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## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
warning = FALSE,
message = FALSE,
fig.height = 5,
fig.width = 8,
fig.align = "center"
)
## -----------------------------------------------------------------------------
library(xgboost)
library(ggplot2)
library(shapviz)
head(miami)
x_coord <- c("LATITUDE", "LONGITUDE")
x_nongeo <- c("TOT_LVG_AREA", "LND_SQFOOT", "structure_quality", "age")
x <- c(x_coord, x_nongeo)
# Train/valid split
set.seed(1)
ix <- sample(nrow(miami), 0.8 * nrow(miami))
X_train <- data.matrix(miami[ix, x])
X_valid <- data.matrix(miami[-ix, x])
y_train <- log(miami$SALE_PRC[ix])
y_valid <- log(miami$SALE_PRC[-ix])
# Fit XGBoost model with early stopping
dtrain <- xgb.DMatrix(X_train, label = y_train, nthread = 1)
dvalid <- xgb.DMatrix(X_valid, label = y_valid, nthread = 1)
params <- list(
learning_rate = 0.2, objective = "reg:squarederror", max_depth = 5, nthread = 1
)
fit <- xgb.train(
params = params,
data = dtrain,
watchlist = list(valid = dvalid),
early_stopping_rounds = 20,
nrounds = 1000,
callbacks = list(cb.print.evaluation(period = 100))
)
## -----------------------------------------------------------------------------
sv <- shapviz(fit, X_pred = X_valid)
sv_dependence(
sv,
v = c("TOT_LVG_AREA", "structure_quality", "LONGITUDE", "LATITUDE"),
alpha = 0.2
)
# And now the two-dimensional plot of the sum of SHAP values
sv_dependence2D(sv, x = "LONGITUDE", y = "LATITUDE") +
coord_equal()
## -----------------------------------------------------------------------------
# Extend the feature set
more_geo <- c("CNTR_DIST", "OCEAN_DIST", "RAIL_DIST", "HWY_DIST")
x2 <- c(x, more_geo)
X_train2 <- data.matrix(miami[ix, x2])
X_valid2 <- data.matrix(miami[-ix, x2])
dtrain2 <- xgb.DMatrix(X_train2, label = y_train, nthread = 1)
dvalid2 <- xgb.DMatrix(X_valid2, label = y_valid, nthread = 1)
# Build interaction constraint vector
ic <- c(
list(which(x2 %in% c(x_coord, more_geo)) - 1),
as.list(which(x2 %in% x_nongeo) - 1)
)
# Modify parameters
params$interaction_constraints <- ic
fit2 <- xgb.train(
params = params,
data = dtrain2,
watchlist = list(valid = dvalid2),
early_stopping_rounds = 20,
nrounds = 1000,
callbacks = list(cb.print.evaluation(period = 100))
)
# SHAP analysis
sv2 <- shapviz(fit2, X_pred = X_valid2)
# Two selected features: Thanks to additivity, structure_quality can be read as
# Ceteris Paribus
sv_dependence(sv2, v = c("structure_quality", "LONGITUDE"), alpha = 0.2)
# Total geographic effect (Ceteris Paribus thanks to additivity)
sv_dependence2D(sv2, x = "LONGITUDE", y = "LATITUDE", add_vars = more_geo) +
coord_equal()
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