#I find the same model hyperparameters will have different results depending on
# ordering of the tune options even when setting a seed before running.
# For example, if I use a grid search for the best hyperparameters, and then
# fit an additional model with the same tune, there are different results.

library(WeightIt)
data(lalonde)

lalonde_formula - as.formula(treat ~ age + educ + re75 + nodegree + married)

# Searching for the optimal tree fit.
set.seed(123)
model1 - weightit(lalonde_formula, data=lalonde,
                    method=gbm,
                    shrinkage=c(.01,0.05),
                    bag.fraction=1,
                    interaction.depth=3,
                    n.trees=10000,
                    criterion=smd.mean,
                    estimand=ATT)

model1$info$best.tune

# The optimal fit has been found. Now I redo this same tree fit using the
# same hyperparameters. Note that the seed is reset as well.
set.seed(123)
model2 - weightit(lalonde_formula, data=lalonde,
                   method=gbm,
                   shrinkage=c(0.05),
                   bag.fraction=1,
                   interaction.depth=3,
                   n.trees=321,
                   criterion=smd.mean,
                   estimand=ATT)

# Ideally the same smd.mean would be found in this as the best tune from model1.
# However, the result is different.
subset(model2$info$tree.val,tree==321)


# The order of parameters given, will change the relative performance of the tunes.
set.seed(123)
model3 - weightit(lalonde_formula, data=lalonde,
                   method=gbm,
                   shrinkage=c(.05,0.01),
                   bag.fraction=1,
                   interaction.depth=3,
                   n.trees=10000,
                   criterion=smd.mean,
                   estimand=ATT)
# This model is the exact same model as in model1 but the best tuning parameters are
# completely different.
model3$info$best.tune

# An example of the set seed picking up where previously finished that occurs
# inside each trees in my view. Apologies if this is stating the obvious but had not
# realised this occurs until someone showed me.
set.seed(123)
rnorm(2)
rnorm(2)

set.seed(123)
rnorm(4)