wehub-resource-sync
154 lines
4.5 KiB
R
154 lines
4.5 KiB
R
library(lightgbm)
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# We load in the agaricus dataset
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# In this example, we are aiming to predict whether a mushroom is edible
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data(agaricus.train, package = "lightgbm")
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data(agaricus.test, package = "lightgbm")
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train <- agaricus.train
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test <- agaricus.test
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# The loaded data is stored in sparseMatrix, and label is a numeric vector in {0,1}
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class(train$label)
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class(train$data)
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# Set parameters for model training
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train_params <- list(
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num_leaves = 4L
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, learning_rate = 1.0
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, objective = "binary"
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, nthread = 2L
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)
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#--------------------Basic Training using lightgbm----------------
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# This is the basic usage of lightgbm you can put matrix in data field
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# Note: we are putting in sparse matrix here, lightgbm naturally handles sparse input
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# Use sparse matrix when your feature is sparse (e.g. when you are using one-hot encoding vector)
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print("Training lightgbm with sparseMatrix")
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bst <- lightgbm(
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data = train$data
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, params = train_params
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, label = train$label
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, nrounds = 2L
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)
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# Alternatively, you can put in dense matrix, i.e. basic R-matrix
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print("Training lightgbm with Matrix")
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bst <- lightgbm(
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data = as.matrix(train$data)
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, params = train_params
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, label = train$label
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, nrounds = 2L
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)
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# You can also put in lgb.Dataset object, which stores label, data and other meta datas needed for advanced features
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print("Training lightgbm with lgb.Dataset")
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dtrain <- lgb.Dataset(
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data = train$data
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, label = train$label
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)
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bst <- lightgbm(
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data = dtrain
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, params = train_params
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, nrounds = 2L
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)
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# Verbose = 0,1,2
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print("Train lightgbm with verbose 0, no message")
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bst <- lightgbm(
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data = dtrain
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, params = train_params
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, nrounds = 2L
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, verbose = 0L
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)
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print("Train lightgbm with verbose 1, print evaluation metric")
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bst <- lightgbm(
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data = dtrain
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, params = train_params
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, nrounds = 2L
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, verbose = 1L
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)
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print("Train lightgbm with verbose 2, also print information about tree")
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bst <- lightgbm(
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data = dtrain
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, params = train_params
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, nrounds = 2L
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, verbose = 2L
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)
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# You can also specify data as file path to a LibSVM/TCV/CSV format input
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# Since we do not have this file with us, the following line is just for illustration
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# bst <- lightgbm(
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# data = "agaricus.train.svm"
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# , num_leaves = 4L
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# , learning_rate = 1.0
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# , nrounds = 2L
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# , objective = "binary"
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# )
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#--------------------Basic prediction using lightgbm--------------
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# You can do prediction using the following line
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# You can put in Matrix, sparseMatrix, or lgb.Dataset
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pred <- predict(bst, test$data)
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err <- mean(as.numeric(pred > 0.5) != test$label)
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print(paste("test-error=", err))
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#--------------------Save and load models-------------------------
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# Save model to binary local file
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lgb.save(bst, "lightgbm.model")
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# Load binary model to R
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bst2 <- lgb.load("lightgbm.model")
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pred2 <- predict(bst2, test$data)
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# pred2 should be identical to pred
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print(paste("sum(abs(pred2-pred))=", sum(abs(pred2 - pred))))
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#--------------------Advanced features ---------------------------
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# To use advanced features, we need to put data in lgb.Dataset
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dtrain <- lgb.Dataset(data = train$data, label = train$label, free_raw_data = FALSE)
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dtest <- lgb.Dataset.create.valid(dtrain, data = test$data, label = test$label)
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#--------------------Using validation set-------------------------
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# valids is a list of lgb.Dataset, each of them is tagged with name
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valids <- list(train = dtrain, test = dtest)
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# To train with valids, use lgb.train, which contains more advanced features
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# valids allows us to monitor the evaluation result on all data in the list
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print("Train lightgbm using lgb.train with valids")
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bst <- lgb.train(
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data = dtrain
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, params = train_params
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, nrounds = 2L
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, valids = valids
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)
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# We can change evaluation metrics, or use multiple evaluation metrics
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print("Train lightgbm using lgb.train with valids, watch logloss and error")
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bst <- lgb.train(
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data = dtrain
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, params = train_params
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, nrounds = 2L
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, valids = valids
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, eval = c("binary_error", "binary_logloss")
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)
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# lgb.Dataset can also be saved using lgb.Dataset.save
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lgb.Dataset.save(dtrain, "dtrain.buffer")
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# To load it in, simply call lgb.Dataset
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dtrain2 <- lgb.Dataset("dtrain.buffer")
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bst <- lgb.train(
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data = dtrain2
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, params = train_params
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, nrounds = 2L
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, valids = valids
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)
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# information can be extracted from lgb.Dataset using get_field()
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label <- get_field(dtest, "label")
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pred <- predict(bst, test$data)
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err <- as.numeric(sum(as.integer(pred > 0.5) != label)) / length(label)
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print(paste("test-error=", err))
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