在 R 中的 keras CNN 模型上使用 lime 时为数据框给出的无效 'dimnames'
invalid 'dimnames' given for data frame when using lime on a keras CNN model in R
在 R
中使用石灰包时，我遇到了与 How to fix "Invalid 'dimnames' given for data frame? 相同的错误
Error in dimnames<-.data.frame(*tmp*, value = list(n)) :
invalid 'dimnames' given for data frame
我正在尝试将 lime 函数（解释）应用于 keras CNN 网络
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)
我的训练集和测试集包含 1900 个特征，这里我只显示 9 个以简化
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"
我认为问题在于 keras 模型需要一个矩阵作为输入。或者至少那是我的分类问题。这是对我有用的（我假设你的模型是顺序的）。我的火车数据也是一个矩阵：
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")
编辑：考虑到评论编辑了答案。
在 R
中使用石灰包时，我遇到了与 How to fix "Invalid 'dimnames' given for data frame? 相同的错误Error in
dimnames<-.data.frame(*tmp*, value = list(n)) : invalid 'dimnames' given for data frame
我正在尝试将 lime 函数（解释）应用于 keras CNN 网络
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)
我的训练集和测试集包含 1900 个特征，这里我只显示 9 个以简化
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"
我认为问题在于 keras 模型需要一个矩阵作为输入。或者至少那是我的分类问题。这是对我有用的（我假设你的模型是顺序的）。我的火车数据也是一个矩阵：
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")
编辑：考虑到评论编辑了答案。