I am having the same error as How to fix "Invalid 'dimnames' given for data frame? when usin lime package in R
Error in
dimnames<-.data.frame(*tmp*, value = list(n)) : invalid 'dimnames' given for data frame
I am trying to apply the lime function (explain) to a keras CNN network
Model
_____________________________________________________________________________________________________
Layer (type) Output Shape Param #
=====================================================================================================
conv1d_8 (Conv1D) (None, 1896, 64) 384
_____________________________________________________________________________________________________________________________________________________
batch_normalization_8 (BatchNormalization) (None, 1896, 64) 256
_____________________________________________________________________________________________________________________________________________________
leaky_re_lu_8 (LeakyReLU) (None, 1896, 64) 0
_____________________________________________________________________________________________________________________________________________________
dropout_10 (Dropout) (None, 1896, 64) 0
_____________________________________________________________________________________________________________________________________________________
conv1d_9 (Conv1D) (None, 1886, 32) 22560
_____________________________________________________________________________________________________________________________________________________
batch_normalization_9 (BatchNormalization) (None, 1886, 32) 128
_____________________________________________________________________________________________________________________________________________________
leaky_re_lu_9 (LeakyReLU) (None, 1886, 32) 0
_____________________________________________________________________________________________________________________________________________________
dropout_11 (Dropout) (None, 1886, 32) 0
_____________________________________________________________________________________________________________________________________________________
conv1d_10 (Conv1D) (None, 1866, 16) 10768
_____________________________________________________________________________________________________________________________________________________
batch_normalization_10 (BatchNormalization) (None, 1866, 16) 64
_____________________________________________________________________________________________________________________________________________________
leaky_re_lu_10 (LeakyReLU) (None, 1866, 16) 0
_____________________________________________________________________________________________________________________________________________________
dropout_12 (Dropout) (None, 1866, 16) 0
_____________________________________________________________________________________________________________________________________________________
conv1d_11 (Conv1D) (None, 1826, 8) 5256
_____________________________________________________________________________________________________________________________________________________
batch_normalization_11 (BatchNormalization) (None, 1826, 8) 32
_____________________________________________________________________________________________________________________________________________________
leaky_re_lu_11 (LeakyReLU) (None, 1826, 8) 0
_____________________________________________________________________________________________________________________________________________________
dropout_13 (Dropout) (None, 1826, 8) 0
_____________________________________________________________________________________________________________________________________________________
flatten_2 (Flatten) (None, 14608) 0
_____________________________________________________________________________________________________________________________________________________
dense_4 (Dense) (None, 100) 1460900
_____________________________________________________________________________________________________________________________________________________
dropout_14 (Dropout) (None, 100) 0
_____________________________________________________________________________________________________________________________________________________
dense_5 (Dense) (None, 5) 505
=====================================================================================================================================================
Total params: 1,500,853
Trainable params: 1,500,613
Non-trainable params: 240
_____________________________________________________________________________________________________________________________________________________
my_explainer <- lime(x =train, model = model, bin_continuous = FALSE)
explanation <- explain(test, explainer = my_explainer, n_labels = 1, n_features = 2, kernel_width = 0.5)
My train and test sets contain 1900 features, here I display only 9 to simplify
str(train[,1:9])
'data.frame': 77 obs. of 9 variables:
$ X1: num 0.005598 0.009835 0.005365 0.000725 0.000992 ...
$ X2: num 0 0.00156 0 0.00172 0.00261 ...
$ X3: num 0 0.00752 0 0 0.00556 ...
$ X4: num 0 0.00191 0.00479 0.00193 0.005 ...
$ X5: num 0.0028 0.0033 0 0.00115 0.00503 ...
$ X6: num 0 0 0 0.000453 0.00258 ...
$ X7: num 0 0.00121 0 0.00127 0.00185 ...
$ X8: num 0.00646 0 0.0097 0.00435 0.00278 ...
$ X9: num 0 0.00301 0.00183 0.0045 0.00241 ...
str(test[,1:9])
'data.frame': 3 obs. of 9 variables:
$ X1: num 0.00651 0.00286 0.00511
$ X2: num 0.00229 0.00592 0.0031
$ X3: num 0.00343 0.00338 0.0094
$ X4: num 0.00464 0.00532 0.01073
$ X5: num 0.00163 0.00203 0.01841
$ X6: num 0.00277 0.0041 0.00865
$ X7: num 0.00169 0.00257 0.01793
$ X8: num 0.00669 0.00213 0.01202
$ X9: num 0.0038 0.01023 0.00843
dimnames(train[,1:9])
[[1]]
[1] "1" "2" "3" "5" "6" "7" "8" "9" "10" "12" "15" "16" "18" "19" "20" "21" "25" "26" "28" "29" "30" "31" "33" "34"
[25] "35" "36" "38" "39" "40" "42" "43" "44" "46" "48" "50" "51" "52" "53" "55" "59" "60" "61" "64" "65" "66" "67" "70" "71"
[49] "73" "74" "76" "77" "78" "79" "80" "83" "84" "85" "86" "87" "88" "90" "92" "94" "97" "102" "103" "104" "105" "106" "108" "109"
[73] "112" "114" "115" "116" "117"
[[2]]
[1] "X1" "X2" "X3" "X4" "X5" "X6" "X7" "X8" "X9"
dimnames(test[,1:9])
[[1]]
[1] "23" "27" "32"
[[2]]
[1] "X1" "X2" "X3" "X4" "X5" "X6" "X7" "X8" "X9"
I think that the problem is that keras model wants a matrix as an input. Or at least that was the problem with my classification. Here is what worked for me (i am assuming that your model is Sequential). Also my train data is a matrix:
model_type.keras.engine.sequential.Sequential <- function(x, ...) {
"classification"
}
# Setup lime::predict_model()
predict_model.keras.engine.sequential.Sequential <- function (x, newdata, type, ...) {
## here you have to write function, that takes a data.frame
## and transform it to shape that keras understands
## for example if u flatten your array before training CNN, you just use
## as-matrix()
## if keras model expect 3d dataset, you write something like
## as.array(newdata, dims(n,12,12))
your_function <- function(data){
## will return proper shape of your data
}
pred <- predict(object = x, x = your_function(newdata))
data.frame (pred) }
x <- as.data.frame(train_data)
x2 <- as.data.frame(test_data)
explainer <- lime(x = x, model= model)
explanation <- lime::explain (
x= x2[1:10,],
explainer = explainer,
n_features = 5,
n_labels=1) ## change for your problem
plot_features (explanation) +
labs (title = "LIME: Feature Importance Visualization CNN")
EDIT: edited answer taking into account the comment.