/
feature-importance.Rmd
531 lines (460 loc) · 14.2 KB
/
feature-importance.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
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
---
title: "Feature importance analysis"
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$set(
fig.retina = 3,
fig.align = "center",
fig.width = 8.5,
fig.asp = 0.66,
out.width = "100%",
echo = FALSE
)
options(
scipen = 999
)
library("drake")
library("hsdar")
library("dplyr")
library("ggplot2")
library("ggpubr")
library("ggpmisc")
library("patchwork")
library("iml")
# load drake objects
loadd(
fi_permut_hr_buffer2,
fi_permut_vi_buffer2,
fi_ale_hr_buffer2,
fi_ale_vi_buffer2,
fi_ale_hr_buffer2_gs20,
fi_ale_vi_buffer2_gs20,
df_wavelengths_from_indices,
spec_sigs
)
```
Preview the ordered feature importance results for datasets "HR" and "VI".
```{r feature-importance-1 }
fi_ranked_hr <- fi_permut_hr_buffer2$res %>%
tibble::rownames_to_column("measure") %>%
tidyr::pivot_longer(
cols = starts_with("B"),
values_to = "importance", names_to = "feature"
) %>%
dplyr::mutate(wavelength = seq(420, 995, 4.75)) %>%
dplyr::mutate(numeric_id = seq(5, 126, 1)) %>%
dplyr::arrange(desc(importance)) %>%
dplyr::mutate(rank = row_number()) %>%
dplyr::select(-measure)
fi_ranked_hr
fi_ranked_vi <- fi_permut_vi_buffer2$res %>%
tibble::rownames_to_column("measure") %>%
tidyr::pivot_longer(
cols = starts_with("B"),
values_to = "importance", names_to = "feature"
) %>%
# mutate(wavelength = seq(420, 995, 4.75)) %>%
dplyr::arrange(desc(importance)) %>%
dplyr::mutate(rank = row_number()) %>%
dplyr::select(-measure) %>%
dplyr::mutate(feature = stringr::str_replace(feature, "bf2_", ""))
fi_ranked_vi
```
# Create a virtual spectral signature of vegetation using PROSAIL.
PROSAIL is a algorithm simulating spectral signatures of vegetation, see `?hsdar::PROSAIL`.
Reflectance is scaled to 0-10 to be able to plot it in the same plot as the feature importance rankings -> the axis limits for the y and z axis needs to match.
PROSAIL returns a spectral signature from 400 nm to 2500 nm -> we take the values only and subset to 400 nm - 1000 nm.
Because we order from 1 - 10 with 1 being the best rank, we have to reverse the scaling of the reflectance values.
```{r feature-importance-2 }
spectra_sim <- hsdar::PROSAIL()
spectra_df <- data.frame(
reflectance = as.vector(spectra_sim@spectra@spectra_ma),
wavelength = seq(400, 2500, 1)
) %>%
dplyr::filter(wavelength < 1000) %>%
# scale the reflectance to [0, 2] to play nicely with the y-axis later (mean dec in rmse)
dplyr::mutate(reflectance = scale(reflectance,
center = FALSE,
scale = max(reflectance, na.rm = TRUE) / 2
))
```
Next we bind the simulated data with the feature importance rankings.
To join both data.frames we need to round the reflectance centers of the bands to integers to match with the reflectance values created by PROSAIL.
```{r feature-importance-3 }
# round the wavelengths of the HR dataset to match with the simulated ones
fi_ranked_hr$wavelength <- round(fi_ranked_hr$wavelength)
data_hr_merged <- left_join(spectra_df, fi_ranked_hr, by = c("wavelength")) %>%
left_join(spec_sigs, by = c("wavelength")) %>%
mutate(class = "HR") %>%
mutate(reflectance = as.numeric(reflectance))
```
To label only a subset of the data, a custom data.frame is created.
```{r feature-importance-4 }
df_wavelengths_from_indices_imp <- df_wavelengths_from_indices %>%
left_join(fi_ranked_vi, by = c("class" = "feature")) %>%
na.omit()
df_wavelengths_from_indices_label <- df_wavelengths_from_indices_imp %>%
arrange(rank) %>%
group_by(class, rank) %>%
dplyr::slice(1) %>%
dplyr::ungroup() %>%
dplyr::arrange(rank) %>%
dplyr::slice(1:10)
df_wavelengths_from_hr <- data_hr_merged %>%
arrange(rank) %>%
group_by(class, rank) %>%
dplyr::slice(1) %>%
dplyr::ungroup() %>%
dplyr::arrange(rank) %>%
dplyr::slice(1:10) %>%
dplyr::select(-class) %>%
dplyr::rename(class = feature)
df_label_all <- df_wavelengths_from_indices_label %>%
bind_rows(df_wavelengths_from_hr)
```
```{r feature-importance-5, echo=FALSE}
plot_reflectance_imp_hr <- function(data) {
ggplot(data, aes(x = .data[["wavelength"]], y = .data[["importance"]])) +
geom_line(
data = data_hr_merged[!is.na(data_hr_merged$laukiz1), ],
aes(
x = wavelength, y = laukiz1,
color = "Spectral Signature of Laukiz1"
),
na.rm = TRUE,
linetype = "solid",
size = 0.6
) +
geom_line(
data = data_hr_merged[!is.na(data_hr_merged$laukiz2), ],
aes(
x = wavelength, y = laukiz2,
color = "Spectral Signature of Laukiz2"
),
na.rm = TRUE,
linetype = "solid",
size = 0.6
) +
geom_line(
data = data_hr_merged[!is.na(data_hr_merged$luiando), ],
aes(
x = wavelength, y = luiando,
color = "Spectral Signature of Luiando"
),
na.rm = TRUE,
linetype = "solid",
size = 0.6
) +
geom_line(
data = data_hr_merged[!is.na(data_hr_merged$oiartzun), ],
aes(
x = wavelength, y = oiartzun,
color = "Spectral Signature of Oiartzun"
),
na.rm = TRUE,
linetype = "solid",
size = 0.6
) +
# HR: plot only rank 11:last
geom_point(
data = data_hr_merged[which(!(data_hr_merged$rank %in% c(1:10))), ],
aes(color = "HR: Rank 11:122"),
size = 1.8,
shape = 1,
na.rm = TRUE
) +
# HR: plot only rank 1:10
geom_point(
data = data_hr_merged[which(data_hr_merged$rank %in% c(1:10)), ],
aes(color = "HR: Rank 1:10"),
shape = 20,
size = 1.8,
na.rm = TRUE
) +
scale_color_manual(values = c(
"HR: Rank 11:122" = "grey",
"HR: Rank 1:10" = "black",
"Spectral Signature of Laukiz1" = "#BC3C29",
"Spectral Signature of Laukiz2" = "#0072B5",
"Spectral Signature of Luiando" = "#E18727",
"Spectral Signature of Oiartzun" = "#20854E"
)) +
scale_x_continuous(limits = c(400, 1000), breaks = scales::pretty_breaks()) +
scale_y_continuous(
sec.axis = sec_axis(~ scale(-.,
center = FALSE,
scale = max(., na.rm = TRUE)
),
labels = c(1.0, 0.75, 0.55, 0.25, 0),
name = "Scaled Reflectance [0, 1]"
)
) +
guides(color = guide_legend(
title = NULL,
override.aes = list(
linetype = c("blank", "blank", "solid", "solid", "solid", "solid"),
shape = c(20, 1, NA, NA, NA, NA),
color = c("black", "grey", "#BC3C29", "#0072B5", "#E18727", "#20854E")
)
)) +
labs(
title = "Permutation-based Variable Importance for dataset 'HR'",
subtitle = paste0(
"The ten most important features are labeled by their band number."
),
caption = "Learner: SVM; 100 Monte-Carlo Iterations",
y = "Mean decrease in RMSE", x = "Wavelength [nm]"
) +
ggrepel::geom_label_repel(
# only label the best 10 features
data = df_wavelengths_from_hr,
label = df_wavelengths_from_hr$class,
size = 4,
na.rm = TRUE
) +
ggpubr::theme_pubclean(base_size = 14)
}
plot_reflectance_imp_vi <- function(data) {
ggplot(data, aes(x = .data[["wavelength"]], y = .data[["importance"]])) +
geom_line(
data = data_hr_merged[!is.na(data_hr_merged$laukiz1), ],
aes(
x = wavelength, y = laukiz1,
color = "Spectral Signature of Laukiz1"
),
na.rm = TRUE,
linetype = "solid",
size = 0.6
) +
geom_line(
data = data_hr_merged[!is.na(data_hr_merged$laukiz2), ],
aes(
x = wavelength, y = laukiz2,
color = "Spectral Signature of Laukiz2"
),
na.rm = TRUE,
linetype = "solid",
size = 0.6
) +
geom_line(
data = data_hr_merged[!is.na(data_hr_merged$luiando), ],
aes(
x = wavelength, y = luiando,
color = "Spectral Signature of Luiando"
),
na.rm = TRUE,
linetype = "solid",
size = 0.6
) +
geom_line(
data = data_hr_merged[!is.na(data_hr_merged$oiartzun), ],
aes(
x = wavelength, y = oiartzun,
color = "Spectral Signature of Oiartzun"
),
na.rm = TRUE,
linetype = "solid",
size = 0.6
) +
# VI: rank 1:10
geom_point(
data = df_wavelengths_from_indices_imp[which((df_wavelengths_from_indices_imp$rank %in% c(1:10))), ],
aes(x = wavelength, y = importance, group = class),
size = 1.4, shape = 20
) +
# VI: rank 1:10
geom_line(
data = df_wavelengths_from_indices_imp[which((df_wavelengths_from_indices_imp$rank %in% c(1:10))), ],
aes(x = wavelength, y = importance, group = class, color = "VI: Rank 1:10"),
size = 0.2, linetype = "dashed"
) +
# VI: rank 11:89
geom_point(
data = df_wavelengths_from_indices_imp[which(!(df_wavelengths_from_indices_imp$rank %in% c(1:10))), ],
aes(x = wavelength, y = importance, group = class),
color = "grey",
shape = 1, size = 2
) +
# VI: rank 11:89
geom_line(
data = df_wavelengths_from_indices_imp[which(!(df_wavelengths_from_indices_imp$rank %in% c(1:10))), ],
aes(x = wavelength, y = importance, group = class, color = "VI: Rank 11:89"),
size = 0.2, linetype = "dashed"
) +
scale_color_manual(values = c(
"VI: Rank 11:89" = "grey",
"VI: Rank 1:10" = "black",
"Spectral Signature of Laukiz1" = "#BC3C29",
"Spectral Signature of Laukiz2" = "#0072B5",
"Spectral Signature of Luiando" = "#E18727",
"Spectral Signature of Oiartzun" = "#20854E"
)) +
scale_x_continuous(limits = c(400, 1000), breaks = scales::pretty_breaks()) +
scale_y_continuous(
sec.axis = sec_axis(~ scale(-.,
center = FALSE,
scale = max(., na.rm = TRUE)
),
labels = c(1.0, 0.75, 0.55, 0.25, 0),
name = "Scaled Reflectance [0, 1]"
)
) +
guides(color = guide_legend(
title = NULL,
override.aes = list(
linetype = c("solid", "solid", "solid", "solid", "dashed", "dashed"),
shape = c(NA, NA, NA, NA, 20, 1),
color = c("#BC3C29", "#0072B5", "#E18727", "#20854E", "black", "grey")
)
)) +
labs(
title = "Permutation-based Variable Importance for dataset 'VI'",
subtitle = paste0(
"The ten most important features are labeled by their index name."
),
caption = "Learner: SVM; 100 Monte-Carlo Iterations",
y = "Mean decrease in RMSE", x = "Wavelength [nm]"
) +
ggrepel::geom_label_repel(
# only label the best 10 features
data = df_wavelengths_from_indices_label,
label = df_wavelengths_from_indices_label$class,
size = 4,
na.rm = TRUE
) +
ggpubr::theme_pubclean(base_size = 14)
}
plot_reflectance_imp_absolute <- function(data, x_identifier, class) {
pl <- ggplot(data, aes(x = .data[[x_identifier]], y = .data[["importance"]])) +
labs(y = "Importance", x = "Band number") +
geom_segment(aes(
x = .data[[x_identifier]], y = 0,
xend = .data[[x_identifier]], yend = .data[["importance"]]
),
color = "grey", show.legend = FALSE
) +
geom_point(size = 1, color = "black", show.legend = T) +
labs(
title = glue::glue("Permutation-based Variable Importance for Dataset '{class}'"),
subtitle = "Absolute importance values by band",
caption = "Learner: SVM; 100 Monte-Carlo iterations"
) +
ggpubr::theme_pubclean()
if (is.character(data[[x_identifier]])) {
pl
} else {
pl + scale_x_continuous(breaks = seq(5, 125, 5))
pl
}
}
```
# P1 Main plot
```{r fi-permut-hr, warning=FALSE, fig.show='hide'}
p11 <- data_hr_merged %>%
plot_reflectance_imp_hr()
p11
```
```{r fi-permut-vi, warning=FALSE, fig.show='hide'}
p12 <- data_hr_merged %>%
plot_reflectance_imp_vi()
p12
```
```{r fi-permut-vi-hr, dev = c("png", "pdf"), warning=FALSE, fig.asp=1.2}
p11 / p12
```
# Plots by dataset {.tabset .tabset-fade}
## HR
### P2 Absolute permutation based Var Imp
```{r fi-permut-hr-abs, dev = c("png", "pdf")}
p2 <- fi_ranked_hr %>%
plot_reflectance_imp_absolute("numeric_id", class = "HR")
p2
```
## VI
### P3 Absolute permutation based Var Imp
```{r fi-permut-vi-abs, dev = c("png", "pdf")}
p3 <- fi_ranked_vi %>%
plot_reflectance_imp_absolute("feature", class = "VI") +
ggpubr::rotate_x_text()
p3
```
Vogelmann2 $(R_{734}-R_{747})/(R_{715}+R_{726})$ Vogelmann et al. (1993)
Vogelmann4 $(R_{734}-R_{747})/(R_{715}+R_{720})$ Vogelmann et al. (1993)
Vogelmann3 $D_{715}/D_{705}$ Vogelmann et al. (1993)
Vogelmann $R_{740}/R_{720}$ Vogelmann et al. (1993)
NPCI $(R_{680}-R_{430})/(R_{680}+R_{430})$
D2 $D_{705}/D_{722}$
Datt3 $D_{754}/D_{704}$
PWI $R_{900}/R_{970}$
SR7 $R_{440}/R_{690}$
SRPI $R_{430}/R_{680}$
Dxxx: First derivation of reflectance values at wavelength 'xxx'.
Rxxx: Reflectance at wavelength 'xxx'.
Reference: `?hsdar::vegindex()`
## Combined
```{r fi-hr-vi-abs-comb, fig.width=9.5, fig.asp=1.1}
p2 / p3
```
# ALE plots {.tabset .tabset-fade}
ALE plots via package {iml}
## P2 HR
### Grid size: 100
Top ten HR features from permutation Vimp
```{r fi-hr-ale, dev = c("png", "pdf")}
fi_ale_hr_buffer2$plot(
features = df_wavelengths_from_hr$class,
ncol = 2
) *
theme_pubr(base_size = 12) *
theme(
legend.text = element_text(size = 13),
legend.title = element_text(size = 13)
)
```
### Grid size: 20
Top ten HR features from permutation Vimp
```{r}
fi_ale_hr_buffer2_gs20$plot(
features = df_wavelengths_from_hr$class,
ncol = 2
) *
theme_pubr(base_size = 12) *
theme(
legend.text = element_text(size = 13),
legend.title = element_text(size = 13)
)
```
## P3 VI
### Grid size: 100
Top ten VI features from permutation Vimp
```{r fi-vi-ale, dev = c("png", "pdf")}
fi_ale_vi_buffer2$plot(
features = paste0("bf2_", df_wavelengths_from_indices_label$class),
ncol = 2
) *
theme_pubr(base_size = 12) *
theme(
legend.text = element_text(size = 13),
legend.title = element_text(size = 13)
)
```
### Grid size: 20
Top ten HR features from permutation Vimp
```{r}
fi_ale_vi_buffer2_gs20$plot(
features = paste0("bf2_", df_wavelengths_from_indices_label$class),
ncol = 2
) *
theme_pubr(base_size = 12) *
theme(
legend.text = element_text(size = 13),
legend.title = element_text(size = 13)
)
```