Description Usage Arguments Value See Also Examples
The autoplot
function plots performance evaluation measures
by using ggplot2 instead of the general R plot.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ## S3 method for class 'sscurves'
autoplot(object, curvetype = c("ROC", "PRC"), ...)
## S3 method for class 'mscurves'
autoplot(object, curvetype = c("ROC", "PRC"), ...)
## S3 method for class 'smcurves'
autoplot(object, curvetype = c("ROC", "PRC"), ...)
## S3 method for class 'mmcurves'
autoplot(object, curvetype = c("ROC", "PRC"), ...)
## S3 method for class 'sspoints'
autoplot(object, curvetype = .get_metric_names("basic"),
...)
## S3 method for class 'mspoints'
autoplot(object, curvetype = .get_metric_names("basic"),
...)
## S3 method for class 'smpoints'
autoplot(object, curvetype = .get_metric_names("basic"),
...)
## S3 method for class 'mmpoints'
autoplot(object, curvetype = .get_metric_names("basic"),
...)
|
object |
An
See the Value section of | ||||||||||||||||||||||||||||||
curvetype |
A character vector with the following curve types.
| ||||||||||||||||||||||||||||||
... |
Following additional arguments can be specified.
|
The autoplot
function returns a ggplot
object
for a single-panel plot and a frame-grob object for a multiple-panel plot.
evalmod
for generating an S3
object.
fortify
for converting a curves and points object
to a data frame. plot
for plotting the equivalent curves
with the general R plot.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | ## Not run:
## Load libraries
library(ggplot2)
library(grid)
##################################################
### Single model & single test dataset
###
## Load a dataset with 10 positives and 10 negatives
data(P10N10)
## Generate an sscurve object that contains ROC and Precision-Recall curves
sscurves <- evalmod(scores = P10N10$scores, labels = P10N10$labels)
## Plot both ROC and Precision-Recall curves
autoplot(sscurves)
## Reduced/Full supporting points
sampss <- create_sim_samples(1, 50000, 50000)
evalss <- evalmod(scores = sampss$scores, labels = sampss$labels)
# Reduced supporting point
system.time(autoplot(evalss))
# Full supporting points
system.time(autoplot(evalss, reduce_points = FALSE))
## Get a grob object for multiple plots
pp1 <- autoplot(sscurves, ret_grob = TRUE)
plot.new()
grid.draw(pp1)
## A ROC curve
autoplot(sscurves, curvetype = "ROC")
## A Precision-Recall curve
autoplot(sscurves, curvetype = "PRC")
## Generate an sspoints object that contains basic evaluation measures
sspoints <- evalmod(mode = "basic", scores = P10N10$scores,
labels = P10N10$labels)
## Normalized ranks vs. basic evaluation measures
autoplot(sspoints)
## Normalized ranks vs. precision
autoplot(sspoints, curvetype = "precision")
##################################################
### Multiple models & single test dataset
###
## Create sample datasets with 100 positives and 100 negatives
samps <- create_sim_samples(1, 100, 100, "all")
mdat <- mmdata(samps[["scores"]], samps[["labels"]],
modnames = samps[["modnames"]])
## Generate an mscurve object that contains ROC and Precision-Recall curves
mscurves <- evalmod(mdat)
## ROC and Precision-Recall curves
autoplot(mscurves)
## Reduced/Full supporting points
sampms <- create_sim_samples(5, 50000, 50000)
evalms <- evalmod(scores = sampms$scores, labels = sampms$labels)
# Reduced supporting point
system.time(autoplot(evalms))
# Full supporting points
system.time(autoplot(evalms, reduce_points = FALSE))
## Hide the legend
autoplot(mscurves, show_legend = FALSE)
## Generate an mspoints object that contains basic evaluation measures
mspoints <- evalmod(mdat, mode = "basic")
## Normalized ranks vs. basic evaluation measures
autoplot(mspoints)
## Hide the legend
autoplot(mspoints, show_legend = FALSE)
##################################################
### Single model & multiple test datasets
###
## Create sample datasets with 100 positives and 100 negatives
samps <- create_sim_samples(10, 100, 100, "good_er")
mdat <- mmdata(samps[["scores"]], samps[["labels"]],
modnames = samps[["modnames"]],
dsids = samps[["dsids"]])
## Generate an smcurve object that contains ROC and Precision-Recall curves
smcurves <- evalmod(mdat, raw_curves = TRUE)
## Average ROC and Precision-Recall curves
autoplot(smcurves, raw_curves = FALSE)
## Hide confidence bounds
autoplot(smcurves, raw_curves = FALSE, show_cb = FALSE)
## Raw ROC and Precision-Recall curves
autoplot(smcurves, raw_curves = TRUE, show_cb = FALSE)
## Reduced/Full supporting points
sampsm <- create_sim_samples(4, 5000, 5000)
mdatsm <- mmdata(sampsm$scores, sampsm$labels, expd_first = "dsids")
evalsm <- evalmod(mdatsm, raw_curves = TRUE)
# Reduced supporting point
system.time(autoplot(evalsm, raw_curves = TRUE))
# Full supporting points
system.time(autoplot(evalsm, raw_curves = TRUE, reduce_points = FALSE))
## Generate an smpoints object that contains basic evaluation measures
smpoints <- evalmod(mdat, mode = "basic")
## Normalized ranks vs. average basic evaluation measures
autoplot(smpoints)
##################################################
### Multiple models & multiple test datasets
###
## Create sample datasets with 100 positives and 100 negatives
samps <- create_sim_samples(10, 100, 100, "all")
mdat <- mmdata(samps[["scores"]], samps[["labels"]],
modnames = samps[["modnames"]],
dsids = samps[["dsids"]])
## Generate an mscurve object that contains ROC and Precision-Recall curves
mmcurves <- evalmod(mdat, raw_curves = TRUE)
## Average ROC and Precision-Recall curves
autoplot(mmcurves, raw_curves = FALSE)
## Show confidence bounds
autoplot(mmcurves, raw_curves = FALSE, show_cb = TRUE)
## Raw ROC and Precision-Recall curves
autoplot(mmcurves, raw_curves = TRUE)
## Reduced/Full supporting points
sampmm <- create_sim_samples(4, 5000, 5000)
mdatmm <- mmdata(sampmm$scores, sampmm$labels, modnames = c("m1", "m2"),
dsids = c(1, 2), expd_first = "modnames")
evalmm <- evalmod(mdatmm, raw_curves = TRUE)
# Reduced supporting point
system.time(autoplot(evalmm, raw_curves = TRUE))
# Full supporting points
system.time(autoplot(evalmm, raw_curves = TRUE, reduce_points = FALSE))
## Generate an mmpoints object that contains basic evaluation measures
mmpoints <- evalmod(mdat, mode = "basic")
## Normalized ranks vs. average basic evaluation measures
autoplot(mmpoints)
##################################################
### N-fold cross validation datasets
###
## Load test data
data(M2N50F5)
## Speficy nessesary columns to create mdat
cvdat <- mmdata(nfold_df = M2N50F5, score_cols = c(1, 2),
lab_col = 3, fold_col = 4,
modnames = c("m1", "m2"), dsids = 1:5)
## Generate an mmcurve object that contains ROC and Precision-Recall curves
cvcurves <- evalmod(cvdat)
## Average ROC and Precision-Recall curves
autoplot(cvcurves)
## Show confidence bounds
autoplot(cvcurves, show_cb = TRUE)
## Generate an mmpoints object that contains basic evaluation measures
cvpoints <- evalmod(cvdat, mode = "basic")
## Normalized ranks vs. average basic evaluation measures
autoplot(cvpoints)
## End(Not run)
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