wehub-resource-sync
71 lines
1.9 KiB
R
71 lines
1.9 KiB
R
library(lightgbm)
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# We load the default iris dataset shipped with R
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data(iris)
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# We must convert factors to numeric
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# They must be starting from number 0 to use multiclass
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# For instance: 0, 1, 2, 3, 4, 5...
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iris$Species <- as.numeric(as.factor(iris$Species)) - 1L
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# We cut the data set into 80% train and 20% validation
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# The 10 last samples of each class are for validation
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train <- as.matrix(iris[c(1L:40L, 51L:90L, 101L:140L), ])
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test <- as.matrix(iris[c(41L:50L, 91L:100L, 141L:150L), ])
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dtrain <- lgb.Dataset(data = train[, 1L:4L], label = train[, 5L])
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dtest <- lgb.Dataset.create.valid(dtrain, data = test[, 1L:4L], label = test[, 5L])
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valids <- list(test = dtest)
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# Method 1 of training
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params <- list(
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objective = "multiclass"
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, metric = "multi_error"
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, num_class = 3L
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, min_data = 1L
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, learning_rate = 1.0
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)
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model <- lgb.train(
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params
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, dtrain
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, 100L
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, valids
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, early_stopping_rounds = 10L
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)
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# We can predict on test data, outputs a 90-length vector
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# Order: obs1 class1, obs1 class2, obs1 class3, obs2 class1, obs2 class2, obs2 class3...
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my_preds <- predict(model, test[, 1L:4L])
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# Method 2 of training, identical
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params <- list(
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min_data = 1L
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, learning_rate = 1.0
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, objective = "multiclass"
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, metric = "multi_error"
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, num_class = 3L
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)
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model <- lgb.train(
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params
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, dtrain
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, 100L
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, valids
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, early_stopping_rounds = 10L
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)
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# We can predict on test data, identical
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my_preds <- predict(model, test[, 1L:4L])
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# A (30x3) matrix with the predictions
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# class1 class2 class3
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# obs1 obs1 obs1
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# obs2 obs2 obs2
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# .... .... ....
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my_preds <- predict(model, test[, 1L:4L])
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# We can also get the predicted scores before the Sigmoid/Softmax application
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my_preds <- predict(model, test[, 1L:4L], type = "raw")
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# We can also get the leaf index
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my_preds <- predict(model, test[, 1L:4L], type = "leaf")
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