lime/keras image classification: Input must be a vector, not a `superpixel_list` object.
iacobus42 opened this issue · comments
Using this tutorial on the RStudio AI blog: https://blogs.rstudio.com/ai/posts/2018-03-09-lime-v04-the-kitten-picture-edition/
Everything runs fine until
explanation <- explain(img_path, explainer, n_labels = 2, n_features = 20)
Running either explanation or plot_image_explanation(explanation) returns the error message:
> plot_image_explanation(explanation)
Error: Input must be a vector, not a `superpixel_list` object.
Run `rlang::last_error()` to see where the error occurred.
Using rlang::last_error() and rlang::last_trace() suggest a possible conflict with the vctrs package.
> rlang::last_error()
<error/vctrs_error_scalar_type>
Input must be a vector, not a `superpixel_list` object.
Backtrace:
1. lime::plot_image_explanation(explanation)
7. vctrs:::stop_scalar_type(...)
8. vctrs:::stop_vctrs(msg, "vctrs_error_scalar_type", actual = x)
> rlang::last_trace()
<error/vctrs_error_scalar_type>
Input must be a vector, not a `superpixel_list` object.
Backtrace:
█
1. ├─lime::plot_image_explanation(explanation)
2. │ ├─...[]
3. │ └─tibble:::`[.tbl_df`(...)
4. │ └─tibble:::tbl_subset_row(xo, i = i, i_arg)
5. │ └─base::lapply(unclass(x), vec_slice, i = i)
6. │ └─vctrs:::FUN(X[[i]], ...)
7. └─vctrs:::stop_scalar_type(...)
8. └─vctrs:::stop_vctrs(msg, "vctrs_error_scalar_type", actual = x)
str(explanation) looks complete and explain() doesn't cause any errors - only when attempting to plot/print/view the results.
My session info is below:
> sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Pop!_OS 20.04 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /home/jacob/.local/share/r-miniconda/envs/r-reticulate/lib/libmkl_rt.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
[4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] magick_2.3 lime_0.5.1 keras_2.2.5.0 forcats_0.5.0 stringr_1.4.0
[6] dplyr_0.8.5 purrr_0.3.3 readr_1.3.1 tidyr_1.0.2 tibble_3.0.0
[11] ggplot2_3.3.0 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] httr_1.4.1 jsonlite_1.6.1 foreach_1.5.0 modelr_0.1.6 shiny_1.4.0.2
[6] assertthat_0.2.1 cellranger_1.1.0 pillar_1.4.3 backports_1.1.5 lattice_0.20-40
[11] glue_1.4.0 reticulate_1.15 digest_0.6.25 promises_1.1.0 rvest_0.3.5
[16] colorspace_1.4-1 htmltools_0.4.0 httpuv_1.5.2 Matrix_1.2-18 pkgconfig_2.0.3
[21] broom_0.5.5 haven_2.2.0 xtable_1.8-4 scales_1.1.0 whisker_0.4
[26] later_1.0.0 gower_0.2.1 farver_2.0.3 generics_0.0.2 ellipsis_0.3.0
[31] withr_2.1.2 cli_2.0.2 magrittr_1.5 crayon_1.3.4 readxl_1.3.1
[36] mime_0.9 fs_1.3.2 fansi_0.4.1 nlme_3.1-144 xml2_1.2.5
[41] tools_3.6.3 hms_0.5.3 lifecycle_0.2.0 munsell_0.5.0 reprex_0.3.0
[46] glmnet_3.0-2 compiler_3.6.3 rlang_0.4.5 grid_3.6.3 iterators_1.0.12
[51] rstudioapi_0.11 rappdirs_0.3.1 htmlwidgets_1.5.1 labeling_0.3 base64enc_0.1-3
[56] gtable_0.3.0 codetools_0.2-16 curl_4.3 abind_1.4-5 DBI_1.1.0
[61] R6_2.4.1 tfruns_1.4 lubridate_1.7.4 tensorflow_2.0.0 utf8_1.1.4
[66] fastmap_1.0.1 zeallot_0.1.0 shinythemes_1.1.2 shape_1.4.4 stringi_1.4.6
[71] Rcpp_1.0.4 vctrs_0.2.4 dbplyr_1.4.2 tidyselect_1.0.0
I got the same error and was able to fix it using:
explanation <- as.data.frame(explanation)
plot_image_explanation(explanation)
Found out that the class of the object explanation was also tbl_df and tbl instead of just data.frame (solved it thanks to the example from the help-file of plot_image_explanation).
I got the same error and now the as.data.frame is not working for the tbl.
The example of plot_image_explanation is a data frame and it still work. However, the object from explain is tbl and will throw the following error for as.data.frame:
Error in `vec_size()`:
! `x` must be a vector, not a <superpixel_list> object.
---
Backtrace:
▆
1. ├─base::as.data.frame(explanation)
2. ├─tibble:::as.data.frame.tbl_df(explanation)
3. │ ├─base::`[<-`(`*tmp*`, unname, value = `<named list>`)
4. │ └─tibble:::`[<-.tbl_df`(`*tmp*`, unname, value = `<named list>`)
5. │ └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
6. │ └─tibble:::vectbl_recycle_rhs_rows(...)
7. │ ├─base::withCallingHandlers(...)
8. │ └─vctrs::vec_recycle(value[[j]], nrow)
9. └─vctrs:::stop_scalar_type(`<fn>`(`<sprpxl_l>`), "x", `<fn>`(vec_size()))
10. └─vctrs:::stop_vctrs(...)
11. └─rlang::abort(message, class = c(class, "vctrs_error"), ..., call = vctrs_error_call(call))
I wonder if anyone has insights on it? Thanks!
I figured that we can create a data frame from explanation object in the following code. We need to take care of the nested list object (eg. feature_value, data, prediction). So here is the workaround so that explanation_df is the input of plot_image_explanation() function:
explanation_df <- data.frame(model_type = explanation$model_type,
case = explanation$case,
label = explanation$label,
label_prob = explanation$label_prob,
model_r2 = explanation$model_r2,
model_intercept = explanation$model_intercept,
model_prediction = explanation$model_prediction,
feature = explanation$feature,
feature_value = NA,
feature_weight = explanation$feature_weight,
feature_desc = explanation$feature_desc,
data = NA,
prediction = NA,
stringsAsFactors = FALSE)
## this will coerce superpixel_list and bitmap_list to list, but still works for plotting
for (i in 1:nrow(explanation_df)){
explanation_df$feature_value[i] <- explanation$feature_value[i]
explanation_df$data[i] <- explanation$data[i]
explanation_df$prediction[i] <- explanation$prediction[i]
}
plot_image_explanation(explanation_df)
Thanks for this... I want to experiment with lime on images but got stuck installing tensorflow. Might anyone have a link to the explanation data frame to use with lime thus bypassing the modeling step? Much appreciated.