/
eval-performance.Rmd
357 lines (319 loc) · 10.6 KB
/
eval-performance.Rmd
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
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
---
title: "Evaluation of performances"
output:
workflowr::wflow_html:
includes:
in_header: header.html
editor_options:
chunk_output_type: console
author: "Patrick Schratz"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
fig.retina = 3,
fig.align = "center",
fig.width = 6.93,
fig.height = 6.13,
out.width = "100%",
echo = FALSE
)
R.utils::sourceDirectory("R")
library("drake")
# load drake objects
loadd(
bm_aggregated_new_buffer2
)
library("xtable")
library("flextable")
library("ggbeeswarm")
library("ggsci")
library("ggrepel")
library("ggpubr")
library("here")
library("mlr")
library("dplyr")
library("forcats")
```
Last update:
```{r}
date()
```
```{r, warning = FALSE}
df_perf <- getBMRPerformances(bm_aggregated_new_buffer2, as.df = T) %>%
mutate(task.id = recode_factor(task.id,
`hr_buffer2` = "HR",
`vi_buffer2` = "VI",
`nri_buffer2` = "NRI",
`nri_vi_buffer2` = "NRI-VI",
`hr_nri_buffer2` = "HR-NRI",
`hr_vi_buffer2` = "HR-VI",
`hr_nri_vi_buffer2` = "HR-NRI-VI",
)) %>%
tidyr::separate(learner.id, c("learner_group", "filter"),
remove = FALSE,
sep = " MBO "
)
```
```{r aggr-perf}
# Aggregate performances and add standard error column.
df_perf %<>%
group_by(task.id, learner.id, filter) %>%
mutate(rmse_aggr = round(mean(rmse), 3)) %>%
mutate(se = round(sd(rmse), 3)) %>%
select(-rmse, iter) %>%
ungroup()
```
Fold performances of "SVM MBO No Filter" on the HR Task
- Fold 1: Luiando
- Fold 2: Laukiz1
- Fold 3: Laukiz2
- Fold 4: Oiartzun
```{r}
df <- mlr::getBMRPerformances(bm_aggregated_new_buffer2,
"hr_buffer2", "SVM MBO No Filter",
as.df = TRUE
)
df_tab <- df %>%
dplyr::select(iter, rmse) %>%
dplyr::rename(Plot = iter, RMSE = rmse) %>%
dplyr::mutate(Plot = as.character(Plot)) %>%
dplyr::mutate(Plot = forcats::fct_recode(Plot,
Laukiz1 = "2", Laukiz2 = "3",
Luiando = "1", Oiartzun = "4"
)) %>%
dplyr::mutate(RMSE = round(RMSE, 2))
df_tab %>%
xtable::xtable(
type = "latex",
caption = "Single fold performances for learner SVM on the HR dataset without using a filter.",
label = "tab:svm-single-fold-perf"
) %>%
print(
file = here::here("docs/00-manuscripts/ieee/performance-svm-single-plot.tex"),
include.rownames = TRUE,
latex.environments = c("center"),
table.placement = "ht!",
caption.placement = "top",
timestamp = NULL
)
saveRDS(df_tab, here("docs/00-manuscripts/presentation/table-svm-single-plot.rda"))
flextable::flextable(df_tab)
```
## (Table) T1 All leaner/filter/task combinations ordered by performance.
Overall leaderboard across all settings, sorted descending by performance.
```{r eval-performance-1, warning=FALSE}
table1 <- df_perf %>%
group_by(learner.id, task.id, filter) %>%
slice(which.min(rmse_aggr)) %>%
dplyr::rename(
"Model" = learner_group,
"Learner ID" = learner.id,
"Task" = task.id,
"Filter" = filter,
"RMSE" = rmse_aggr,
"SE" = se,
) %>%
ungroup() %>%
mutate(Filter = replace(Filter, is.na(Filter), "No Filter")) %>%
select(-iter, -`Learner ID`) %>%
arrange(RMSE)
# save as latex table
table1 %>%
ungroup() %>%
arrange(RMSE) %>%
slice(1:10) %>%
xtable(
type = "latex",
caption = "Top 10 results for any task/learner/filter combination, sorted by performance.",
label = "tab:perf-top-10"
) %>%
print(
file = here("docs/00-manuscripts/ieee/performance-top-10.tex"),
include.rownames = TRUE,
latex.environments = c("center"),
table.placement = "ht!",
caption.placement = "top",
timestamp = NULL
)
saveRDS(table1, here("docs/00-manuscripts/presentation/table-perf.rda"))
table1 %>%
flextable() %>%
autofit()
```
## (Table) T2 Best learner/filter/task combination
Learners: On which task and using which filter did every learner score their best result on?
*CV: L2 penalized regression using the internal 10-fold CV tuning of the `glmnet` package
*MBO: L2 penalized regression using using MBO for hyperparameter optimization.
```{r eval-performance-2, warning=FALSE}
table2 <- df_perf %>%
group_by(learner_group) %>%
slice(which.min(rmse_aggr)) %>%
mutate(filter = replace(filter, is.na(filter), "No Filter")) %>%
arrange(rmse_aggr) %>%
dplyr::rename(
"Model" = learner_group,
"Learner ID" = learner.id,
"Task" = task.id,
"Filter" = filter,
"RMSE" = rmse_aggr,
"SE" = se,
) %>%
select(-iter) %>%
select(-`Learner ID`)
# save as latex table
table2 %>%
xtable(
type = "latex",
caption = "Best performance of each learner across any task and filter method.",
label = "tab:best-learner-perf"
) %>%
print(
file = here("docs/00-manuscripts/ieee/performance-best-per-learner.tex"),
include.rownames = TRUE,
latex.environments = c("center"),
table.placement = "ht!",
scalebox = 0.90,
caption.placement = "top",
timestamp = NULL
)
saveRDS(table2, here("docs/00-manuscripts/presentation/table-best-learner-per-task.rda"))
table2 %>%
flextable() %>%
autofit()
```
## (Plot) P1 Best learner/filter combs for all tasks
```{r performance-results, warning=FALSE, dev = c("png", "pdf")}
results_aggr <- df_perf %>%
mutate(filter = replace(filter, is.na(filter), "NF")) %>%
mutate(learner_group = recode_factor(learner_group, `XG` = "XGBOOST")) %>%
group_by(learner_group, filter, task.id) %>%
### get the best performance per learner and task
# this group_by() & slice() approach is better than summarise() because we can
# keep additional columns
# in constrast to summarise which only keeps the grouping columns and the
# summarised one
slice(which.min(rmse_aggr)) %>% # this groups the CV iters
ungroup() %>%
group_by(task.id, learner_group) %>%
slice(which.min(rmse_aggr))
results_aggr %>%
ggplot(aes(x = rmse_aggr, y = task.id)) +
# geom_jitter(aes(color = learner_group), size = 2, width = 0, height = 0.3) +
# geom_dotplot(aes(fill = learner_group), binaxis="y",
# stackdir="up") +
geom_beeswarm(groupOnX = FALSE, aes(color = learner_group), size = 3) +
scale_color_nejm(breaks = sort(levels(results_aggr$learner_group))) +
labs(x = "RMSE", y = "Task", color = "Learner") +
guides(size = FALSE) +
scale_x_continuous(limits = c(27, 47)) +
geom_label_repel(
# subset data to remove out of bounds values
data = results_aggr[results_aggr$rmse_aggr < 100, ],
# from ggbeeswarm, avoid overlapping of points by labels
position = position_quasirandom(),
aes(label = paste0(filter, ",", round(rmse_aggr, 2))),
size = 4,
min.segment.length = 0.1,
seed = 123,
point.padding = 0.5
) +
theme_pubr(base_size = 14) +
theme(
panel.grid.major.y = element_line(size = 0.1, linetype = "dashed"),
axis.title.y = element_blank(),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
axis.text.y = element_text(angle = 45),
plot.margin = unit(c(6, 6, 6, 0), "pt")
)
```
## (Plot) P2 Scatterplots of filter methods vs. no filter for each learner and task
Showing the final effect of applying feature selection to a learner for each task.
All filters are colored in the same way whereas using "no filter" appears in a different color.
```{r filter-effect-all-vs-no-filter, warning=FALSE, dev = c("png", "pdf")}
results_aggr1 <- df_perf %>%
filter(learner_group != "Ridge-CV") %>%
filter(learner_group != "Lasso-CV") %>%
# mutate(filter = replace(filter, "No Filter", "NF")) %>%
mutate(learner_group = as.factor(learner_group)) %>%
mutate(learner_group = recode(learner_group, `XGBoost` = "XG")) %>%
mutate(filter = recode(filter, `No Filter` = "NF")) %>%
mutate(learner_group = fct_rev(learner_group)) %>%
group_by(learner_group, task.id, filter) %>%
# we actually took the mean already in chunk 'aggr-perf'. This is only to get
# summarise() working
summarise(perf = mean(rmse_aggr)) %>%
ungroup() %>%
# we need to reverse the order on porpuse here so that ggplot reverses it
# again later
mutate(learner_group = fct_relevel(learner_group, "XGBOOST", "SVM", "RF"))
results_aggr1 %>%
ggplot(aes(x = perf, y = learner_group)) +
geom_beeswarm(
data = results_aggr1[results_aggr1$filter != "NF", ], size = 2.2, shape = 3,
groupOnX = FALSE, aes(color = "Filter")
) +
geom_point(
data = results_aggr1[results_aggr1$filter == "NF", ],
size = 2.2, shape = 19, aes(color = "No Filter")
) +
facet_wrap(~task.id) +
scale_color_nejm(guide = guide_legend(override.aes = list(shape = c(3, 19)))) +
labs(x = "RMSE", y = "Task", colour = NULL) +
guides(size = FALSE) +
scale_x_continuous(limits = c(27, 51)) +
theme_pubr(base_size = 14) +
theme(
panel.grid.major.y = element_line(size = 0.1, linetype = "dashed"),
axis.title.y = element_blank(),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
)
```
## (Plot) P3 Scatterplots of filter methods vs. Borda for each learner and task
Showing the final effect of applying feature selection to a learner for each task.
All filters are summarized into a a single color whereas the "Borda" filter appears in its own color.
```{r filter-effect-all-vs-borda-filter, warning=FALSE, dev = c("png", "pdf")}
results_aggr2 <- df_perf %>%
na.omit() %>%
filter(learner_group != "Ridge-CV") %>%
filter(learner_group != "Lasso-CV") %>%
mutate(learner_group = recode_factor(learner_group, `XGBoost` = "XG")) %>%
group_by(learner_group, task.id, filter) %>%
# we actually took the mean already in chunk 'aggr-perf'. This is only to get
# summarise() working
summarise(perf = mean(rmse_aggr)) %>%
ungroup() %>%
# we need to reverse the order on porpuse here so that ggplot reverses it
# again later
mutate(learner_group = fct_relevel(learner_group, "XGBOOST", "SVM", "RF"))
results_aggr2 %>%
ggplot(aes(x = perf, y = learner_group)) +
geom_beeswarm(
data = results_aggr2[results_aggr2$filter != "Borda", ],
shape = 3, size = 2.2, aes(color = "Filter"),
groupOnX = FALSE
) +
geom_point(
data = results_aggr2[results_aggr2$filter == "Borda", ],
shape = 19, size = 2.2, aes(color = "Borda Filter")
) +
facet_wrap(~task.id) +
scale_color_manual(
guide = guide_legend(override.aes = list(shape = c(19, 3))),
values = c(
"Filter" = "#BC3C29FF",
"Borda Filter" = "#0072B5FF"
)
) +
labs(x = "RMSE", y = "Task", colour = NULL) +
guides(size = FALSE) +
scale_x_continuous(limits = c(27, 51)) +
theme_pubr(base_size = 14) +
theme(
panel.grid.major.y = element_line(size = 0.1, linetype = "dashed"),
axis.title.y = element_blank(),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
)
```