Closes AppliedDataSciencePartners#16

R/buildExplainer.R

@@ -38,7 +38,7 @@
 #' xgb.test.data <- xgb.DMatrix(test.data)
 #'
 #' param <- list(objective = "binary:logistic")
-#' xgb.model <- xgboost(param =param,  data = xgb.train.data, nrounds=3)
+#' xgb.model <- xgboost(param = param, data = xgb.train.data, nrounds=3)
 #'
 #' col_names = colnames(X)
 #'
@@ -47,20 +47,20 @@
 #' trees = xgb.model.dt.tree(col_names, model = xgb.model)
 #'
 #' #### The XGBoost Explainer
-#' explainer = buildExplainer(xgb.model,xgb.train.data, type="binary", base_score = 0.5, n_first_tree = xgb.model$best_ntreelimit - 1)
+#' explainer = buildExplainer(xgb.model, xgb.train.data, type="binary", base_score = 0.5)
 #' pred.breakdown = explainPredictions(xgb.model, explainer, xgb.test.data)
 #'
 #' showWaterfall(xgb.model, explainer, xgb.test.data, test.data,  2, type = "binary")
 #' showWaterfall(xgb.model, explainer, xgb.test.data, test.data,  8, type = "binary")
 
 
-buildExplainer = function(xgb.model, trainingData, type = "binary", base_score = 0.5, n_first_tree = NULL){
+buildExplainer = function(xgb.model, trainingData, type = "binary", base_score = 0.5, n_first_tree = 1){
 
   col_names = attr(trainingData, ".Dimnames")[[2]]
   cat('\nCreating the trees of the xgboost model...')
   trees = xgb.model.dt.tree(col_names, model = xgb.model, n_first_tree = n_first_tree)
   cat('\nGetting the leaf nodes for the training set observations...')
-  nodes.train = predict(xgb.model,trainingData,predleaf =TRUE)
+  nodes.train = predict(xgb.model, trainingData, predleaf = TRUE)
 
   cat('\nBuilding the Explainer...')
   cat('\nSTEP 1 of 2')