5879acde9a
* [CI] Move lint to a separate script * [CI] Improved lintr launcher * Add lintr as a separate action * Add custom parsing logic to print out logs * Fix lintr issues in demos * Run R demos * Fix CRAN checks * Install XGBoost into R env before running lintr * Install devtools (needed to run demos)
50 lines
1.2 KiB
R
50 lines
1.2 KiB
R
library(xgboost)
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library(data.table)
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library(cplm)
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data(AutoClaim)
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# auto insurance dataset analyzed by Yip and Yau (2005)
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dt <- data.table(AutoClaim)
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# exclude these columns from the model matrix
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exclude <- c('POLICYNO', 'PLCYDATE', 'CLM_FREQ5', 'CLM_AMT5', 'CLM_FLAG', 'IN_YY')
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# retains the missing values
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# NOTE: this dataset is comes ready out of the box
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options(na.action = 'na.pass')
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x <- sparse.model.matrix(~ . - 1, data = dt[, -exclude, with = FALSE])
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options(na.action = 'na.omit')
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# response
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y <- dt[, CLM_AMT5]
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d_train <- xgb.DMatrix(data = x, label = y, missing = NA)
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# the tweedie_variance_power parameter determines the shape of
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# distribution
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# - closer to 1 is more poisson like and the mass
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# is more concentrated near zero
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# - closer to 2 is more gamma like and the mass spreads to the
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# the right with less concentration near zero
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params <- list(
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objective = 'reg:tweedie',
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eval_metric = 'rmse',
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tweedie_variance_power = 1.4,
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max_depth = 6,
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eta = 1)
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bst <- xgb.train(
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data = d_train,
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params = params,
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maximize = FALSE,
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watchlist = list(train = d_train),
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nrounds = 20)
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var_imp <- xgb.importance(attr(x, 'Dimnames')[[2]], model = bst)
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preds <- predict(bst, d_train)
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rmse <- sqrt(sum(mean((y - preds) ^ 2)))
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