SHAP values for feature contributions (#2438)
* SHAP values for feature contributions * Fix commenting error * New polynomial time SHAP value estimation algorithm * Update API to support SHAP values * Fix merge conflicts with updates in master * Correct submodule hashes * Fix variable sized stack allocation * Make lint happy * Add docs * Fix typo * Adjust tolerances * Remove unneeded def * Fixed cpp test setup * Updated R API and cleaned up * Fixed test typo
This commit is contained in:
Scott Lundberg
GitHub
parent
ff9180cd73
commit
78c4188cec
@ -128,6 +128,7 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
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#' It will use all the trees by default (\code{NULL} value).
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#' @param predleaf whether predict leaf index instead.
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#' @param predcontrib whether to return feature contributions to individual predictions instead (see Details).
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#' @param approxcontrib whether to use a fast approximation for feature contributions (see Details).
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#' @param reshape whether to reshape the vector of predictions to a matrix form when there are several
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#' prediction outputs per case. This option has no effect when \code{predleaf = TRUE}.
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#' @param ... Parameters passed to \code{predict.xgb.Booster}
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@ -148,10 +149,11 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
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#'
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#' Setting \code{predcontrib = TRUE} allows to calculate contributions of each feature to
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#' individual predictions. For "gblinear" booster, feature contributions are simply linear terms
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#' (feature_beta * feature_value). For "gbtree" booster, feature contribution is calculated
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#' as a sum of average contribution of that feature's split nodes across all trees to an
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#' individual prediction, following the idea explained in
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#' \url{http://blog.datadive.net/interpreting-random-forests/}.
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#' (feature_beta * feature_value). For "gbtree" booster, feature contributions are SHAP
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#' values (https://arxiv.org/abs/1706.06060) that sum to the difference between the expected output
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#' of the model and the current prediction (where the hessian weights are used to compute the expectations).
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#' Setting \code{approxcontrib = TRUE} approximates these values following the idea explained
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#' in \url{http://blog.datadive.net/interpreting-random-forests/}.
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#'
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#' @return
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#' For regression or binary classification, it returns a vector of length \code{nrows(newdata)}.
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@ -195,7 +197,7 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
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#' # the result is an nsamples X (nfeatures + 1) matrix
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#' pred_contr <- predict(bst, test$data, predcontrib = TRUE)
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#' str(pred_contr)
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#' # verify that contributions' sums are equal to log-odds of predictions (up to foat precision):
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#' # verify that contributions' sums are equal to log-odds of predictions (up to float precision):
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#' summary(rowSums(pred_contr) - qlogis(pred))
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#' # for the 1st record, let's inspect its features that had non-zero contribution to prediction:
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#' contr1 <- pred_contr[1,]
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@ -258,7 +260,7 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
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#' @rdname predict.xgb.Booster
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#' @export
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predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FALSE, ntreelimit = NULL,
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predleaf = FALSE, predcontrib = FALSE, reshape = FALSE, ...) {
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predleaf = FALSE, predcontrib = FALSE, approxcontrib = FALSE, reshape = FALSE, ...) {
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object <- xgb.Booster.complete(object, saveraw = FALSE)
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if (!inherits(newdata, "xgb.DMatrix"))
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@ -270,7 +272,7 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
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if (ntreelimit < 0)
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stop("ntreelimit cannot be negative")
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option <- 0L + 1L * as.logical(outputmargin) + 2L * as.logical(predleaf) + 4L * as.logical(predcontrib)
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option <- 0L + 1L * as.logical(outputmargin) + 2L * as.logical(predleaf) + 4L * as.logical(predcontrib) + 8L * as.logical(approxcontrib)
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ret <- .Call(XGBoosterPredict_R, object$handle, newdata, option[1], as.integer(ntreelimit))
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@ -80,19 +80,26 @@ test_that("predict feature contributions works", {
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expect_equal(dim(pred_contr), c(nrow(sparse_matrix), ncol(sparse_matrix) + 1))
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expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "BIAS"))
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pred <- predict(bst.Tree, sparse_matrix, outputmargin = TRUE)
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expect_lt(max(abs(rowSums(pred_contr) - pred)), 1e-6)
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expect_lt(max(abs(rowSums(pred_contr) - pred)), 1e-5)
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# gbtree binary classifier (approximate method)
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expect_error(pred_contr <- predict(bst.Tree, sparse_matrix, predcontrib = TRUE, approxcontrib = TRUE), regexp = NA)
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expect_equal(dim(pred_contr), c(nrow(sparse_matrix), ncol(sparse_matrix) + 1))
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expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "BIAS"))
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pred <- predict(bst.Tree, sparse_matrix, outputmargin = TRUE)
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expect_lt(max(abs(rowSums(pred_contr) - pred)), 1e-5)
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# gblinear binary classifier
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expect_error(pred_contr <- predict(bst.GLM, sparse_matrix, predcontrib = TRUE), regexp = NA)
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expect_equal(dim(pred_contr), c(nrow(sparse_matrix), ncol(sparse_matrix) + 1))
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expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "BIAS"))
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pred <- predict(bst.GLM, sparse_matrix, outputmargin = TRUE)
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expect_lt(max(abs(rowSums(pred_contr) - pred)), 2e-6)
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expect_lt(max(abs(rowSums(pred_contr) - pred)), 1e-5)
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# manual calculation of linear terms
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coefs <- xgb.dump(bst.GLM)[-c(1,2,4)] %>% as.numeric
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coefs <- c(coefs[-1], coefs[1]) # intercept must be the last
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pred_contr_manual <- sweep(cbind(sparse_matrix, 1), 2, coefs, FUN="*")
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expect_equal(as.numeric(pred_contr), as.numeric(pred_contr_manual), 2e-6)
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expect_equal(as.numeric(pred_contr), as.numeric(pred_contr_manual), 1e-5)
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# gbtree multiclass
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pred <- predict(mbst.Tree, as.matrix(iris[, -5]), outputmargin = TRUE, reshape = TRUE)
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@ -101,7 +108,7 @@ test_that("predict feature contributions works", {
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expect_length(pred_contr, 3)
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for (g in seq_along(pred_contr)) {
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expect_equal(colnames(pred_contr[[g]]), c(colnames(iris[, -5]), "BIAS"))
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expect_lt(max(abs(rowSums(pred_contr[[g]]) - pred[, g])), 2e-6)
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expect_lt(max(abs(rowSums(pred_contr[[g]]) - pred[, g])), 1e-5)
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}
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# gblinear multiclass (set base_score = 0, which is base margin in multiclass)
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@ -115,10 +115,11 @@ class GradientBooster {
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* \param out_contribs output vector to hold the contributions
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* \param ntree_limit limit the number of trees used in prediction, when it equals 0, this means
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* we do not limit number of trees
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* \param approximate use a faster (inconsistent) approximation of SHAP values
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*/
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virtual void PredictContribution(DMatrix* dmat,
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std::vector<bst_float>* out_contribs,
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unsigned ntree_limit = 0) = 0;
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unsigned ntree_limit = 0, bool approximate = false) = 0;
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/*!
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* \brief dump the model in the requested format
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@ -104,13 +104,15 @@ class Learner : public rabit::Serializable {
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* predictor, when it equals 0, this means we are using all the trees
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* \param pred_leaf whether to only predict the leaf index of each tree in a boosted tree predictor
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* \param pred_contribs whether to only predict the feature contributions
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* \param approx_contribs whether to approximate the feature contributions for speed
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*/
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virtual void Predict(DMatrix* data,
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bool output_margin,
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std::vector<bst_float> *out_preds,
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unsigned ntree_limit = 0,
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bool pred_leaf = false,
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bool pred_contribs = false) const = 0;
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bool pred_contribs = false,
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bool approx_contribs = false) const = 0;
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/*!
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* \brief Set additional attribute to the Booster.
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* The property will be saved along the booster.
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@ -144,12 +144,14 @@ class Predictor {
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* \param [in,out] out_contribs The output feature contribs.
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* \param model Model to make predictions from.
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* \param ntree_limit (Optional) The ntree limit.
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* \param approximate Use fast approximate algorithm.
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*/
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virtual void PredictContribution(DMatrix* dmat,
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std::vector<bst_float>* out_contribs,
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const gbm::GBTreeModel& model,
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unsigned ntree_limit = 0) = 0;
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unsigned ntree_limit = 0,
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bool approximate = false) = 0;
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/**
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* \fn static Predictor* Predictor::Create(std::string name);
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@ -14,6 +14,7 @@
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#include <string>
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#include <cstring>
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#include <algorithm>
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#include <tuple>
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#include "./base.h"
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#include "./data.h"
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#include "./logging.h"
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@ -411,6 +412,20 @@ struct RTreeNodeStat {
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int leaf_child_cnt;
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};
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// Used by TreeShap
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// data we keep about our decision path
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// note that pweight is included for convenience and is not tied with the other attributes
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// the pweight of the i'th path element is the permuation weight of paths with i-1 ones in them
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struct PathElement {
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int feature_index;
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bst_float zero_fraction;
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bst_float one_fraction;
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bst_float pweight;
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PathElement() {}
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PathElement(int i, bst_float z, bst_float o, bst_float w) :
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feature_index(i), zero_fraction(z), one_fraction(o), pweight(w) {}
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};
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/*!
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* \brief define regression tree to be the most common tree model.
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* This is the data structure used in xgboost's major tree models.
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@ -482,13 +497,26 @@ class RegTree: public TreeModel<bst_float, RTreeNodeStat> {
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*/
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inline bst_float Predict(const FVec& feat, unsigned root_id = 0) const;
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/*!
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* \brief calculate the feature contributions for the given root
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* \brief calculate the feature contributions (https://arxiv.org/abs/1706.06060) for the tree
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* \param feat dense feature vector, if the feature is missing the field is set to NaN
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* \param root_id starting root index of the instance
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* \param out_contribs output vector to hold the contributions
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*/
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inline void CalculateContributions(const RegTree::FVec& feat, unsigned root_id,
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bst_float *out_contribs) const;
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inline void TreeShap(const RegTree::FVec& feat, bst_float *phi,
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unsigned node_index, unsigned unique_depth,
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PathElement *parent_unique_path, bst_float parent_zero_fraction,
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bst_float parent_one_fraction, int parent_feature_index) const;
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/*!
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* \brief calculate the approximate feature contributions for the given root
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* \param feat dense feature vector, if the feature is missing the field is set to NaN
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* \param root_id starting root index of the instance
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* \param out_contribs output vector to hold the contributions
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*/
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inline void CalculateContributionsApprox(const RegTree::FVec& feat, unsigned root_id,
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bst_float *out_contribs) const;
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/*!
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* \brief get next position of the tree given current pid
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* \param pid Current node id.
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@ -590,7 +618,7 @@ inline bst_float RegTree::FillNodeMeanValue(int nid) {
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return result;
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}
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inline void RegTree::CalculateContributions(const RegTree::FVec& feat, unsigned root_id,
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inline void RegTree::CalculateContributionsApprox(const RegTree::FVec& feat, unsigned root_id,
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bst_float *out_contribs) const {
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CHECK_GT(this->node_mean_values.size(), 0U);
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// this follows the idea of http://blog.datadive.net/interpreting-random-forests/
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@ -617,6 +645,154 @@ inline void RegTree::CalculateContributions(const RegTree::FVec& feat, unsigned
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out_contribs[split_index] += leaf_value - node_value;
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}
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// extend our decision path with a fraction of one and zero extensions
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inline void ExtendPath(PathElement *unique_path, unsigned unique_depth,
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bst_float zero_fraction, bst_float one_fraction, int feature_index) {
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unique_path[unique_depth].feature_index = feature_index;
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unique_path[unique_depth].zero_fraction = zero_fraction;
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unique_path[unique_depth].one_fraction = one_fraction;
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unique_path[unique_depth].pweight = (unique_depth == 0 ? 1 : 0);
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for (int i = unique_depth-1; i >= 0; i--) {
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unique_path[i+1].pweight += one_fraction*unique_path[i].pweight*(i+1)
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/ static_cast<bst_float>(unique_depth+1);
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unique_path[i].pweight = zero_fraction*unique_path[i].pweight*(unique_depth-i)
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/ static_cast<bst_float>(unique_depth+1);
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}
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}
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// undo a previous extension of the decision path
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inline void UnwindPath(PathElement *unique_path, unsigned unique_depth, unsigned path_index) {
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const bst_float one_fraction = unique_path[path_index].one_fraction;
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const bst_float zero_fraction = unique_path[path_index].zero_fraction;
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bst_float next_one_portion = unique_path[unique_depth].pweight;
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for (int i = unique_depth-1; i >= 0; --i) {
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if (one_fraction != 0) {
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const bst_float tmp = unique_path[i].pweight;
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unique_path[i].pweight = next_one_portion*(unique_depth+1)
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/ static_cast<bst_float>((i+1)*one_fraction);
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next_one_portion = tmp - unique_path[i].pweight*zero_fraction*(unique_depth-i)
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/ static_cast<bst_float>(unique_depth+1);
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} else {
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unique_path[i].pweight = (unique_path[i].pweight*(unique_depth+1))
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/ static_cast<bst_float>(zero_fraction*(unique_depth-i));
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}
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}
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for (int i = path_index; i < unique_depth; ++i) {
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unique_path[i].feature_index = unique_path[i+1].feature_index;
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unique_path[i].zero_fraction = unique_path[i+1].zero_fraction;
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unique_path[i].one_fraction = unique_path[i+1].one_fraction;
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}
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}
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// determine what the total permuation weight would be if
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// we unwound a previous extension in the decision path
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inline bst_float UnwoundPathSum(const PathElement *unique_path, unsigned unique_depth,
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unsigned path_index) {
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const bst_float one_fraction = unique_path[path_index].one_fraction;
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const bst_float zero_fraction = unique_path[path_index].zero_fraction;
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bst_float next_one_portion = unique_path[unique_depth].pweight;
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bst_float total = 0;
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for (int i = unique_depth-1; i >= 0; --i) {
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if (one_fraction != 0) {
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const bst_float tmp = next_one_portion*(unique_depth+1)
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/ static_cast<bst_float>((i+1)*one_fraction);
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total += tmp;
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next_one_portion = unique_path[i].pweight - tmp*zero_fraction*((unique_depth-i)
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/ static_cast<bst_float>(unique_depth+1));
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} else {
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total += (unique_path[i].pweight/zero_fraction)/((unique_depth-i)
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/ static_cast<bst_float>(unique_depth+1));
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}
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}
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return total;
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}
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// recursive computation of SHAP values for a decision tree
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inline void RegTree::TreeShap(const RegTree::FVec& feat, bst_float *phi,
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unsigned node_index, unsigned unique_depth,
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PathElement *parent_unique_path, bst_float parent_zero_fraction,
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bst_float parent_one_fraction, int parent_feature_index) const {
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const auto node = (*this)[node_index];
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// extend the unique path
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PathElement *unique_path = parent_unique_path + unique_depth;
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if (unique_depth > 0) std::copy(parent_unique_path, parent_unique_path+unique_depth, unique_path);
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ExtendPath(unique_path, unique_depth, parent_zero_fraction,
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parent_one_fraction, parent_feature_index);
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const unsigned split_index = node.split_index();
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// leaf node
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if (node.is_leaf()) {
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for (int i = 1; i <= unique_depth; ++i) {
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const bst_float w = UnwoundPathSum(unique_path, unique_depth, i);
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const PathElement &el = unique_path[i];
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phi[el.feature_index] += w*(el.one_fraction-el.zero_fraction)*node.leaf_value();
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}
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// internal node
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} else {
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// find which branch is "hot" (meaning x would follow it)
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unsigned hot_index = 0;
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if (feat.is_missing(split_index)) {
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hot_index = node.cdefault();
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} else if (feat.fvalue(split_index) < node.split_cond()) {
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hot_index = node.cleft();
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} else {
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hot_index = node.cright();
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}
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const unsigned cold_index = (hot_index == node.cleft() ? node.cright() : node.cleft());
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const bst_float w = this->stat(node_index).sum_hess;
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const bst_float hot_zero_fraction = this->stat(hot_index).sum_hess/w;
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const bst_float cold_zero_fraction = this->stat(cold_index).sum_hess/w;
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bst_float incoming_zero_fraction = 1;
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bst_float incoming_one_fraction = 1;
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// see if we have already split on this feature,
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// if so we undo that split so we can redo it for this node
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unsigned path_index = 0;
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for (; path_index <= unique_depth; ++path_index) {
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if (unique_path[path_index].feature_index == split_index) break;
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}
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if (path_index != unique_depth+1) {
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incoming_zero_fraction = unique_path[path_index].zero_fraction;
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incoming_one_fraction = unique_path[path_index].one_fraction;
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UnwindPath(unique_path, unique_depth, path_index);
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unique_depth -= 1;
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}
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TreeShap(feat, phi, hot_index, unique_depth+1, unique_path,
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hot_zero_fraction*incoming_zero_fraction, incoming_one_fraction, split_index);
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TreeShap(feat, phi, cold_index, unique_depth+1, unique_path,
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cold_zero_fraction*incoming_zero_fraction, 0, split_index);
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}
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}
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inline void RegTree::CalculateContributions(const RegTree::FVec& feat, unsigned root_id,
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bst_float *out_contribs) const {
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// find the expected value of the tree's predictions
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bst_float base_value = 0.0;
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bst_float total_cover = 0;
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for (unsigned i = 0; i < (*this).param.num_nodes; ++i) {
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const auto node = (*this)[i];
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if (node.is_leaf()) {
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const auto cover = this->stat(i).sum_hess;
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base_value += cover*node.leaf_value();
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total_cover += cover;
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}
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}
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out_contribs[feat.size()] += base_value / total_cover;
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// Preallocate space for the unique path data
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const int maxd = this->MaxDepth(root_id)+1;
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PathElement *unique_path_data = new PathElement[(maxd*(maxd+1))/2];
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TreeShap(feat, out_contribs, root_id, 0, unique_path_data, 1, 1, -1);
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delete[] unique_path_data;
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}
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/*! \brief get next position of the tree given current pid */
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inline int RegTree::GetNext(int pid, bst_float fvalue, bool is_unknown) const {
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bst_float split_value = (*this)[pid].split_cond();
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@ -990,7 +990,7 @@ class Booster(object):
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return self.eval_set([(data, name)], iteration)
|
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def predict(self, data, output_margin=False, ntree_limit=0, pred_leaf=False,
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pred_contribs=False):
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pred_contribs=False, approx_contribs=False):
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"""
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Predict with data.
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@ -1018,9 +1018,12 @@ class Booster(object):
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pred_contribs : bool
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When this option is on, the output will be a matrix of (nsample, nfeats+1)
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with each record indicating the feature contributions of all trees. The sum of
|
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all feature contributions is equal to the prediction. Note that the bias is added
|
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as the final column, on top of the regular features.
|
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with each record indicating the feature contributions (SHAP values) for that
|
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prediction. The sum of all feature contributions is equal to the prediction.
|
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Note that the bias is added as the final column, on top of the regular features.
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|
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approx_contribs : bool
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Approximate the contributions of each feature
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Returns
|
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-------
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@ -1033,6 +1036,8 @@ class Booster(object):
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option_mask |= 0x02
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if pred_contribs:
|
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option_mask |= 0x04
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if approx_contribs:
|
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option_mask |= 0x08
|
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|
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self._validate_features(data)
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|
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|
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@ -758,7 +758,8 @@ XGB_DLL int XGBoosterPredict(BoosterHandle handle,
|
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(option_mask & 1) != 0,
|
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&preds, ntree_limit,
|
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(option_mask & 2) != 0,
|
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(option_mask & 4) != 0);
|
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(option_mask & 4) != 0,
|
||||
(option_mask & 8) != 0);
|
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*out_result = dmlc::BeginPtr(preds);
|
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*len = static_cast<xgboost::bst_ulong>(preds.size());
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API_END();
|
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|
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@ -224,7 +224,7 @@ class GBLinear : public GradientBooster {
|
||||
|
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void PredictContribution(DMatrix* p_fmat,
|
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std::vector<bst_float>* out_contribs,
|
||||
unsigned ntree_limit) override {
|
||||
unsigned ntree_limit, bool approximate) override {
|
||||
if (model.weight.size() == 0) {
|
||||
model.InitModel();
|
||||
}
|
||||
|
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@ -233,8 +233,8 @@ class GBTree : public GradientBooster {
|
||||
|
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void PredictContribution(DMatrix* p_fmat,
|
||||
std::vector<bst_float>* out_contribs,
|
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unsigned ntree_limit) override {
|
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predictor->PredictContribution(p_fmat, out_contribs, model_, ntree_limit);
|
||||
unsigned ntree_limit, bool approximate) override {
|
||||
predictor->PredictContribution(p_fmat, out_contribs, model_, ntree_limit, approximate);
|
||||
}
|
||||
|
||||
std::vector<std::string> DumpModel(const FeatureMap& fmap,
|
||||
|
||||
@ -433,9 +433,9 @@ class LearnerImpl : public Learner {
|
||||
|
||||
void Predict(DMatrix* data, bool output_margin,
|
||||
std::vector<bst_float>* out_preds, unsigned ntree_limit,
|
||||
bool pred_leaf, bool pred_contribs) const override {
|
||||
bool pred_leaf, bool pred_contribs, bool approx_contribs) const override {
|
||||
if (pred_contribs) {
|
||||
gbm_->PredictContribution(data, out_preds, ntree_limit);
|
||||
gbm_->PredictContribution(data, out_preds, ntree_limit, approx_contribs);
|
||||
} else if (pred_leaf) {
|
||||
gbm_->PredictLeaf(data, out_preds, ntree_limit);
|
||||
} else {
|
||||
|
||||
@ -206,9 +206,9 @@ class CPUPredictor : public Predictor {
|
||||
}
|
||||
}
|
||||
|
||||
void PredictContribution(DMatrix* p_fmat,
|
||||
std::vector<bst_float>* out_contribs,
|
||||
const gbm::GBTreeModel& model, unsigned ntree_limit) override {
|
||||
void PredictContribution(DMatrix* p_fmat, std::vector<bst_float>* out_contribs,
|
||||
const gbm::GBTreeModel& model, unsigned ntree_limit,
|
||||
bool approximate) override {
|
||||
const int nthread = omp_get_max_threads();
|
||||
InitThreadTemp(nthread, model.param.num_feature);
|
||||
const MetaInfo& info = p_fmat->info();
|
||||
@ -225,11 +225,13 @@ class CPUPredictor : public Predictor {
|
||||
// make sure contributions is zeroed, we could be reusing a previously
|
||||
// allocated one
|
||||
std::fill(contribs.begin(), contribs.end(), 0);
|
||||
if (approximate) {
|
||||
// initialize tree node mean values
|
||||
#pragma omp parallel for schedule(static)
|
||||
for (bst_omp_uint i = 0; i < ntree_limit; ++i) {
|
||||
model.trees[i]->FillNodeMeanValues();
|
||||
}
|
||||
}
|
||||
// start collecting the contributions
|
||||
dmlc::DataIter<RowBatch>* iter = p_fmat->RowIterator();
|
||||
const std::vector<bst_float>& base_margin = info.base_margin;
|
||||
@ -253,7 +255,11 @@ class CPUPredictor : public Predictor {
|
||||
if (model.tree_info[j] != gid) {
|
||||
continue;
|
||||
}
|
||||
if (!approximate) {
|
||||
model.trees[j]->CalculateContributions(feats, root_id, p_contribs);
|
||||
} else {
|
||||
model.trees[j]->CalculateContributionsApprox(feats, root_id, p_contribs);
|
||||
}
|
||||
}
|
||||
feats.Drop(batch[i]);
|
||||
// add base margin to BIAS
|
||||
|
||||
@ -384,9 +384,10 @@ class GPUPredictor : public xgboost::Predictor {
|
||||
void PredictContribution(DMatrix* p_fmat,
|
||||
std::vector<bst_float>* out_contribs,
|
||||
const gbm::GBTreeModel& model,
|
||||
unsigned ntree_limit) override {
|
||||
unsigned ntree_limit,
|
||||
bool approximate) override {
|
||||
cpu_predictor->PredictContribution(p_fmat, out_contribs, model,
|
||||
ntree_limit);
|
||||
ntree_limit, approximate);
|
||||
}
|
||||
|
||||
void Init(const std::vector<std::pair<std::string, std::string>>& cfg,
|
||||
|
||||
@ -12,6 +12,7 @@ TEST(cpu_predictor, Test) {
|
||||
trees.push_back(std::unique_ptr<RegTree>(new RegTree));
|
||||
trees.back()->InitModel();
|
||||
(*trees.back())[0].set_leaf(1.5f);
|
||||
(*trees.back()).stat(0).sum_hess = 1.0f;
|
||||
gbm::GBTreeModel model(0.5);
|
||||
model.CommitModel(std::move(trees), 0);
|
||||
model.param.num_output_group = 1;
|
||||
@ -50,5 +51,11 @@ TEST(cpu_predictor, Test) {
|
||||
for (int i = 0; i < out_contribution.size(); i++) {
|
||||
ASSERT_EQ(out_contribution[i], 1.5);
|
||||
}
|
||||
|
||||
// Test predict contribution (approximate method)
|
||||
cpu_predictor->PredictContribution(dmat.get(), &out_contribution, model, true);
|
||||
for (int i = 0; i < out_contribution.size(); i++) {
|
||||
ASSERT_EQ(out_contribution[i], 1.5);
|
||||
}
|
||||
}
|
||||
} // namespace xgboost
|
||||
@ -19,6 +19,7 @@ TEST(gpu_predictor, Test) {
|
||||
trees.push_back(std::unique_ptr<RegTree>());
|
||||
trees.back()->InitModel();
|
||||
(*trees.back())[0].set_leaf(1.5f);
|
||||
(*trees.back()).stat(0).sum_hess = 1.0f;
|
||||
gbm::GBTreeModel model(0.5);
|
||||
model.CommitModel(std::move(trees), 0);
|
||||
model.param.num_output_group = 1;
|
||||
|
||||
@ -291,3 +291,18 @@ def test_contributions():
|
||||
|
||||
for max_depth, num_rounds in itertools.product(range(0, 3), range(1, 5)):
|
||||
yield test_fn, max_depth, num_rounds
|
||||
|
||||
# check that we get the right SHAP values for a basic AND example
|
||||
# (https://arxiv.org/abs/1706.06060)
|
||||
X = np.zeros((4, 2))
|
||||
X[0, :] = 1
|
||||
X[1, 0] = 1
|
||||
X[2, 1] = 1
|
||||
y = np.zeros(4)
|
||||
y[0] = 1
|
||||
param = {"max_depth": 2, "base_score": 0.0, "eta": 1.0, "lambda": 0}
|
||||
bst = xgb.train(param, xgb.DMatrix(X, label=y), 1)
|
||||
out = bst.predict(xgb.DMatrix(X[0:1, :]), pred_contribs=True)
|
||||
assert out[0, 0] == 0.375
|
||||
assert out[0, 1] == 0.375
|
||||
assert out[0, 2] == 0.25
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user