E q20B \n",
"0.3175158 0.7926794 0.2584396 0.8811266 "
]
},
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},
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"output_type": "stream",
"text": [
" q1 q2 q3 q4 q5 q6 q7 q8 q9 q10 q11 q12 q13 q14 q15 q16 q17 q18 q19 q20 \n",
"\"C\" \"B\" \"C\" \"D\" \"A\" \"D\" \"E\" \"C\" \"D\" \"E\" \"A\" \"A\" \"B\" \"B\" \"B\" \"A\" \"D\" \"E\" \"C\" \"B\" \n",
"q21 q22 q23 q24 q25 \n",
"\"C\" \"E\" \"E\" \"C\" \"C\" \n"
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"'Diferencia de resultado por estudiante:'"
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"[1] \"Diferencia de resultado por estudiante:\""
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"<ol class=list-inline>\n",
"\t<li>1</li>\n",
"\t<li>-1</li>\n",
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"\t<li>-1</li>\n",
"\t<li>-2</li>\n",
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"\\item -2\n",
"\\item -2\n",
"\\item 0\n",
"\\item 1\n",
"\\item -1\n",
"\\item 1\n",
"\\item -2\n",
"\\item 0\n",
"\\item -1\n",
"\\item -2\n",
"\\item -2\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 1\n",
"2. -1\n",
"3. -1\n",
"4. -1\n",
"5. -2\n",
"6. 1\n",
"7. -1\n",
"8. 0\n",
"9. -1\n",
"10. 0\n",
"11. 1\n",
"12. -1\n",
"13. -2\n",
"14. -2\n",
"15. 0\n",
"16. 1\n",
"17. -1\n",
"18. 1\n",
"19. -2\n",
"20. 0\n",
"21. -1\n",
"22. -2\n",
"23. -2\n",
"\n",
"\n"
],
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" [1] 1 -1 -1 -1 -2 1 -1 0 -1 0 1 -1 -2 -2 0 1 -1 1 -2 0 -1 -2 -2"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"'Diferencia total:'"
],
"text/latex": [
"'Diferencia total:'"
],
"text/markdown": [
"'Diferencia total:'"
],
"text/plain": [
"[1] \"Diferencia total:\""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"25"
],
"text/latex": [
"25"
],
"text/markdown": [
"25"
],
"text/plain": [
"[1] 25"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tab <- (cbind(answer.na.deleted, answer.na.correct, answer.na.noanswer))\n",
"colnames(tab) <- c(\"NA deleted\", \"NA correct\", \"NA noanswer\")\n",
"tab\n",
"coef.na.correct[coef.na.correct>0][11:14]\n",
"coef.na.noanswer[coef.na.noanswer>0][11:14]\n",
"\n",
"new_sol <- c(answer.na.deleted[1:4],\n",
" answer.na.noanswer[5],\n",
" answer.na.deleted[5:12],\n",
" answer.na.correct[13],\n",
" answer.na.deleted[13:19]\n",
" )\n",
"new_sol <- get.exam.answers(new_sol,\n",
" solmod)\n",
"print(na.noanswer)\n",
"\n",
"scores_guess = get_scores(correct_answers = na.noanswer)\n",
"\"Diferencia de resultado por estudiante:\"\n",
"scores_real - scores_guess\n",
"\"Diferencia total:\"\n",
"sum(abs(scores_real - scores_guess))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"print_names <- function(coefs, clamping=0) {\n",
" names(coefs[coefs>clamping])\n",
"}\n",
"print (sol)\n",
"# print_names(coef.na.noanswer)\n",
"# print_names(coef.na.correct)\n",
"# print_names(coef.na.deleted)\n",
"\n",
"w <- coef.na.noanswer + coef.na.correct + coef.na.deleted\n",
"w <- w/3"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Random forest para selección de variables"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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"\t<li>0.0134067627973492</li>\n",
"\t<li>0.011988676797772</li>\n",
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"\\item 0.217399742438867\n",
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"\\item 0.048071612599722\n",
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"\\item 0.0408918251331436\n",
"\\item 0.0301370039084309\n",
"\\item 0.023702151531134\n",
"\\item 0.0227558957013513\n",
"\\item 0.0214606440267389\n",
"\\item 0.0191836659339674\n",
"\\item 0.0187806070669771\n",
"\\item 0.0182442440277188\n",
"\\item 0.0179963150392818\n",
"\\item 0.0172522250569795\n",
"\\item 0.016021227871493\n",
"\\item 0.0150195167687727\n",
"\\item 0.0145640052362993\n",
"\\item 0.0144605995254125\n",
"\\item 0.014444990800135\n",
"\\item 0.0134067627973492\n",
"\\item 0.011988676797772\n",
"\\item 0.0111206889301591\n",
"\\item 0.0106117254892549\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 0.217399742438867\n",
"2. 0.10315919113193\n",
"3. 0.05825561364189\n",
"4. 0.048071612599722\n",
"5. 0.0425507748196266\n",
"6. 0.0408918251331436\n",
"7. 0.0301370039084309\n",
"8. 0.023702151531134\n",
"9. 0.0227558957013513\n",
"10. 0.0214606440267389\n",
"11. 0.0191836659339674\n",
"12. 0.0187806070669771\n",
"13. 0.0182442440277188\n",
"14. 0.0179963150392818\n",
"15. 0.0172522250569795\n",
"16. 0.016021227871493\n",
"17. 0.0150195167687727\n",
"18. 0.0145640052362993\n",
"19. 0.0144605995254125\n",
"20. 0.014444990800135\n",
"21. 0.0134067627973492\n",
"22. 0.011988676797772\n",
"23. 0.0111206889301591\n",
"24. 0.0106117254892549\n",
"\n",
"\n"
],
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" [1] 0.21739974 0.10315919 0.05825561 0.04807161 0.04255077 0.04089183\n",
" [7] 0.03013700 0.02370215 0.02275590 0.02146064 0.01918367 0.01878061\n",
"[13] 0.01824424 0.01799632 0.01725223 0.01602123 0.01501952 0.01456401\n",
"[19] 0.01446060 0.01444499 0.01340676 0.01198868 0.01112069 0.01061173"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"require(randomForest)\n",
"\n",
"fit.rf = randomForest(total~., data=data)\n",
"vi <- importance(fit.rf)\n",
"w <- as.data.frame(vi/sum(vi))\n",
"w.ordered <- w[order(-w$IncNodePurity), , drop = FALSE]\n",
"w.ordered[w.ordered>0.01]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"library(randomForest)\n",
"library(mlbench)\n",
"library(caret)\n",
"\n",
"control <- trainControl(method=\"repeatedcv\", \n",
" number=20, \n",
" repeats=3,\n",
" search=\"random\")\n",
"mtry <- sqrt(ncol(x))\n",
"tunegrid <- expand.grid(.mtry=c(1:81))\n",
"rf_default <- train(total~., data=data, \n",
" method=\"rf\",\n",
" seed = seed,\n",
" metric=\"RMSE\",\n",
" importance=T,\n",
" tuneGrid=tunegrid,\n",
" tuneLength=15,\n",
" trControl=control)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"plot(rf_default)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# names(rf_default)\n",
"rf <- randomForest(x, y, mtry = 44, importance=T)\n",
"vi <- varImp(rf)\n",
"\n",
"\n",
"\n",
"coef_names <- substrLeft(rownames(vi),1)\n",
"coef_answer <- substrRight(rownames(vi),1)\n",
"# colnames(d)\n",
"last_occur <- length(coef_names)-match(unique(coef_names),rev(coef_names))+1\n",
"# length(last_occur)\n",
"for (i in 1:1){\n",
" if (i==1){\n",
" idx <- which.max(vi[c(1:last_occur[1]),])\n",
" print (cbind(coef_names[idx], coef_answer[idx]))\n",
" }else{\n",
" \n",
" }\n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": 232,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message:\n",
"\"In seq.default(0, 1, size = 10) :\n",
" extra argument 'size' will be disregarded\""
]
},
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>0</li>\n",
"\t<li>1</li>\n",
"</ol>\n"
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"\\begin{enumerate*}\n",
"\\item 0\n",
"\\item 1\n",
"\\end{enumerate*}\n"
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"1. 0\n",
"2. 1\n",
"\n",
"\n"
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"[1] 0 1"
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"source": [
"seq(0,1,size=10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Gradient Bosting Models"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"gbmGrid <- expand.grid(interaction.depth = c(1, 5, 9), \n",
" n.trees = (1:30)*50, \n",
" shrinkage = seq(0.1,0.6,length=5),\n",
" n.minobsinnode = c(1,2,3,4))\n",
"fitControl <- trainControl(method = \"repeatedcv\",\n",
" number = 20,\n",
" repeats = 3)\n",
"gbmFit <- train(total ~ ., data = data, \n",
" method = \"gbm\", \n",
" trControl = fitControl, \n",
" verbose = FALSE, \n",
" tuneGrid = gbmGrid,\n",
" ## Specify which metric to optimize\n",
" metric = \"RMSE\")\n",
"gbmFit"
]
},
{
"cell_type": "code",
"execution_count": 272,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"gbm variable importance\n",
"\n",
" only 20 most important variables shown (out of 84)\n",
"\n",
" Overall\n",
"q4E 83.260\n",
"q10E 73.695\n",
"q3C 44.082\n",
"q21C 32.129\n",
"q10C 31.881\n",
"q22E 31.583\n",
"q8C 30.998\n",
"q12D 19.120\n",
"q4D 18.391\n",
"q12E 15.329\n",
"q6D 13.023\n",
"q24C 11.977\n",
"q1B 10.507\n",
"q23E 10.430\n",
"q20E 8.805\n",
"q17B 7.214\n",
"q17D 5.010\n",
"q5D 4.309\n",
"q8E 4.293\n",
"q14E 3.526"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# whichTwoPct <- tolerance(gbmFit$results, metric = \"RMSE\", \n",
"# maximize = F)\n",
"# whichTwoPct\n",
"# (gbmFit$results[whichTwoPct,])\n",
"bestfit<-(gbmFit$results[186,])\n",
"# predict(gbmFit, x, type=\"coefficients\")\n",
"varImp(gbmFit, scale=F)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Eliminación recursiva de variables\n",
"Uso de *random forest* para eliminar las variables de acuerdo a su importancia. En cada iteración se hace uso de N predictores. El algoritmo trata de buscar el valor óptimo de N."
]
},
{
"cell_type": "code",
"execution_count": 273,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message:\n",
"\"package 'mlbench' was built under R version 3.3.3\"Loading required package: Formula\n",
"\n",
"Attaching package: 'Hmisc'\n",
"\n",
"The following objects are masked from 'package:plyr':\n",
"\n",
" is.discrete, summarize\n",
"\n",
"The following object is masked from 'package:randomForest':\n",
"\n",
" combine\n",
"\n",
"The following object is masked from 'package:e1071':\n",
"\n",
" impute\n",
"\n",
"The following objects are masked from 'package:base':\n",
"\n",
" format.pval, round.POSIXt, trunc.POSIXt, units\n",
"\n"
]
},
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>'q9E'</li>\n",
"\t<li>'q25E'</li>\n",
"\t<li>'q21B'</li>\n",
"\t<li>'q3B'</li>\n",
"\t<li>'q10D'</li>\n",
"\t<li>'q21E'</li>\n",
"</ol>\n"
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"text/latex": [
"\\begin{enumerate*}\n",
"\\item 'q9E'\n",
"\\item 'q25E'\n",
"\\item 'q21B'\n",
"\\item 'q3B'\n",
"\\item 'q10D'\n",
"\\item 'q21E'\n",
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"1. 'q9E'\n",
"2. 'q25E'\n",
"3. 'q21B'\n",
"4. 'q3B'\n",
"5. 'q10D'\n",
"6. 'q21E'\n",
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{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading required package: ipred\n",
"Warning message:\n",
"\"package 'ipred' was built under R version 3.3.3\"Loading required package: rpart\n"
]
},
{
"data": {
"text/plain": [
"\n",
"Recursive feature selection\n",
"\n",
"Outer resampling method: Cross-Validated (20 fold, repeated 1 times) \n",
"\n",
"Resampling performance over subset size:\n",
"\n",
" Variables RMSE Rsquared RMSESD RsquaredSD Selected\n",
" 1 2.136 1 1.700 NA \n",
" 2 2.302 1 1.818 0 \n",
" 3 2.290 1 1.814 0 \n",
" 4 2.275 1 1.903 0 \n",
" 5 2.337 1 1.897 0 \n",
" 6 2.287 1 1.835 0 \n",
" 7 2.275 1 1.890 0 \n",
" 8 2.190 1 1.751 0 \n",
" 9 2.232 1 1.856 0 \n",
" 10 2.214 1 1.893 0 \n",
" 11 2.157 1 1.906 0 \n",
" 12 2.274 1 1.974 0 \n",
" 13 2.283 1 2.076 0 \n",
" 14 2.323 1 1.996 0 \n",
" 15 2.194 1 1.830 0 \n",
" 16 2.339 1 1.795 0 \n",
" 17 2.213 1 1.802 0 \n",
" 18 2.263 1 1.927 0 \n",
" 19 2.163 1 1.869 0 \n",
" 20 2.112 1 2.009 0 *\n",
" 21 2.434 1 2.040 0 \n",
" 22 2.134 1 1.875 0 \n",
" 23 2.371 1 1.859 0 \n",
" 24 2.179 1 1.913 0 \n",
" 25 2.261 1 1.867 0 \n",
" 84 2.209 1 1.809 0 \n",
"\n",
"The top 5 variables (out of 20):\n",
" q22E, q3C, q10E, q19C, q24C\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"library(caret)\n",
"library(mlbench)\n",
"library(Hmisc)\n",
"library(randomForest)\n",
"\n",
"corr_mat <- cor(x)\n",
"too_high <- findCorrelation(corr_mat, cutoff = .9)\n",
"colnames(x[,too_high])\n",
"x.2 <- x[, -too_high]\n",
"\n",
"subsets <- c(1:25)\n",
"ctrl <- rfeControl(functions = treebagFuncs,\n",
" method = \"repeatedcv\",\n",
" number = 20,\n",
" verbose = FALSE)\n",
"rfProfile <- rfe(as.data.frame(x), y, sizes = subsets, rfeControl = ctrl)\n",
"rfProfile"
]
},
{
"cell_type": "code",
"execution_count": 278,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
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{
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HLa6OzJ+ByIZEW7ZiNJI5LtGOnKiNnIHIhkRbtmI8kh0n7UzjAEjkjzol2zkeQQ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\nSAABIBJAAIgEEAAiAQTwP+dGY7k0j5dpAAAAAElFTkSuQmCC",
"text/plain": [
"plot without title"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rfProfile$optVariables\n",
"trellis.par.set(caretTheme())\n",
"plot(rfProfile, type = c(\"g\", \"o\"))"
]
},
{
"cell_type": "code",
"execution_count": 281,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"\n",
"Recursive feature selection\n",
"\n",
"Outer resampling method: Cross-Validated (20 fold, repeated 1 times) \n",
"\n",
"Resampling performance over subset size:\n",
"\n",
" Variables RMSE Rsquared RMSESD RsquaredSD Selected\n",
" 1 2.654 1 1.957 0 \n",
" 2 2.237 1 1.614 0 \n",
" 3 2.200 1 1.940 0 \n",
" 4 2.123 1 1.732 0 \n",
" 5 2.134 1 1.723 0 \n",
" 6 2.167 1 1.704 0 \n",
" 7 2.130 1 1.589 0 \n",
" 8 2.135 1 1.673 0 \n",
" 9 2.129 1 1.560 0 \n",
" 10 2.085 1 1.574 0 \n",
" 11 2.018 1 1.618 0 \n",
" 12 1.985 1 1.613 0 \n",
" 13 2.015 1 1.632 0 \n",
" 14 2.017 1 1.598 0 \n",
" 15 2.042 1 1.598 0 \n",
" 16 2.022 1 1.617 0 \n",
" 17 1.994 1 1.647 0 \n",
" 18 2.035 1 1.603 0 \n",
" 19 1.971 1 1.537 0 \n",
" 20 1.937 1 1.617 0 *\n",
" 21 1.965 1 1.601 0 \n",
" 22 2.007 1 1.599 0 \n",
" 23 1.974 1 1.601 0 \n",
" 24 1.946 1 1.637 0 \n",
" 25 1.981 1 1.604 0 \n",
" 84 1.983 1 1.633 0 \n",
"\n",
"The top 5 variables (out of 20):\n",
" q10E, q22E, q10C, q4E, q21C\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"subsets <- c(1:25)\n",
"ctrl <- rfeControl(functions = rfFuncs,\n",
" method = \"repeatedcv\",\n",
" number = 20,\n",
" verbose = FALSE)\n",
"rfProfile <- rfe(as.data.frame(x), y, sizes = subsets, rfeControl = ctrl)\n",
"rfProfile"
]
},
{
"cell_type": "code",
"execution_count": 280,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"data": {},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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S0lGtLVplz/TTs+2FcBbSnZkK52NpSeOxsIqQLaUq8QUt99dkadIw+lej9eMNoz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\nARASQACEBBDA/8ekDNqNkofdAAAAAElFTkSuQmCC",
"text/plain": [
"plot without title"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"trellis.par.set(caretTheme())\n",
"plot(rfProfile, type = c(\"g\", \"o\"))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Uso de algoritmos genéticos"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Error seleccionando la letra más respondida por pregunta"
]
},
{
"cell_type": "code",
"execution_count": 286,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [1] 0 -4 -3 -4 -3 -1 -2 -1 2 -1 1 -3 -1 -3 -2 -1 -2 1 -5 -2 -2 -4 -5\n"
]
},
{
"data": {
"text/html": [
"53"
],
"text/latex": [
"53"
],
"text/markdown": [
"53"
],
"text/plain": [
"[1] 53"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>distance</th><th scope=col>q1</th><th scope=col>q2</th><th scope=col>q3</th><th scope=col>q4</th><th scope=col>q5</th><th scope=col>q6</th><th scope=col>q7</th><th scope=col>q8</th><th scope=col>q9</th><th scope=col>...</th><th scope=col>q16</th><th scope=col>q17</th><th scope=col>q18</th><th scope=col>q19</th><th scope=col>q20</th><th scope=col>q21</th><th scope=col>q22</th><th scope=col>q23</th><th scope=col>q24</th><th scope=col>q25</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>53 </td><td>C </td><td>B </td><td>C </td><td>D </td><td>A </td><td>D </td><td>E </td><td>E </td><td>D </td><td>...</td><td>A </td><td>D </td><td>B </td><td>C </td><td>E </td><td>D </td><td>E </td><td>E </td><td>C </td><td>B </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllllllllllllllllllllllll}\n",
" distance & q1 & q2 & q3 & q4 & q5 & q6 & q7 & q8 & q9 & ... & q16 & q17 & q18 & q19 & q20 & q21 & q22 & q23 & q24 & q25\\\\\n",
"\\hline\n",
"\t 53 & C & B & C & D & A & D & E & E & D & ... & A & D & B & C & E & D & E & E & C & B \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"distance | q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | ... | q16 | q17 | q18 | q19 | q20 | q21 | q22 | q23 | q24 | q25 | \n",
"|---|\n",
"| 53 | C | B | C | D | A | D | E | E | D | ... | A | D | B | C | E | D | E | E | C | B | \n",
"\n",
"\n"
],
"text/plain": [
" distance q1 q2 q3 q4 q5 q6 q7 q8 q9 ... q16 q17 q18 q19 q20 q21 q22 q23 q24\n",
"1 53 C B C D A D E E D ... A D B C E D E E C \n",
" q25\n",
"1 B "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"scores_guess = get_scores(correct_answers = solmod)\n",
"print( scores_real - scores_guess )\n",
"distance = sum( abs( scores_real - scores_guess ) )\n",
"distance\n",
"high_scores = data.frame(distance = distance, t(solmod))\n",
"high_scores"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Definimos la función para mutar"
]
},
{
"cell_type": "code",
"execution_count": 288,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"go_mutate = function(parent, changes = 1){\n",
" # changes indicates how many random changes to make in the 25 answers\n",
" change_these = sample(x = 1:25, size = changes, replace = F)\n",
" for (i in change_these){\n",
" parent[i] = sample(x = setdiff(LETTERS[1:5],parent[i]), size = 1)\n",
" }\n",
" return(parent)\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Definimos el proceso evolutivo"
]
},
{
"cell_type": "code",
"execution_count": 283,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"library(plyr)\n",
"Nbest = 4\n",
"best_score = 53\n",
"go_evolve = function(runs = 1000, population = high_scores, best = best_score){\n",
" for (i in 1:runs){\n",
" # only choose from the best Nbest rows\n",
" selectrow <- sample(x = 1:min(Nbest,nrow(population)), size = 1) \n",
" # we choose a random mutation rate between 1 and 3 else we may get stuck easily\n",
" offspring <- go_mutate(parent = population[selectrow,2:26],\n",
" changes = sample(1:3,1))\n",
" offspring_score <-sum( abs( scores_real - \n",
" get_scores(correct_answers = offspring) ) )\n",
" if (offspring_score <= max(population$distance)){ \n",
" population <- rbind(data.frame(distance = offspring_score, offspring),\n",
" population)\n",
" population <- arrange(df = population, distance)\n",
" # if the new guy is a duplicate, remove him\n",
" population <- population[!duplicated(population),] \n",
" # discard anyone not fit enough (so the code might run faster)\n",
" population <- population[1:min(Nbest,nrow(population)),] \n",
" }\n",
" # keep track of the current highest score\n",
" best <- c(best, population[1,1]) # vector index is the run number\n",
" } \n",
" return(list(population=population,best=best))\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 289,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [1] 53 48 43 38 38 38 38 38 38 38 38\n"
]
}
],
"source": [
"result = go_evolve(runs = 10)\n",
"high_scores = result$population\n",
"best_score = result$best\n",
"print(best_score)"
]
},
{
"cell_type": "code",
"execution_count": 290,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1] 13 13 13 13 13 13\n"
]
}
],
"source": [
"result = go_evolve(runs = 1000)\n",
"high_scores = result$population\n",
"best_score = result$best\n",
"print(tail(best_score))"
]
},
{
"cell_type": "code",
"execution_count": 291,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1] 0 0 0 0 0 0\n"
]
}
],
"source": [
"result = go_evolve(runs = 1000)\n",
"high_scores = result$population\n",
"best_score = result$best\n",
"print(tail(best_score))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Hemos encontrado una solución que devuelve el mismo resultado en *total* que la que nos dan los datos."
]
},
{
"cell_type": "code",
"execution_count": 300,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n"
]
}
],
"source": [
"sol <- high_scores[1,2:26]\n",
"scores_guess = get_scores(correct_answers = sol)\n",
"print( scores_real - scores_guess )"
]
},
{
"cell_type": "code",
"execution_count": 301,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"?sample"
]
},
{
"cell_type": "code",
"execution_count": 293,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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IW0fsNcc5OukNZvmGtu0hXS+g1zzU26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",
"text/plain": [
"plot without title"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ggplot(\n",
" data = data.frame(run=1:length(best_score), \n",
" best=best_score), \n",
" aes(x=run, y=best)) + geom_line() + expand_limits(y=0) + theme_bw()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.3.2"
},
"name": "Manuel Lagunas Arto"
},
"nbformat": 4,
"nbformat_minor": 2
}
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