lgbm.cv example #4
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I think the example is outdated, not sure, I have to check that. I am going to add the code below as the example in my next push. Try this (change the working directory on line 8 and LGBM on line 32): library(Laurae)
library(stringi)
library(Matrix)
library(sparsity)
library(data.table)
remove(list = ls()) # WARNING: CLEANS EVERYTHING IN THE ENVIRONMENT
setwd("D:/Data Science/HousePrices") # CHANGE THIS TO WHATEVER TEMPORARY DIRECTORY WHERE YOU WANT TEMPORARY FILES
DT <- data.table(Split1 = c(rep(0, 50), rep(1, 50)), Split2 = rep(c(rep(0, 25), rep(0.5, 25)), 2))
DT$Split3 <- rep(c(rep(0, 10), rep(0.25, 15)), 4)
DT$Split4 <- rep(c(rep(0, 5), rep(0.1, 5), rep(0, 5), rep(0.1, 10)), 4)
DT$Split5 <- rep(c(rep(0, 5), rep(0.05, 5), rep(0, 10), rep(0.05, 5)), 4)
label <- c(rep(0, 25), rep(1, 25), rep(0, 25), rep(1, 25))
label <- as.numeric((DT$Split2 == 0) & (DT$Split1 == 0) & (DT$Split3 == 0))
label <- as.numeric((DT$Split2 == 0) & (DT$Split1 == 0) & (DT$Split3 == 0) & (DT$Split4 == 0) | ((DT$Split2 == 0.5) & (DT$Split1 == 1) & (DT$Split3 == 0.25) & (DT$Split4 == 0.1) & (DT$Split5 == 0)) | ((DT$Split1 == 0) & (DT$Split2 == 0.5)))
trained <- lgbm.cv(y_train = label,
x_train = DT,
bias_train = NA,
folds = 5,
unicity = TRUE,
application = "binary",
num_iterations = 1,
early_stopping_rounds = 1,
learning_rate = 5,
num_leaves = 16,
min_data_in_leaf = 1,
min_sum_hessian_in_leaf = 1,
tree_learner = "serial",
num_threads = 1,
lgbm_path = "C:/xgboost/LightGBM/windows/x64/Release/lightgbm.exe",
workingdir = file.path(getwd()),
validation = FALSE,
files_exist = FALSE,
verbose = TRUE,
is_training_metric = TRUE,
save_binary = TRUE,
metric = "binary_logloss")
str(trained)I am getting this output: and List of 3
$ Models :List of 5
..$ 1:List of 8
.. ..$ Model : chr [1:14] "max_feature_idx=-1" "sigmoid=1" "" "Tree=0" ...
.. ..$ Path : chr "D:/Data Science/HousePrices/temp"
.. ..$ Name : chr "lgbm_model.txt"
.. ..$ lgbm : chr "C:/xgboost/LightGBM/windows/x64/Release/lightgbm.exe"
.. ..$ Train : chr "lgbm_train.csv"
.. ..$ Valid : chr "lgbm_val.csv"
.. ..$ Test : logi NA
.. ..$ Validation: num [1:20] 1 1 1 1 1 ...
..$ 2:List of 8
.. ..$ Model : chr [1:14] "max_feature_idx=-1" "sigmoid=1" "" "Tree=0" ...
.. ..$ Path : chr "D:/Data Science/HousePrices/temp"
.. ..$ Name : chr "lgbm_model.txt"
.. ..$ lgbm : chr "C:/xgboost/LightGBM/windows/x64/Release/lightgbm.exe"
.. ..$ Train : chr "lgbm_train.csv"
.. ..$ Valid : chr "lgbm_val.csv"
.. ..$ Test : logi NA
.. ..$ Validation: num [1:20] 1 1 1 1 1 ...
..$ 3:List of 8
.. ..$ Model : chr [1:14] "max_feature_idx=-1" "sigmoid=1" "" "Tree=0" ...
.. ..$ Path : chr "D:/Data Science/HousePrices/temp"
.. ..$ Name : chr "lgbm_model.txt"
.. ..$ lgbm : chr "C:/xgboost/LightGBM/windows/x64/Release/lightgbm.exe"
.. ..$ Train : chr "lgbm_train.csv"
.. ..$ Valid : chr "lgbm_val.csv"
.. ..$ Test : logi NA
.. ..$ Validation: num [1:20] 1 1 1 1 1 ...
..$ 4:List of 8
.. ..$ Model : chr [1:14] "max_feature_idx=-1" "sigmoid=1" "" "Tree=0" ...
.. ..$ Path : chr "D:/Data Science/HousePrices/temp"
.. ..$ Name : chr "lgbm_model.txt"
.. ..$ lgbm : chr "C:/xgboost/LightGBM/windows/x64/Release/lightgbm.exe"
.. ..$ Train : chr "lgbm_train.csv"
.. ..$ Valid : chr "lgbm_val.csv"
.. ..$ Test : logi NA
.. ..$ Validation: num [1:20] 1 1 1 1 1 ...
..$ 5:List of 8
.. ..$ Model : chr [1:14] "max_feature_idx=-1" "sigmoid=1" "" "Tree=0" ...
.. ..$ Path : chr "D:/Data Science/HousePrices/temp"
.. ..$ Name : chr "lgbm_model.txt"
.. ..$ lgbm : chr "C:/xgboost/LightGBM/windows/x64/Release/lightgbm.exe"
.. ..$ Train : chr "lgbm_train.csv"
.. ..$ Valid : chr "lgbm_val.csv"
.. ..$ Test : logi NA
.. ..$ Validation: num [1:20] 1 1 1 1 1 ...
$ Validation:List of 2
..$ : num [1:100] 1 1 1 1 1 ...
..$ :List of 5
.. ..$ : num [1:20] 1 1 1 1 1 ...
.. ..$ : num [1:20] 1 1 1 1 1 ...
.. ..$ : num [1:20] 1 1 1 1 1 ...
.. ..$ : num [1:20] 1 1 1 1 1 ...
.. ..$ : num [1:20] 1 1 1 1 1 ...
$ Weights : num [1:5] 0.2 0.2 0.2 0.2 0.2 |
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Thanks! |
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(You can close this if you want or leave it open) |
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Another (potentially silly) question: If I followed the installation guide in the readme for linux, what might my lightgbm path be? |
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I fixed the LightGBM functions' documentation in commit @4fe8e2b35acabbe8979cd3181dca8f004a03ee38.
Your LightGBM should be on the same directory as your LightGBM download. You can find out where it has been compiled using this on your LightGBM path: ls -d */If you installed in a folder named "(...)/LightGBM" path, it should the |
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I didn't even have lightgbm installed! lol. So for future reference, this error means lightgbm isn't installed, or you're pointing at the wrong path: |
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I also got this by omitting the path. I installed on OS X as shown here... Might it be possible to make the It successfully wrote the .conf and train_1.csv and val_1.csv files. I'm not sure waht the other errors are about where it appears to look for a /bin/sh type executable or has the connection failure with no model_1.txt. |
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The lgbm_path in mac was the location of unix executable that you build from source. |
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I'm having trouble figuring out the example in
?lgbm.cv. It looks like it's on the housing price dataset, but I'm not 100% sure. When I try to run it, I get the following error:Do you have a working example that runs on your machine I could try out, to make sure my installation is working?
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