Comments (4)
thomasp85
commented on May 29, 2024
I cannot reproduce this. Have you tried with the most recent version of h2o?
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coforfe
commented on May 29, 2024
Hello,
Sorry for the delay.
I have just tried and it continues giving the same error.
I am using the latest h2o version.
>
> # XGBOOST
> full_iris_frame <- as.h2o(iris)
|=====================================================================================================| 100%
>
> full_xgb <- h2o.xgboost(training_frame = full_iris_frame, y = "Species")
|=====================================================================================================| 100%
>
> full_explainer <- lime::lime(dplyr::select(as.data.frame(full_iris_frame), -Species), full_xgb)
>
> full_explanation <- lime::explain(dplyr::select(as.data.frame(full_iris_frame)[1:4,], -Species), full_explainer, n_labels = 3 , n_features = 3)
|=====================================================================================================| 100%
|=====================================================================================================| 100%
Error in glmnet(x[, c(features, j), drop = FALSE], y, weights = weights, :
x should be a matrix with 2 or more columns
>
> packageVersion("h2o")
[1] ‘3.16.0.2’
> packageVersion("xgboost")
[1] ‘0.6.4.1’
> packageVersion("lime")
[1] ‘0.3.1’Thanks,
Carlos.
from lime.
coforfe
commented on May 29, 2024
Hello,
There is already a new h2o version (3.18.0.2) and with it everything runs smoothly:
> # XGBOOST
> full_iris_frame <- as.h2o(iris)
|=========================================================================================================| 100%
>
> full_xgb <- h2o.xgboost(training_frame = full_iris_frame, y = "Species")
|=========================================================================================================| 100%
>
> full_explainer <- lime::lime(dplyr::select(as.data.frame(full_iris_frame), -Species), full_xgb)
>
> full_explanation <- lime::explain(dplyr::select(as.data.frame(full_iris_frame)[1:4,], -Species), full_explainer, n_labels = 3 , n_features = 3)
|=========================================================================================================| 100%
|=========================================================================================================| 100%
>
> head(full_explanation)
model_type case label label_prob model_r2 model_intercept model_prediction feature feature_value
1 classification 1 setosa 0.996033490 0.7474399 0.06413509 0.96476458 Sepal.Width 3.5
2 classification 1 setosa 0.996033490 0.7474399 0.06413509 0.96476458 Petal.Length 1.4
3 classification 1 setosa 0.996033490 0.7474399 0.06413509 0.96476458 Petal.Width 0.2
4 classification 1 versicolor 0.002750067 0.2595668 0.42947763 0.05936985 Sepal.Length 5.1
5 classification 1 versicolor 0.002750067 0.2595668 0.42947763 0.05936985 Petal.Length 1.4
6 classification 1 versicolor 0.002750067 0.2595668 0.42947763 0.05936985 Petal.Width 0.2
feature_weight feature_desc data prediction
1 0.033300165 3.3 < Sepal.Width 5.1, 3.5, 1.4, 0.2 0.996033490, 0.002750067, 0.001216384
2 0.827178873 Petal.Length <= 1.60 5.1, 3.5, 1.4, 0.2 0.996033490, 0.002750067, 0.001216384
3 0.040150456 Petal.Width <= 0.3 5.1, 3.5, 1.4, 0.2 0.996033490, 0.002750067, 0.001216384
4 0.001132338 Sepal.Length <= 5.1 5.1, 3.5, 1.4, 0.2 0.996033490, 0.002750067, 0.001216384
5 -0.446456113 Petal.Length <= 1.60 5.1, 3.5, 1.4, 0.2 0.996033490, 0.002750067, 0.001216384
6 0.075215999 Petal.Width <= 0.3 5.1, 3.5, 1.4, 0.2 0.996033490, 0.002750067, 0.001216384
>
> packageVersion("h2o")
[1] ‘3.18.0.2’
> packageVersion("xgboost")
[1] ‘0.6.4.1’
> packageVersion("lime")
[1] ‘0.3.1’
> Thanks!
Carlos.
from lime.
coforfe
commented on May 29, 2024
I close the issue...
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