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FPM-Outliers-Evaluation.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaluation of algorithms based on the frequent pattern mining"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Author: __Jaroslav Kuchař__ *(firstname.lastname@{vse.cz,fit.cvut.cz})*\n",
" * Department of Information and Knowledge Engineering, Faculty of Informatics and Statistics, University of Economics Prague \n",
" * Web Intelligence Research Group, Faculty of Information Technology, Czech Technical University in Prague\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Table of contents:\n",
"1. [Functions](#Functions) with algorithm based on frequent itemsets mining.\n",
" * package [fpmoutliers](https://github.com/jaroslav-kuchar/fpmoutliers)\n",
"1. [Breast-Cancer-Wisconsin (breast-w)](#Breast-Cancer-Wisconsin:breast-w)\n",
"1. [Lymphography (lymph)](#Lymphography:lymph)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# anomaly detection\n",
"library(fpmoutliers) \n",
"options(warn=-1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Functions"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# compute anomaly score using all algorithms\n",
"computeScores <- function(){\n",
" scoresFPOF <<- FPOF(data, minSupport = ms, mlen = ml)$scores\n",
" scoresLFPOF <<- LFPOF(data, minSupport = ms, mlen = ml)$scores\n",
" scoresFPCOF <<- FPCOF(data, minSupport = ms, mlen = ml)$scores\n",
" scoresWFPOF <<- WFPOF(data, minSupport = ms, mlen = ml)$scores\n",
" scoresWCFPOF <<- WCFPOF(data, minSupport = ms, mlen = ml)$scores\n",
" scoresMFPOF <<- MFPOF(data, minSupport = ms, mlen = ml)$scores\n",
" scoresFPI <<- FPI(data, minSupport = ms, mlen = ml)$scores\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# compute detection rates for each algorithm\n",
"prepareResults <- function() {\n",
" # assign classes\n",
" labelsFPOF <- labels[order(scoresFPOF, decreasing = FALSE)]\n",
" labelsLFPOF <- labels[order(scoresLFPOF, decreasing = FALSE)]\n",
" labelsFPCOF <- labels[order(scoresFPCOF, decreasing = FALSE)]\n",
" labelsWFPOF <- labels[order(scoresWFPOF, decreasing = FALSE)]\n",
" labelsWCFPOF <- labels[order(scoresWCFPOF, decreasing = FALSE)]\n",
" labelsMFPOF <- labels[order(scoresMFPOF, decreasing = FALSE)]\n",
" labelsFPI <- labels[order(scoresFPI, decreasing = TRUE)]\n",
"\n",
" # prepare data frame\n",
" results <- data.frame(\n",
" topRatio=topRatio, \n",
" fpof=rep(0,length(topRatio)), \n",
" lfpof=rep(0,length(topRatio)),\n",
" fpcof=rep(0,length(topRatio)),\n",
" wfpof=rep(0,length(topRatio)),\n",
" wcfpof=rep(0,length(topRatio)),\n",
" mfpof=rep(0,length(topRatio)),\n",
" fpi=rep(0,length(topRatio))\n",
" )\n",
" results$topRatio <- as.factor(topRatio)\n",
" # compute detection ration\n",
" for(tr in 1:length(topRatio)){\n",
" results$fpof[tr] <- length(which(labelsFPOF[1:topRatio[tr]]%in%anomalyClasses))/length(which(labels%in%anomalyClasses))\n",
" results$lfpof[tr] <- length(which(labelsLFPOF[1:topRatio[tr]]%in%anomalyClasses))/length(which(labels%in%anomalyClasses))\n",
" results$fpcof[tr] <- length(which(labelsFPCOF[1:topRatio[tr]]%in%anomalyClasses))/length(which(labels%in%anomalyClasses))\n",
" results$wfpof[tr] <- length(which(labelsWFPOF[1:topRatio[tr]]%in%anomalyClasses))/length(which(labels%in%anomalyClasses))\n",
" results$wcfpof[tr] <- length(which(labelsWCFPOF[1:topRatio[tr]]%in%anomalyClasses))/length(which(labels%in%anomalyClasses))\n",
" results$mfpof[tr] <- length(which(labelsMFPOF[1:topRatio[tr]]%in%anomalyClasses))/length(which(labels%in%anomalyClasses))\n",
" results$fpi[tr] <- length(which(labelsFPI[1:topRatio[tr]]%in%anomalyClasses))/length(which(labels%in%anomalyClasses))\n",
" }\n",
" results \n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading required package: gplots\n",
"\n",
"Attaching package: ‘gplots’\n",
"\n",
"The following object is masked from ‘package:stats’:\n",
"\n",
" lowess\n",
"\n",
"Loading required package: caret\n",
"Loading required package: lattice\n",
"Loading required package: ggplot2\n",
"Type 'citation(\"pROC\")' for a citation.\n",
"\n",
"Attaching package: ‘pROC’\n",
"\n",
"The following objects are masked from ‘package:stats’:\n",
"\n",
" cov, smooth, var\n",
"\n"
]
}
],
"source": [
"# compute auc\n",
"library(ROCR)\n",
"require(caret)\n",
"library(pROC)\n",
"\n",
"auc <- function() {\n",
" # assign classes\n",
" labelsFPOF <- labels[order(scoresFPOF, decreasing = FALSE)]\n",
" labelsLFPOF <- labels[order(scoresLFPOF, decreasing = FALSE)]\n",
" labelsFPCOF <- labels[order(scoresFPCOF, decreasing = FALSE)]\n",
" labelsWFPOF <- labels[order(scoresWFPOF, decreasing = FALSE)]\n",
" labelsWCFPOF <- labels[order(scoresWCFPOF, decreasing = FALSE)]\n",
" labelsMFPOF <- labels[order(scoresMFPOF, decreasing = FALSE)]\n",
" labelsFPI <- labels[order(scoresFPI, decreasing = TRUE)]\n",
" \n",
" # prepare data frame\n",
" results <- data.frame(\n",
" fpof=rep(0,2), \n",
" lfpof=rep(0,2),\n",
" fpcof=rep(0,2),\n",
" wfpof=rep(0,2),\n",
" wcfpof=rep(0,2),\n",
" mfpof=rep(0,2),\n",
" fpi=rep(0,2)\n",
" )\n",
" \n",
" for(tr in 1:1){\n",
" results$fpof[tr] <- unlist(performance(prediction(scoresFPOF, labels),\"auc\")@y.values)\n",
" results$lfpof[tr] <- unlist(performance(prediction(scoresLFPOF, labels),\"auc\")@y.values)\n",
" results$fpcof[tr] <- unlist(performance(prediction(scoresFPCOF, labels),\"auc\")@y.values)\n",
" results$wfpof[tr] <- unlist(performance(prediction(scoresWFPOF, labels),\"auc\")@y.values)\n",
" results$wcfpof[tr] <- unlist(performance(prediction(scoresWCFPOF, labels),\"auc\")@y.values)\n",
" results$mfpof[tr] <- unlist(performance(prediction(scoresMFPOF, labels),\"auc\")@y.values)\n",
" results$fpi[tr] <- unlist(performance(prediction(scoresFPI, labels),\"auc\")@y.values)\n",
" }\n",
" \n",
" for(tr in 2:2){\n",
" results$fpof[tr] <- pROC::auc(labels, scoresFPOF)\n",
" results$lfpof[tr] <- pROC::auc(labels, scoresLFPOF)\n",
" results$fpcof[tr] <- pROC::auc(labels, scoresFPCOF)\n",
" results$wfpof[tr] <- pROC::auc(labels, scoresWFPOF)\n",
" results$wcfpof[tr] <- pROC::auc(labels, scoresWCFPOF)\n",
" results$mfpof[tr] <- pROC::auc(labels, scoresMFPOF)\n",
" results$fpi[tr] <- pROC::auc(labels, scoresFPI)\n",
" } \n",
" \n",
" results\n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# plot results as line graph\n",
"plotResults <- function(results, title){\n",
" # set up the plot \n",
" par(cex=1.5)\n",
" xrange <- range(as.numeric(as.character(results$topRatio)))\n",
" yrange <- c(0,1)\n",
" nc <- ncol(results)-1\n",
" plot(xrange, yrange, type=\"n\", xlab=\"Top K\", ylab=\"Detection Rate\", main = title) \n",
" colors <- rainbow(nc) \n",
" linetype <- c(1:nc) \n",
" plotchar <- seq(18,18+nc,1)\n",
" # add lines \n",
" for (i in 1:nc) { \n",
" lines(as.numeric(as.character(results$topRatio)), results[,i+1], type=\"b\", lwd=1.5,\n",
" lty=linetype[i], col=colors[i], pch=plotchar[i]) \n",
" } \n",
" # add a legend \n",
" legend(xrange[2]*.78, 0.56,tail(colnames(results),-1), cex=0.85, col=colors, pch=plotchar, lty=linetype, title=\"Algorithms\") \n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# compute frequency and percentage of classes\n",
"valueSummary <- function(x) {cbind(frequency = table(x), percentage = paste(round(prop.table(table(x))*100,2),\"%\"))}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Breast Cancer Wisconsin:breast-w"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# file to load\n",
"fileName <- \"./breast-w.csv\"\n",
"# predefined top-k ratios to check\n",
"topRatio <- c(4,8,16,24,32,40,48,56,64,72,80,100,112)\n",
"# minimum support and maximum length of a frequent pattern\n",
"ms <- 0.1\n",
"ml <- 5\n",
"# name of attribute with class\n",
"className <- \"class\"\n",
"# class values as anomalies\n",
"anomalyClasses <- c(\"anomaly\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# load csv\n",
"data <- read.csv(fileName)\n",
"# preprocessing\n",
"classes <- as.character(data$class)\n",
"# rename classes\n",
"classes[which(classes==\"malignant\")] <- \"anomaly\"\n",
"classes[which(classes!=\"anomaly\")] <- \"normal\"\n",
"data$class <- as.factor(classes)\n",
"anomalies<-which(data$class==\"anomaly\")\n",
"# create highly imbalanced dataset with limited amount of anomalies - one in every six malignant records was chosen\n",
"index <- c(which(data$class!=\"anomaly\"), anomalies[seq(1, length(anomalies), 6)])\n",
"data <- data[index,]\n",
"# remember labels\n",
"labels <- data[[className]]\n",
"# delete class atribute\n",
"data[[className]] <- NULL"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"Overview of classes:"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>frequency</th><th scope=col>percentage</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>anomaly</th><td>41 </td><td>8.22 % </td></tr>\n",
"\t<tr><th scope=row>normal</th><td>458 </td><td>91.78 %</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|ll}\n",
" & frequency & percentage\\\\\n",
"\\hline\n",
"\tanomaly & 41 & 8.22 \\% \\\\\n",
"\tnormal & 458 & 91.78 \\%\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| <!--/--> | frequency | percentage | \n",
"|---|---|\n",
"| anomaly | 41 | 8.22 % | \n",
"| normal | 458 | 91.78 % | \n",
"\n",
"\n"
],
"text/plain": [
" frequency percentage\n",
"anomaly 41 8.22 % \n",
"normal 458 91.78 % "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"IRdisplay::display_markdown(\"Overview of classes:\")\n",
"valueSummary(labels)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Apriori\n",
"\n",
"Parameter specification:\n",
" confidence minval smax arem aval originalSupport maxtime support minlen\n",
" NA 0.1 1 none FALSE TRUE 5 0.1 1\n",
" maxlen target ext\n",
" 5 frequent itemsets FALSE\n",
"\n",
"Algorithmic control:\n",
" filter tree heap memopt load sort verbose\n",
" 0.1 TRUE TRUE FALSE TRUE 2 TRUE\n",
"\n",
"Absolute minimum support count: 49 \n",
"\n",
"set item appearances ...[0 item(s)] done [0.00s].\n",
"set transactions ...[87 item(s), 499 transaction(s)] done [0.00s].\n",
"sorting and recoding items ... [15 item(s)] done [0.00s].\n",
"creating transaction tree ... done [0.00s].\n",
"checking subsets of size 1 2 3 4 5 done [0.00s].\n",
"writing ... [683 set(s)] done [0.00s].\n",
"creating S4 object ... done [0.00s].\n",
"Apriori\n",
"\n",
"Parameter specification:\n",
" confidence minval smax arem aval originalSupport maxtime support minlen\n",
" NA 0.1 1 none FALSE TRUE 5 0.1 1\n",
" maxlen target ext\n",
" 5 frequent itemsets FALSE\n",
"\n",
"Algorithmic control:\n",
" filter tree heap memopt load sort verbose\n",
" 0.1 TRUE TRUE FALSE TRUE 2 TRUE\n",
"\n",
"Absolute minimum support count: 49 \n",
"\n",
"set item appearances ...[0 item(s)] done [0.00s].\n",
"set transactions ...[87 item(s), 499 transaction(s)] done [0.00s].\n",
"sorting and recoding items ... [15 item(s)] done [0.00s].\n",
"creating transaction tree ... done [0.00s].\n",
"checking subsets of size 1 2 3 4 5 done [0.00s].\n",
"writing ... [683 set(s)] done [0.00s].\n",
"creating S4 object ... done [0.00s].\n",
"Apriori\n",
"\n",
"Parameter specification:\n",
" confidence minval smax arem aval originalSupport maxtime support minlen\n",
" NA 0.1 1 none FALSE TRUE 5 0.1 1\n",
" maxlen target ext\n",
" 5 frequent itemsets FALSE\n",
"\n",
"Algorithmic control:\n",
" filter tree heap memopt load sort verbose\n",
" 0.1 TRUE TRUE FALSE TRUE 2 TRUE\n",
"\n",
"Absolute minimum support count: 49 \n",
"\n",
"set item appearances ...[0 item(s)] done [0.00s].\n",
"set transactions ...[87 item(s), 499 transaction(s)] done [0.00s].\n",
"sorting and recoding items ... [15 item(s)] done [0.00s].\n",
"creating transaction tree ... done [0.00s].\n",
"checking subsets of size 1 2 3 4 5 done [0.00s].\n",
"writing ... [683 set(s)] done [0.00s].\n",
"creating S4 object ... done [0.00s].\n",
"Apriori\n",
"\n",
"Parameter specification:\n",
" confidence minval smax arem aval originalSupport maxtime support minlen\n",
" NA 0.1 1 none FALSE TRUE 5 0.1 1\n",
" maxlen target ext\n",
" 5 frequent itemsets FALSE\n",
"\n",
"Algorithmic control:\n",
" filter tree heap memopt load sort verbose\n",
" 0.1 TRUE TRUE FALSE TRUE 2 TRUE\n",
"\n",
"Absolute minimum support count: 49 \n",
"\n",
"set item appearances ...[0 item(s)] done [0.00s].\n",
"set transactions ...[87 item(s), 499 transaction(s)] done [0.00s].\n",
"sorting and recoding items ... [15 item(s)] done [0.00s].\n",
"creating transaction tree ... done [0.00s].\n",
"checking subsets of size 1 2 3 4 5 done [0.00s].\n",
"writing ... [683 set(s)] done [0.00s].\n",
"creating S4 object ... done [0.00s].\n",
"Apriori\n",
"\n",
"Parameter specification:\n",
" confidence minval smax arem aval originalSupport maxtime support minlen\n",
" NA 0.1 1 none FALSE TRUE 5 0.1 1\n",
" maxlen target ext\n",
" 5 frequent itemsets FALSE\n",
"\n",
"Algorithmic control:\n",
" filter tree heap memopt load sort verbose\n",
" 0.1 TRUE TRUE FALSE TRUE 2 TRUE\n",
"\n",
"Absolute minimum support count: 49 \n",
"\n",
"set item appearances ...[0 item(s)] done [0.00s].\n",
"set transactions ...[87 item(s), 499 transaction(s)] done [0.00s].\n",
"sorting and recoding items ... [15 item(s)] done [0.00s].\n",
"creating transaction tree ... done [0.00s].\n",
"checking subsets of size 1 2 3 4 5 done [0.00s].\n",
"writing ... [683 set(s)] done [0.00s].\n",
"creating S4 object ... done [0.00s].\n",
"Apriori\n",
"\n",
"Parameter specification:\n",
" confidence minval smax arem aval originalSupport maxtime support minlen\n",
" NA 0.1 1 none FALSE TRUE 5 0.1 1\n",
" maxlen target ext\n",
" 5 frequent itemsets FALSE\n",
"\n",
"Algorithmic control:\n",
" filter tree heap memopt load sort verbose\n",
" 0.1 TRUE TRUE FALSE TRUE 2 TRUE\n",
"\n",
"Absolute minimum support count: 49 \n",
"\n",
"set item appearances ...[0 item(s)] done [0.00s].\n",
"set transactions ...[87 item(s), 499 transaction(s)] done [0.00s].\n",
"sorting and recoding items ... [15 item(s)] done [0.00s].\n",
"creating transaction tree ... done [0.00s].\n",
"checking subsets of size 1 2 3 4 5 done [0.00s].\n",
"writing ... [683 set(s)] done [0.00s].\n",
"creating S4 object ... done [0.00s].\n",
"Apriori\n",
"\n",
"Parameter specification:\n",
" confidence minval smax arem aval originalSupport maxtime support minlen\n",
" NA 0.1 1 none FALSE TRUE 5 0.1 1\n",
" maxlen target ext\n",
" 5 frequent itemsets FALSE\n",
"\n",
"Algorithmic control:\n",
" filter tree heap memopt load sort verbose\n",
" 0.1 TRUE TRUE FALSE TRUE 2 TRUE\n",
"\n",
"Absolute minimum support count: 49 \n",
"\n",
"set item appearances ...[0 item(s)] done [0.00s].\n",
"set transactions ...[87 item(s), 499 transaction(s)] done [0.00s].\n",
"sorting and recoding items ... [15 item(s)] done [0.00s].\n",
"creating transaction tree ... done [0.00s].\n",
"checking subsets of size 1 2 3 4 5 done [0.00s].\n",
"writing ... [683 set(s)] done [0.00s].\n",
"creating S4 object ... done [0.00s].\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"FI lengths: 0.00151610374450684\n",
"FI qualities: 0.00149989128112793\n",
"FI coverages: 0.0719771385192871\n",
"FI multiply: 0.00895881652832031\n",
"Penalization: 0.00519084930419922\n",
"Means: 0.231338024139404\n"
]
}
],
"source": [
"computeScores()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>topRatio</th><th scope=col>fpof</th><th scope=col>lfpof</th><th scope=col>fpcof</th><th scope=col>wfpof</th><th scope=col>wcfpof</th><th scope=col>mfpof</th><th scope=col>fpi</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>4 </td><td>0.07317073</td><td>0.07317073</td><td>0 </td><td>0.07317073</td><td>0.07317073</td><td>0.0000000 </td><td>0.07317073</td></tr>\n",
"\t<tr><td>8 </td><td>0.17073171</td><td>0.17073171</td><td>0 </td><td>0.17073171</td><td>0.17073171</td><td>0.0000000 </td><td>0.17073171</td></tr>\n",
"\t<tr><td>16 </td><td>0.34146341</td><td>0.31707317</td><td>0 </td><td>0.34146341</td><td>0.34146341</td><td>0.0000000 </td><td>0.34146341</td></tr>\n",
"\t<tr><td>24 </td><td>0.48780488</td><td>0.51219512</td><td>0 </td><td>0.48780488</td><td>0.48780488</td><td>0.1707317 </td><td>0.51219512</td></tr>\n",
"\t<tr><td>32 </td><td>0.68292683</td><td>0.70731707</td><td>0 </td><td>0.68292683</td><td>0.68292683</td><td>0.3658537 </td><td>0.70731707</td></tr>\n",
"\t<tr><td>40 </td><td>0.78048780</td><td>0.73170732</td><td>0 </td><td>0.78048780</td><td>0.78048780</td><td>0.5609756 </td><td>0.80487805</td></tr>\n",
"\t<tr><td>48 </td><td>0.82926829</td><td>0.85365854</td><td>0 </td><td>0.82926829</td><td>0.82926829</td><td>0.7560976 </td><td>0.90243902</td></tr>\n",
"\t<tr><td>56 </td><td>0.95121951</td><td>0.92682927</td><td>0 </td><td>0.95121951</td><td>0.95121951</td><td>0.9512195 </td><td>0.97560976</td></tr>\n",
"\t<tr><td>64 </td><td>1.00000000</td><td>0.97560976</td><td>0 </td><td>1.00000000</td><td>1.00000000</td><td>0.9756098 </td><td>1.00000000</td></tr>\n",
"\t<tr><td>72 </td><td>1.00000000</td><td>0.97560976</td><td>0 </td><td>1.00000000</td><td>1.00000000</td><td>0.9756098 </td><td>1.00000000</td></tr>\n",
"\t<tr><td>80 </td><td>1.00000000</td><td>1.00000000</td><td>0 </td><td>1.00000000</td><td>1.00000000</td><td>1.0000000 </td><td>1.00000000</td></tr>\n",
"\t<tr><td>100 </td><td>1.00000000</td><td>1.00000000</td><td>0 </td><td>1.00000000</td><td>1.00000000</td><td>1.0000000 </td><td>1.00000000</td></tr>\n",
"\t<tr><td>112 </td><td>1.00000000</td><td>1.00000000</td><td>0 </td><td>1.00000000</td><td>1.00000000</td><td>1.0000000 </td><td>1.00000000</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllllll}\n",
" topRatio & fpof & lfpof & fpcof & wfpof & wcfpof & mfpof & fpi\\\\\n",
"\\hline\n",
"\t 4 & 0.07317073 & 0.07317073 & 0 & 0.07317073 & 0.07317073 & 0.0000000 & 0.07317073\\\\\n",
"\t 8 & 0.17073171 & 0.17073171 & 0 & 0.17073171 & 0.17073171 & 0.0000000 & 0.17073171\\\\\n",
"\t 16 & 0.34146341 & 0.31707317 & 0 & 0.34146341 & 0.34146341 & 0.0000000 & 0.34146341\\\\\n",
"\t 24 & 0.48780488 & 0.51219512 & 0 & 0.48780488 & 0.48780488 & 0.1707317 & 0.51219512\\\\\n",
"\t 32 & 0.68292683 & 0.70731707 & 0 & 0.68292683 & 0.68292683 & 0.3658537 & 0.70731707\\\\\n",
"\t 40 & 0.78048780 & 0.73170732 & 0 & 0.78048780 & 0.78048780 & 0.5609756 & 0.80487805\\\\\n",
"\t 48 & 0.82926829 & 0.85365854 & 0 & 0.82926829 & 0.82926829 & 0.7560976 & 0.90243902\\\\\n",
"\t 56 & 0.95121951 & 0.92682927 & 0 & 0.95121951 & 0.95121951 & 0.9512195 & 0.97560976\\\\\n",
"\t 64 & 1.00000000 & 0.97560976 & 0 & 1.00000000 & 1.00000000 & 0.9756098 & 1.00000000\\\\\n",
"\t 72 & 1.00000000 & 0.97560976 & 0 & 1.00000000 & 1.00000000 & 0.9756098 & 1.00000000\\\\\n",
"\t 80 & 1.00000000 & 1.00000000 & 0 & 1.00000000 & 1.00000000 & 1.0000000 & 1.00000000\\\\\n",
"\t 100 & 1.00000000 & 1.00000000 & 0 & 1.00000000 & 1.00000000 & 1.0000000 & 1.00000000\\\\\n",
"\t 112 & 1.00000000 & 1.00000000 & 0 & 1.00000000 & 1.00000000 & 1.0000000 & 1.00000000\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"topRatio | fpof | lfpof | fpcof | wfpof | wcfpof | mfpof | fpi | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| 4 | 0.07317073 | 0.07317073 | 0 | 0.07317073 | 0.07317073 | 0.0000000 | 0.07317073 | \n",
"| 8 | 0.17073171 | 0.17073171 | 0 | 0.17073171 | 0.17073171 | 0.0000000 | 0.17073171 | \n",
"| 16 | 0.34146341 | 0.31707317 | 0 | 0.34146341 | 0.34146341 | 0.0000000 | 0.34146341 | \n",
"| 24 | 0.48780488 | 0.51219512 | 0 | 0.48780488 | 0.48780488 | 0.1707317 | 0.51219512 | \n",
"| 32 | 0.68292683 | 0.70731707 | 0 | 0.68292683 | 0.68292683 | 0.3658537 | 0.70731707 | \n",
"| 40 | 0.78048780 | 0.73170732 | 0 | 0.78048780 | 0.78048780 | 0.5609756 | 0.80487805 | \n",
"| 48 | 0.82926829 | 0.85365854 | 0 | 0.82926829 | 0.82926829 | 0.7560976 | 0.90243902 | \n",
"| 56 | 0.95121951 | 0.92682927 | 0 | 0.95121951 | 0.95121951 | 0.9512195 | 0.97560976 | \n",
"| 64 | 1.00000000 | 0.97560976 | 0 | 1.00000000 | 1.00000000 | 0.9756098 | 1.00000000 | \n",
"| 72 | 1.00000000 | 0.97560976 | 0 | 1.00000000 | 1.00000000 | 0.9756098 | 1.00000000 | \n",
"| 80 | 1.00000000 | 1.00000000 | 0 | 1.00000000 | 1.00000000 | 1.0000000 | 1.00000000 | \n",
"| 100 | 1.00000000 | 1.00000000 | 0 | 1.00000000 | 1.00000000 | 1.0000000 | 1.00000000 | \n",
"| 112 | 1.00000000 | 1.00000000 | 0 | 1.00000000 | 1.00000000 | 1.0000000 | 1.00000000 | \n",
"\n",
"\n"
],
"text/plain": [
" topRatio fpof lfpof fpcof wfpof wcfpof mfpof \n",
"1 4 0.07317073 0.07317073 0 0.07317073 0.07317073 0.0000000\n",
"2 8 0.17073171 0.17073171 0 0.17073171 0.17073171 0.0000000\n",
"3 16 0.34146341 0.31707317 0 0.34146341 0.34146341 0.0000000\n",
"4 24 0.48780488 0.51219512 0 0.48780488 0.48780488 0.1707317\n",
"5 32 0.68292683 0.70731707 0 0.68292683 0.68292683 0.3658537\n",
"6 40 0.78048780 0.73170732 0 0.78048780 0.78048780 0.5609756\n",
"7 48 0.82926829 0.85365854 0 0.82926829 0.82926829 0.7560976\n",
"8 56 0.95121951 0.92682927 0 0.95121951 0.95121951 0.9512195\n",
"9 64 1.00000000 0.97560976 0 1.00000000 1.00000000 0.9756098\n",
"10 72 1.00000000 0.97560976 0 1.00000000 1.00000000 0.9756098\n",
"11 80 1.00000000 1.00000000 0 1.00000000 1.00000000 1.0000000\n",
"12 100 1.00000000 1.00000000 0 1.00000000 1.00000000 1.0000000\n",
"13 112 1.00000000 1.00000000 0 1.00000000 1.00000000 1.0000000\n",
" fpi \n",
"1 0.07317073\n",
"2 0.17073171\n",
"3 0.34146341\n",
"4 0.51219512\n",
"5 0.70731707\n",
"6 0.80487805\n",
"7 0.90243902\n",
"8 0.97560976\n",
"9 1.00000000\n",
"10 1.00000000\n",
"11 1.00000000\n",
"12 1.00000000\n",
"13 1.00000000"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"results <- prepareResults()\n",
"results"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>fpof</th><th scope=col>lfpof</th><th scope=col>fpcof</th><th scope=col>wfpof</th><th scope=col>wcfpof</th><th scope=col>mfpof</th><th scope=col>fpi</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>0.9900682 </td><td>0.9892100 </td><td>0.5 </td><td>0.9900682 </td><td>0.9900682 </td><td>0.9799233 </td><td>0.008014698</td></tr>\n",
"\t<tr><td>0.9900682 </td><td>0.9903611 </td><td>0.5 </td><td>0.9900682 </td><td>0.9900682 </td><td>0.9799233 </td><td>0.991985302</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllllll}\n",
" fpof & lfpof & fpcof & wfpof & wcfpof & mfpof & fpi\\\\\n",
"\\hline\n",
"\t 0.9900682 & 0.9892100 & 0.5 & 0.9900682 & 0.9900682 & 0.9799233 & 0.008014698\\\\\n",
"\t 0.9900682 & 0.9903611 & 0.5 & 0.9900682 & 0.9900682 & 0.9799233 & 0.991985302\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"fpof | lfpof | fpcof | wfpof | wcfpof | mfpof | fpi | \n",
"|---|---|\n",
"| 0.9900682 | 0.9892100 | 0.5 | 0.9900682 | 0.9900682 | 0.9799233 | 0.008014698 | \n",
"| 0.9900682 | 0.9903611 | 0.5 | 0.9900682 | 0.9900682 | 0.9799233 | 0.991985302 | \n",
"\n",
"\n"
],
"text/plain": [
" fpof lfpof fpcof wfpof wcfpof mfpof fpi \n",
"1 0.9900682 0.9892100 0.5 0.9900682 0.9900682 0.9799233 0.008014698\n",
"2 0.9900682 0.9903611 0.5 0.9900682 0.9900682 0.9799233 0.991985302"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"aucs <- auc()\n",
"aucs"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<strong>pdf:</strong> 3"
],
"text/latex": [
"\\textbf{pdf:} 3"
],
"text/markdown": [
"**pdf:** 3"
],
"text/plain": [
"pdf \n",
" 3 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<strong>pdf:</strong> 2"
],
"text/latex": [
"\\textbf{pdf:} 2"
],
"text/markdown": [
"**pdf:** 2"
],
"text/plain": [
"pdf \n",
" 2 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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0e+V5Z+Q6u9Xb6VeFJic/2NWibrcjc8SJbxstF8r6Q+v5Eo8Jrpykumr6vqDZNe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\nRRICCCCAAAIIIIAAAggggAACCCDgKXCG5NoNCTq82LMUmQgkT+A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snI0i9HFfuQidbFWa6dZRuZVW53Mtq9M4a8c2sotsZhsVKPxL5fAu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JQgLVMzREAEREAEREAEapkAr80CzjG4lhVH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\ncdwzatbscjSGx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SrHtA77Aqt6efrp3R6GpbXQh09uw80xMOF1yOyGUQ/wTo4kmg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\nIECAAAECBAgQIECAwASB/wKYO1nC0j5M7AAAAABJRU5ErkJggg==",
"text/plain": [
"Plot with title “Breast Cancer Wisconsin (breast-w)”"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"options(repr.plot.width=10)\n",
"options(repr.plot.height = 7)\n",
"plotResults(results, \"Breast Cancer Wisconsin (breast-w)\")\n",
"dev.copy(pdf,'breast-w.pdf', width = 10, height = 7)\n",
"dev.off()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# # generate latex table\n",
"# library(Hmisc)\n",
"# for(col in colnames(results)){\n",
"# if(\"topRatio\"!=col) {\n",
"# results[[col]] = round(results[[col]],2)\n",
"# }\n",
"# }\n",
"# latex(results, file=\"breast-w.tex\",rowname=NULL,table.env=FALSE)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lymphography:lymph"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# file to load\n",
"fileName <- \"./lymph.csv\"\n",
"# predefined top-k ratios to check\n",
"topRatio <- c(3,5,7,8,9,10,15,16,22,30)\n",
"# minimum support and maximum length of a frequent pattern\n",
"ms <- 0.1\n",
"ml <- 5\n",
"# name of attribute with class\n",
"className <- \"class\"\n",
"# class values as anomalies\n",
"anomalyClasses <- c(\"anomaly\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# load csv\n",
"data <- read.csv(fileName)\n",
"# preprocessing\n",
"classes <- as.character(data$class)\n",
"# rename classes\n",
"classes[which(classes==\"normal\" | classes==\"fibrosis\")] <- \"anomaly\"\n",
"classes[which(classes!=\"anomaly\")] <- \"normal\"\n",
"data$class <- as.factor(classes)\n",
"# remember labels\n",
"labels <- data[[className]]\n",
"# delete class atribute\n",
"data[[className]] <- NULL"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"Overview of classes:"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>frequency</th><th scope=col>percentage</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>anomaly</th><td>6 </td><td>4.05 % </td></tr>\n",
"\t<tr><th scope=row>normal</th><td>142 </td><td>95.95 %</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|ll}\n",
" & frequency & percentage\\\\\n",
"\\hline\n",
"\tanomaly & 6 & 4.05 \\% \\\\\n",
"\tnormal & 142 & 95.95 \\%\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| <!--/--> | frequency | percentage | \n",
"|---|---|\n",
"| anomaly | 6 | 4.05 % | \n",
"| normal | 142 | 95.95 % | \n",
"\n",
"\n"
],
"text/plain": [
" frequency percentage\n",
"anomaly 6 4.05 % \n",
"normal 142 95.95 % "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"IRdisplay::display_markdown(\"Overview of classes:\")\n",
"valueSummary(labels)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Apriori\n",
"\n",
"Parameter specification:\n",
" confidence minval smax arem aval originalSupport maxtime support minlen\n",
" NA 0.1 1 none FALSE TRUE 5 0.1 1\n",
" maxlen target ext\n",
" 5 frequent itemsets FALSE\n",
"\n",
"Algorithmic control:\n",
" filter tree heap memopt load sort verbose\n",
" 0.1 TRUE TRUE FALSE TRUE 2 TRUE\n",
"\n",
"Absolute minimum support count: 14 \n",
"\n",
"set item appearances ...[0 item(s)] done [0.00s].\n",
"set transactions ...[59 item(s), 148 transaction(s)] done [0.00s].\n",
"sorting and recoding items ... [41 item(s)] done [0.00s].\n",
"creating transaction tree ... done [0.00s].\n",
"checking subsets of size 1 2 3 4 5 done [0.01s].\n",
"writing ... [18088 set(s)] done [0.00s].\n",
"creating S4 object ... done [0.01s].\n",
"Apriori\n",
"\n",
"Parameter specification:\n",
" confidence minval smax arem aval originalSupport maxtime support minlen\n",
" NA 0.1 1 none FALSE TRUE 5 0.1 1\n",
" maxlen target ext\n",
" 5 frequent itemsets FALSE\n",
"\n",
"Algorithmic control:\n",
" filter tree heap memopt load sort verbose\n",
" 0.1 TRUE TRUE FALSE TRUE 2 TRUE\n",
"\n",
"Absolute minimum support count: 14 \n",
"\n",
"set item appearances ...[0 item(s)] done [0.00s].\n",
"set transactions ...[59 item(s), 148 transaction(s)] done [0.00s].\n",
"sorting and recoding items ... [41 item(s)] done [0.00s].\n",
"creating transaction tree ... done [0.00s].\n",
"checking subsets of size 1 2 3 4 5 done [0.01s].\n",
"writing ... [18088 set(s)] done [0.00s].\n",
"creating S4 object ... done [0.00s].\n",
"Apriori\n",
"\n",
"Parameter specification:\n",
" confidence minval smax arem aval originalSupport maxtime support minlen\n",
" NA 0.1 1 none FALSE TRUE 5 0.1 1\n",
" maxlen target ext\n",
" 5 frequent itemsets FALSE\n",
"\n",
"Algorithmic control:\n",
" filter tree heap memopt load sort verbose\n",
" 0.1 TRUE TRUE FALSE TRUE 2 TRUE\n",
"\n",
"Absolute minimum support count: 14 \n",
"\n",
"set item appearances ...[0 item(s)] done [0.00s].\n",
"set transactions ...[59 item(s), 148 transaction(s)] done [0.00s].\n",
"sorting and recoding items ... [41 item(s)] done [0.00s].\n",
"creating transaction tree ... done [0.00s].\n",
"checking subsets of size 1 2 3 4 5 done [0.01s].\n",
"writing ... [18088 set(s)] done [0.00s].\n",
"creating S4 object ... done [0.00s].\n",
"Apriori\n",
"\n",
"Parameter specification:\n",
" confidence minval smax arem aval originalSupport maxtime support minlen\n",
" NA 0.1 1 none FALSE TRUE 5 0.1 1\n",
" maxlen target ext\n",
" 5 frequent itemsets FALSE\n",
"\n",
"Algorithmic control:\n",
" filter tree heap memopt load sort verbose\n",
" 0.1 TRUE TRUE FALSE TRUE 2 TRUE\n",
"\n",
"Absolute minimum support count: 14 \n",
"\n",
"set item appearances ...[0 item(s)] done [0.00s].\n",
"set transactions ...[59 item(s), 148 transaction(s)] done [0.00s].\n",
"sorting and recoding items ... [41 item(s)] done [0.00s].\n",
"creating transaction tree ... done [0.00s].\n",
"checking subsets of size 1 2 3 4 5 done [0.01s].\n",
"writing ... [18088 set(s)] done [0.00s].\n",
"creating S4 object ... done [0.01s].\n",
"Apriori\n",
"\n",
"Parameter specification:\n",
" confidence minval smax arem aval originalSupport maxtime support minlen\n",
" NA 0.1 1 none FALSE TRUE 5 0.1 1\n",
" maxlen target ext\n",
" 5 frequent itemsets FALSE\n",
"\n",
"Algorithmic control:\n",
" filter tree heap memopt load sort verbose\n",
" 0.1 TRUE TRUE FALSE TRUE 2 TRUE\n",
"\n",
"Absolute minimum support count: 14 \n",
"\n",
"set item appearances ...[0 item(s)] done [0.00s].\n",
"set transactions ...[59 item(s), 148 transaction(s)] done [0.00s].\n",
"sorting and recoding items ... [41 item(s)] done [0.00s].\n",
"creating transaction tree ... done [0.00s].\n",
"checking subsets of size 1 2 3 4 5 done [0.01s].\n",
"writing ... [18088 set(s)] done [0.00s].\n",
"creating S4 object ... done [0.01s].\n",
"Apriori\n",
"\n",
"Parameter specification:\n",
" confidence minval smax arem aval originalSupport maxtime support minlen\n",
" NA 0.1 1 none FALSE TRUE 5 0.1 1\n",
" maxlen target ext\n",
" 5 frequent itemsets FALSE\n",
"\n",
"Algorithmic control:\n",
" filter tree heap memopt load sort verbose\n",
" 0.1 TRUE TRUE FALSE TRUE 2 TRUE\n",
"\n",
"Absolute minimum support count: 14 \n",
"\n",
"set item appearances ...[0 item(s)] done [0.00s].\n",
"set transactions ...[59 item(s), 148 transaction(s)] done [0.00s].\n",
"sorting and recoding items ... [41 item(s)] done [0.00s].\n",
"creating transaction tree ... done [0.00s].\n",
"checking subsets of size 1 2 3 4 5 done [0.01s].\n",
"writing ... [18088 set(s)] done [0.00s].\n",
"creating S4 object ... done [0.00s].\n",
"Apriori\n",
"\n",
"Parameter specification:\n",
" confidence minval smax arem aval originalSupport maxtime support minlen\n",
" NA 0.1 1 none FALSE TRUE 5 0.1 1\n",
" maxlen target ext\n",
" 5 frequent itemsets FALSE\n",
"\n",
"Algorithmic control:\n",
" filter tree heap memopt load sort verbose\n",
" 0.1 TRUE TRUE FALSE TRUE 2 TRUE\n",
"\n",
"Absolute minimum support count: 14 \n",
"\n",
"set item appearances ...[0 item(s)] done [0.00s].\n",
"set transactions ...[59 item(s), 148 transaction(s)] done [0.00s].\n",
"sorting and recoding items ... [41 item(s)] done [0.00s].\n",
"creating transaction tree ... done [0.00s].\n",
"checking subsets of size 1 2 3 4 5 done [0.01s].\n",
"writing ... [18088 set(s)] done [0.00s].\n",
"creating S4 object ... done [0.00s].\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"FI lengths: 0.000357151031494141\n",
"FI qualities: 0.00989389419555664\n",
"FI coverages: 1.08302593231201\n",
"FI multiply: 0.0110659599304199\n",
"Penalization: 0.0101790428161621\n",
"Means: 0.577965021133423\n"
]
}
],
"source": [
"computeScores()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>topRatio</th><th scope=col>fpof</th><th scope=col>lfpof</th><th scope=col>fpcof</th><th scope=col>wfpof</th><th scope=col>wcfpof</th><th scope=col>mfpof</th><th scope=col>fpi</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>3 </td><td>0.5000000</td><td>0.1666667</td><td>0.1666667</td><td>0.5000000</td><td>0.5000000</td><td>0.5000000</td><td>0.5000000</td></tr>\n",
"\t<tr><td>5 </td><td>0.6666667</td><td>0.1666667</td><td>0.1666667</td><td>0.6666667</td><td>0.6666667</td><td>0.6666667</td><td>0.8333333</td></tr>\n",
"\t<tr><td>7 </td><td>0.8333333</td><td>0.1666667</td><td>0.1666667</td><td>0.8333333</td><td>0.8333333</td><td>0.6666667</td><td>0.8333333</td></tr>\n",
"\t<tr><td>8 </td><td>0.8333333</td><td>0.1666667</td><td>0.1666667</td><td>0.8333333</td><td>0.8333333</td><td>0.8333333</td><td>0.8333333</td></tr>\n",
"\t<tr><td>9 </td><td>0.8333333</td><td>0.1666667</td><td>0.1666667</td><td>0.8333333</td><td>0.8333333</td><td>0.8333333</td><td>1.0000000</td></tr>\n",
"\t<tr><td>10 </td><td>0.8333333</td><td>0.1666667</td><td>0.1666667</td><td>0.8333333</td><td>0.8333333</td><td>0.8333333</td><td>1.0000000</td></tr>\n",
"\t<tr><td>15 </td><td>0.8333333</td><td>0.1666667</td><td>0.1666667</td><td>0.8333333</td><td>1.0000000</td><td>0.8333333</td><td>1.0000000</td></tr>\n",
"\t<tr><td>16 </td><td>1.0000000</td><td>0.1666667</td><td>0.1666667</td><td>1.0000000</td><td>1.0000000</td><td>0.8333333</td><td>1.0000000</td></tr>\n",
"\t<tr><td>22 </td><td>1.0000000</td><td>0.1666667</td><td>0.1666667</td><td>1.0000000</td><td>1.0000000</td><td>1.0000000</td><td>1.0000000</td></tr>\n",
"\t<tr><td>30 </td><td>1.0000000</td><td>0.1666667</td><td>0.1666667</td><td>1.0000000</td><td>1.0000000</td><td>1.0000000</td><td>1.0000000</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllllll}\n",
" topRatio & fpof & lfpof & fpcof & wfpof & wcfpof & mfpof & fpi\\\\\n",
"\\hline\n",
"\t 3 & 0.5000000 & 0.1666667 & 0.1666667 & 0.5000000 & 0.5000000 & 0.5000000 & 0.5000000\\\\\n",
"\t 5 & 0.6666667 & 0.1666667 & 0.1666667 & 0.6666667 & 0.6666667 & 0.6666667 & 0.8333333\\\\\n",
"\t 7 & 0.8333333 & 0.1666667 & 0.1666667 & 0.8333333 & 0.8333333 & 0.6666667 & 0.8333333\\\\\n",
"\t 8 & 0.8333333 & 0.1666667 & 0.1666667 & 0.8333333 & 0.8333333 & 0.8333333 & 0.8333333\\\\\n",
"\t 9 & 0.8333333 & 0.1666667 & 0.1666667 & 0.8333333 & 0.8333333 & 0.8333333 & 1.0000000\\\\\n",
"\t 10 & 0.8333333 & 0.1666667 & 0.1666667 & 0.8333333 & 0.8333333 & 0.8333333 & 1.0000000\\\\\n",
"\t 15 & 0.8333333 & 0.1666667 & 0.1666667 & 0.8333333 & 1.0000000 & 0.8333333 & 1.0000000\\\\\n",
"\t 16 & 1.0000000 & 0.1666667 & 0.1666667 & 1.0000000 & 1.0000000 & 0.8333333 & 1.0000000\\\\\n",
"\t 22 & 1.0000000 & 0.1666667 & 0.1666667 & 1.0000000 & 1.0000000 & 1.0000000 & 1.0000000\\\\\n",
"\t 30 & 1.0000000 & 0.1666667 & 0.1666667 & 1.0000000 & 1.0000000 & 1.0000000 & 1.0000000\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"topRatio | fpof | lfpof | fpcof | wfpof | wcfpof | mfpof | fpi | \n",
"|---|---|---|---|---|---|---|---|---|---|\n",
"| 3 | 0.5000000 | 0.1666667 | 0.1666667 | 0.5000000 | 0.5000000 | 0.5000000 | 0.5000000 | \n",
"| 5 | 0.6666667 | 0.1666667 | 0.1666667 | 0.6666667 | 0.6666667 | 0.6666667 | 0.8333333 | \n",
"| 7 | 0.8333333 | 0.1666667 | 0.1666667 | 0.8333333 | 0.8333333 | 0.6666667 | 0.8333333 | \n",
"| 8 | 0.8333333 | 0.1666667 | 0.1666667 | 0.8333333 | 0.8333333 | 0.8333333 | 0.8333333 | \n",
"| 9 | 0.8333333 | 0.1666667 | 0.1666667 | 0.8333333 | 0.8333333 | 0.8333333 | 1.0000000 | \n",
"| 10 | 0.8333333 | 0.1666667 | 0.1666667 | 0.8333333 | 0.8333333 | 0.8333333 | 1.0000000 | \n",
"| 15 | 0.8333333 | 0.1666667 | 0.1666667 | 0.8333333 | 1.0000000 | 0.8333333 | 1.0000000 | \n",
"| 16 | 1.0000000 | 0.1666667 | 0.1666667 | 1.0000000 | 1.0000000 | 0.8333333 | 1.0000000 | \n",
"| 22 | 1.0000000 | 0.1666667 | 0.1666667 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | \n",
"| 30 | 1.0000000 | 0.1666667 | 0.1666667 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | \n",
"\n",
"\n"
],
"text/plain": [
" topRatio fpof lfpof fpcof wfpof wcfpof mfpof \n",
"1 3 0.5000000 0.1666667 0.1666667 0.5000000 0.5000000 0.5000000\n",
"2 5 0.6666667 0.1666667 0.1666667 0.6666667 0.6666667 0.6666667\n",
"3 7 0.8333333 0.1666667 0.1666667 0.8333333 0.8333333 0.6666667\n",
"4 8 0.8333333 0.1666667 0.1666667 0.8333333 0.8333333 0.8333333\n",
"5 9 0.8333333 0.1666667 0.1666667 0.8333333 0.8333333 0.8333333\n",
"6 10 0.8333333 0.1666667 0.1666667 0.8333333 0.8333333 0.8333333\n",
"7 15 0.8333333 0.1666667 0.1666667 0.8333333 1.0000000 0.8333333\n",
"8 16 1.0000000 0.1666667 0.1666667 1.0000000 1.0000000 0.8333333\n",
"9 22 1.0000000 0.1666667 0.1666667 1.0000000 1.0000000 1.0000000\n",
"10 30 1.0000000 0.1666667 0.1666667 1.0000000 1.0000000 1.0000000\n",
" fpi \n",
"1 0.5000000\n",
"2 0.8333333\n",
"3 0.8333333\n",
"4 0.8333333\n",
"5 1.0000000\n",
"6 1.0000000\n",
"7 1.0000000\n",
"8 1.0000000\n",
"9 1.0000000\n",
"10 1.0000000"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"results <- prepareResults()\n",
"results"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>fpof</th><th scope=col>lfpof</th><th scope=col>fpcof</th><th scope=col>wfpof</th><th scope=col>wcfpof</th><th scope=col>mfpof</th><th scope=col>fpi</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>0.9859155 </td><td>0.5 </td><td>0.5 </td><td>0.9859155 </td><td>0.9906103 </td><td>0.9835681 </td><td>0.003521127</td></tr>\n",
"\t<tr><td>0.9859155 </td><td>0.5 </td><td>0.5 </td><td>0.9859155 </td><td>0.9906103 </td><td>0.9835681 </td><td>0.996478873</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllllll}\n",
" fpof & lfpof & fpcof & wfpof & wcfpof & mfpof & fpi\\\\\n",
"\\hline\n",
"\t 0.9859155 & 0.5 & 0.5 & 0.9859155 & 0.9906103 & 0.9835681 & 0.003521127\\\\\n",
"\t 0.9859155 & 0.5 & 0.5 & 0.9859155 & 0.9906103 & 0.9835681 & 0.996478873\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"fpof | lfpof | fpcof | wfpof | wcfpof | mfpof | fpi | \n",
"|---|---|\n",
"| 0.9859155 | 0.5 | 0.5 | 0.9859155 | 0.9906103 | 0.9835681 | 0.003521127 | \n",
"| 0.9859155 | 0.5 | 0.5 | 0.9859155 | 0.9906103 | 0.9835681 | 0.996478873 | \n",
"\n",
"\n"
],
"text/plain": [
" fpof lfpof fpcof wfpof wcfpof mfpof fpi \n",
"1 0.9859155 0.5 0.5 0.9859155 0.9906103 0.9835681 0.003521127\n",
"2 0.9859155 0.5 0.5 0.9859155 0.9906103 0.9835681 0.996478873"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"aucs <- auc()\n",
"aucs"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<strong>pdf:</strong> 3"
],
"text/latex": [
"\\textbf{pdf:} 3"
],
"text/markdown": [
"**pdf:** 3"
],
"text/plain": [
"pdf \n",
" 3 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<strong>pdf:</strong> 2"
],
"text/latex": [
"\\textbf{pdf:} 2"
],
"text/markdown": [
"**pdf:** 2"
],
"text/plain": [
"pdf \n",
" 2 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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d+4eWHa3+HZfk24QrU22f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",
"text/plain": [
"Plot with title “Lymphography (lymph)”"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"options(repr.plot.width=10)\n",
"options(repr.plot.height = 7)\n",
"plotResults(results, \"Lymphography (lymph)\")\n",
"dev.copy(pdf,'lymph.pdf', width = 10, height = 7)\n",
"dev.off()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# # generate latex table\n",
"# library(Hmisc)\n",
"# for(col in colnames(results)){\n",
"# if(\"topRatio\"!=col) {\n",
"# results[[col]] = round(results[[col]],2)\n",
"# }\n",
"# }\n",
"# latex(results, file=\"lymph.tex\",rowname=NULL,table.env=FALSE)"
]
}
],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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