92 lines
3.9 KiB
R
92 lines
3.9 KiB
R
#' Create new features from a previously learned model
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#'
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#' May improve the learning by adding new features to the training data based on the decision trees from a previously learned model.
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#'
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#' @param model decision tree boosting model learned on the original data
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#' @param data original data (usually provided as a \code{dgCMatrix} matrix)
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#' @param ... currently not used
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#'
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#' @return \code{dgCMatrix} matrix including both the original data and the new features.
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#'
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#' @details
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#' This is the function inspired from the paragraph 3.1 of the paper:
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#'
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#' \strong{Practical Lessons from Predicting Clicks on Ads at Facebook}
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#'
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#' \emph{(Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yan, xin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers,
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#' Joaquin Quinonero Candela)}
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#'
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#' International Workshop on Data Mining for Online Advertising (ADKDD) - August 24, 2014
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#'
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#' \url{https://research.facebook.com/publications/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}.
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#'
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#' Extract explaining the method:
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#'
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#' "We found that boosted decision trees are a powerful and very
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#' convenient way to implement non-linear and tuple transformations
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#' of the kind we just described. We treat each individual
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#' tree as a categorical feature that takes as value the
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#' index of the leaf an instance ends up falling in. We use
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#' 1-of-K coding of this type of features.
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#'
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#' For example, consider the boosted tree model in Figure 1 with 2 subtrees,
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#' where the first subtree has 3 leafs and the second 2 leafs. If an
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#' instance ends up in leaf 2 in the first subtree and leaf 1 in
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#' second subtree, the overall input to the linear classifier will
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#' be the binary vector \code{[0, 1, 0, 1, 0]}, where the first 3 entries
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#' correspond to the leaves of the first subtree and last 2 to
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#' those of the second subtree.
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#'
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#' [...]
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#'
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#' We can understand boosted decision tree
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#' based transformation as a supervised feature encoding that
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#' converts a real-valued vector into a compact binary-valued
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#' vector. A traversal from root node to a leaf node represents
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#' a rule on certain features."
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#'
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#' @examples
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#' data(agaricus.train, package='xgboost')
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#' data(agaricus.test, package='xgboost')
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#' dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label, nthread = 2))
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#' dtest <- with(agaricus.test, xgb.DMatrix(data, label = label, nthread = 2))
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#'
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#' param <- list(max_depth=2, eta=1, objective='binary:logistic')
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#' nrounds = 4
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#'
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#' bst = xgb.train(params = param, data = dtrain, nrounds = nrounds, nthread = 2)
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#'
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#' # Model accuracy without new features
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#' accuracy.before <- sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label) /
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#' length(agaricus.test$label)
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#'
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#' # Convert previous features to one hot encoding
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#' new.features.train <- xgb.create.features(model = bst, agaricus.train$data)
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#' new.features.test <- xgb.create.features(model = bst, agaricus.test$data)
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#'
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#' # learning with new features
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#' new.dtrain <- xgb.DMatrix(
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#' data = new.features.train, label = agaricus.train$label, nthread = 2
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#' )
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#' new.dtest <- xgb.DMatrix(
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#' data = new.features.test, label = agaricus.test$label, nthread = 2
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#' )
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#' watchlist <- list(train = new.dtrain)
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#' bst <- xgb.train(params = param, data = new.dtrain, nrounds = nrounds, nthread = 2)
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#'
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#' # Model accuracy with new features
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#' accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) /
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#' length(agaricus.test$label)
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#'
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#' # Here the accuracy was already good and is now perfect.
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#' cat(paste("The accuracy was", accuracy.before, "before adding leaf features and it is now",
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#' accuracy.after, "!\n"))
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#'
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#' @export
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xgb.create.features <- function(model, data, ...) {
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check.deprecation(...)
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pred_with_leaf <- predict(model, data, predleaf = TRUE)
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cols <- lapply(as.data.frame(pred_with_leaf), factor)
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cbind(data, sparse.model.matrix(~ . -1, cols)) # nolint
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}
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