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Ensemble.R
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#####################
# 04/01 Modeling #
#####################
load.fun <- function(x) {
x <- as.character(x)
if(isTRUE(x %in% .packages(all.available=TRUE))) {
eval(parse(text=paste("require(", x, ")", sep="")))
print(paste(c(x, " : already installed; requiring"), collapse=''))
} else {
#update.packages()
print(paste(c(x, " : not installed; installing"), collapse=''))
eval(parse(text=paste("install.packages('", x, "')", sep="")))
print(paste(c(x, " : installed and requiring"), collapse=''))
eval(parse(text=paste("require(", x, ")", sep="")))
}
}
packages = c("scales", "digest","colorspace","prodlim","yardstick","dplyr", "bayesplot", "lme4", "rstan", "shinystan", "RcppEigen",
"tidyverse", "tidyr", "AmesHousing", "broom", "caret", "dials", "doParallel", "e1071", "earth",
"ggrepel", "glmnet", "ipred", "klaR", "kknn", "pROC", "rpart", "randomForest",
"sessioninfo", "tidymodels","ranger", "recipes", "workflows", "themis","xgboost",
"sf", "nngeo", "mapview","parsnip", "vctrs", "rsample", "tune")
for(i in seq_along(packages)){
packge <- as.character(packages[i])
load.fun(packge)
}
warnings()
library(dplyr)
library(bayesplot)
library(lme4)
library(rstan)
library(shinystan)
library(RcppEigen)
library(tidyverse)
library(broom)
library(ggplot2)
#Check NAs
sum(is.na(all_2))
which(is.na(all_2$Night.Owl))
#nshifts, Clockwise
#Create dataset without NAs
data.quarter <- all_2 %>%
drop_na()
library(glmnet)
library(tidyr)
set.seed(717)
theme_set(theme_bw())
"%!in%" <- Negate("%in%")
g <- glimpse
data.quarter$STOP_ID<- NULL
#Slipt the data into training and testing sets
data_split <- rsample::initial_split(data, strata = "mean_on", prop = 0.75)
bus_train <- rsample::training(data_split)
bus_test <- rsample::testing(data_split)
table(bus_train$label)
cv_splits_geo <- rsample::group_vfold_cv(bus_train, strata = "mean_on", group = "label")
print(cv_splits_geo)
#Create recipe
model_rec <- recipe(mean_on ~ ., data = bus_train) %>% #the "." means using every variable we have in the training dataset
update_role(label, new_role = "label") %>% #This is more like to keep the neighborhood variable out of the model
step_other(label, threshold = 0.005) %>%
step_dummy(all_nominal(), -label) %>%
step_log(mean_on) %>%
step_zv(all_predictors()) %>%
step_center(all_predictors(), -mean_on) %>%
step_scale(all_predictors(), -mean_on) #%>% #put on standard deviation scale
#step_ns(Latitude, Longitude, options = list(df = 4))
model_rec
#Build the model
lm_plan <-
parsnip::linear_reg() %>%
parsnip::set_engine("lm")
glmnet_plan <-
parsnip::linear_reg() %>%
parsnip::set_args(penalty = tune()) %>%
parsnip::set_args(mixture = tune()) %>%
parsnip::set_engine("glmnet")
rf_plan <- parsnip::rand_forest() %>%
parsnip::set_args(mtry = tune()) %>%
parsnip::set_args(min_n = tune()) %>%
parsnip::set_args(trees = 1000) %>%
parsnip::set_engine("ranger", importance = "impurity") %>%
parsnip::set_mode("regression")
XGB_plan <- parsnip::boost_tree() %>%
parsnip::set_args(mtry = tune()) %>%
parsnip::set_args(min_n = tune()) %>%
parsnip::set_args(trees = 100) %>%
parsnip::set_engine("xgboost") %>%
parsnip::set_mode("regression")
#
glmnet_grid <- expand.grid(penalty = seq(0, 1, by = .25),
mixture = seq(0,1,0.25))
rf_grid <- expand.grid(mtry = c(2,5),
min_n = c(1,5))
xgb_grid <- expand.grid(mtry = c(3,5),
min_n = c(1,5))
#Create workflow
lm_wf <-
workflows::workflow() %>%
add_recipe(model_rec) %>%
add_model(lm_plan)
glmnet_wf <-
workflow() %>%
add_recipe(model_rec) %>%
add_model(glmnet_plan)
rf_wf <-
workflow() %>%
add_recipe(model_rec) %>%
add_model(rf_plan)
xgb_wf <-
workflow() %>%
add_recipe(model_rec) %>%
add_model(XGB_plan)
# fit model to workflow and calculate metrics
control <- tune::control_resamples(save_pred = TRUE, verbose = TRUE)
library(tune)
library(yardstick)
?tune_grid
?metric_set
lm_tuned <- lm_wf %>%
fit_resamples(.,
resamples = cv_splits_geo,
control = control,
metrics = metric_set(rmse, rsq))
glmnet_tuned <- glmnet_wf %>%
tune_grid(.,
resamples = cv_splits_geo,
grid = glmnet_grid,
control = control,
metrics = metric_set(rmse, rsq))
rf_tuned <- rf_wf %>%
tune::tune_grid(.,
resamples = cv_splits_geo,
grid = rf_grid,
control = control,
metrics = metric_set(rmse, rsq))
xgb_tuned <- xgb_wf %>%
tune::tune_grid(.,
resamples = cv_splits_geo,
grid = xgb_grid,
control = control,
metrics = metric_set(rmse, rsq))
show_best(lm_tuned, metric = "rmse", n = 15, maximize = FALSE)
show_best(glmnet_tuned, metric = "rmse", n = 15, maximize = FALSE)
show_best(rf_tuned, metric = "rmse", n = 15, maximize = FALSE)
show_best(xgb_tuned, metric = "rmse", n = 15, maximize = FALSE)
lm_best_params <- select_best(lm_tuned, metric = "rmse", maximize = FALSE)
glmnet_best_params <- select_best(glmnet_tuned, metric = "rmse", maximize = FALSE)
rf_best_params <- select_best(rf_tuned, metric = "rmse", maximize = FALSE)
xgb_best_params <- select_best(xgb_tuned, metric = "rmse", maximize = FALSE)
#Final workflow
lm_best_wf <- finalize_workflow(lm_wf, lm_best_params)
glmnet_best_wf <- finalize_workflow(glmnet_wf, glmnet_best_params)
rf_best_wf <- finalize_workflow(rf_wf, rf_best_params)
xgb_best_wf <- finalize_workflow(xgb_wf, xgb_best_params)
lm_val_fit_geo <- lm_best_wf %>%
last_fit(split = data_split,
control = control,
metrics = metric_set(rmse, rsq))
glmnet_val_fit_geo <- glmnet_best_wf %>%
last_fit(split = data_split,
control = control,
metrics = metric_set(rmse, rsq))
rf_val_fit_geo <- rf_best_wf %>%
last_fit(split = data_split,
control = control,
metrics = metric_set(rmse, rsq))
xgb_val_fit_geo <- xgb_best_wf %>%
last_fit(split = data_split,
control = control,
metrics = metric_set(rmse, rsq))
####################################Model Validation
# Pull best hyperparam preds from out-of-fold predictions
lm_best_OOF_preds <- collect_predictions(lm_tuned)
glmnet_best_OOF_preds <- collect_predictions(glmnet_tuned) %>%
filter(penalty == glmnet_best_params$penalty[1] & mixture == glmnet_best_params$mixture[1])
rf_best_OOF_preds <- collect_predictions(rf_tuned) %>%
filter(mtry == rf_best_params$mtry[1] & min_n == rf_best_params$min_n[1])
xgb_best_OOF_preds <- collect_predictions(xgb_tuned) %>%
filter(mtry == xgb_best_params$mtry[1] & min_n == xgb_best_params$min_n[1])
# collect validation set predictions from last_fit model
lm_val_pred_geo <- collect_predictions(lm_val_fit_geo)
glmnet_val_pred_geo <- collect_predictions(glmnet_val_fit_geo)
rf_val_pred_geo <- collect_predictions(rf_val_fit_geo)
xgb_val_pred_geo <- collect_predictions(xgb_val_fit_geo)
# Aggregate OOF predictions (they do not overlap with Validation prediction set)
OOF_preds <- rbind(data.frame(dplyr::select(lm_best_OOF_preds, .pred, mean_on), model = "lm"),
data.frame(dplyr::select(glmnet_best_OOF_preds, .pred, mean_on), model = "glmnet"),
data.frame(dplyr::select(rf_best_OOF_preds, .pred, mean_on), model = "RF"),
data.frame(dplyr::select(xgb_best_OOF_preds, .pred, mean_on), model = "xgb")) %>%
group_by(model) %>%
mutate(.pred = exp(.pred),
mean_on = exp(mean_on),
RMSE = yardstick::rmse_vec(mean_on, .pred),
MAE = yardstick::mae_vec(mean_on, .pred),
MAPE = yardstick::mape_vec(mean_on, .pred)) %>%
ungroup()
# Aggregate predictions from Validation set
val_preds <- rbind(data.frame(lm_val_pred_geo, model = "lm"),
data.frame(glmnet_val_pred_geo, model = "glmnet"),
data.frame(rf_val_pred_geo, model = "rf"),
data.frame(xgb_val_pred_geo, model = "xgb")) %>%
left_join(., data %>%
rowid_to_column(var = ".row") %>%
dplyr::select(label, .row),
by = ".row") %>%
group_by(model) %>%
mutate(.pred = exp(.pred),
mean_on = exp(mean_on),
RMSE = yardstick::rmse_vec(mean_on, .pred),
MAE = yardstick::mae_vec(mean_on, .pred),
MAPE = yardstick::mape_vec(mean_on, .pred)) %>%
ungroup()
###################################Modelling part ends here, below are the visualizations##############
#MAPE chart
ggplot(data = val_preds %>%
dplyr::select(model, MAPE) %>%
distinct() ,
aes(x = model, y = MAPE, group = 1)) +
geom_path(color = "blue") +
geom_label(aes(label = paste0(round(MAPE,1),"%"))) +
labs(title= "MAPE of each model on the testing set")
theme_bw()
#MAE chart
ggplot(data = val_preds %>%
dplyr::select(model, MAE) %>%
distinct() ,
aes(x = model, y = MAE, group = 1)) +
geom_path(color = "blue") +
geom_label(aes(label = paste0(round(MAE,1)))) +
labs(title= "MAE of each model on the testing set")
theme_bw()
#Predicted vs Observed
ggplot(val_preds, aes(x =.pred, y = mean_on, group = model)) +
geom_point() +
geom_abline(linetype = "dashed", color = "red") +
geom_smooth(method = "lm", color = "blue") +
coord_equal() +
facet_wrap(~model, nrow = 2) +
labs(title="Predicted vs Observed on the testing set", subtitle= "blue line is predicted value")
theme_bw()
#Neighborhood validation
val_MAPE_by_hood <- val_preds %>%
group_by(label, model) %>%
summarise(RMSE = yardstick::rmse_vec(mean_on, .pred),
MAE = yardstick::mae_vec(mean_on, .pred),
MAPE = yardstick::mape_vec(mean_on, .pred)) %>%
ungroup()
#Barchart of the MAE in each neighborhood
plotTheme <- function(base_size = 20) {
theme(
text = element_text( color = "black"),
plot.title = element_text(size = 80,colour = "black"),
plot.subtitle = element_text(face="italic"),
plot.caption = element_text(hjust=0),
axis.ticks = element_blank(),
panel.background = element_blank(),
panel.grid.major = element_line("grey80", size = 0.1),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=2),
strip.background = element_rect(fill = "grey80", color = "white"),
strip.text = element_text(size=60),
axis.title = element_text(size=60),
axis.text = element_text(size=45),
plot.background = element_blank(),
legend.background = element_blank(),
legend.title = element_text(colour = "black", face = "italic", size= 80),
legend.text = element_text(colour = "black", face = "italic",size = 80),
strip.text.x = element_text(size = 50)
)
}
palette4 <- c("#eff3ff", "#bdd7e7","#6baed6","#2171b5")
val_MAPE_by_hood %>%
dplyr::select(label, model, MAE) %>%
gather(Variable, MAE, -model, -label) %>%
ggplot(aes(label, MAE)) +
geom_bar(aes(fill = model), position = "dodge", stat="identity") +
scale_fill_manual(values = "Blues") +
facet_wrap(~label,scales="free", ncol=6)+
labs(title = "Mean Absolute Errors by model specification and neighborhood") +
plotTheme()
#Map of MAE in each neighborhood
#Load neighborhood data
nhood <- st_read("https://data.austintexas.gov/resource/nz5f-3t2e.geojson")%>%
st_set_crs(4326)%>%
st_transform(2278)
#Add geometry to the MAE
MAE.nhood <- merge(nhood, val_MAPE_by_hood, by.x="label", by.y="label", all.y=TRUE)
#Produce the map
q5 <- function(variable) {as.factor(ntile(variable, 5))}
qBr <- function(df, variable, rnd) {
if (missing(rnd)) {
as.character(quantile(round(df[[variable]],0),
c(.01,.2,.4,.6,.8), na.rm=T))
} else if (rnd == FALSE | rnd == F) {
as.character(formatC(quantile(df[[variable]]), digits = 3),
c(.01,.2,.4,.6,.8), na.rm=T)
}
}
library(RColorBrewer)
mapTheme <- function(base_size = 12) {
theme(
text = element_text( color = "black"),
plot.title = element_text(size = 14,colour = "black"),
plot.subtitle=element_text(face="italic"),
plot.caption=element_text(hjust=0),
axis.ticks = element_blank(),
panel.background = element_blank(),axis.title = element_blank(),
axis.text = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = NA, fill=NA, size=2)
)
}
#Map: MAPE of lm
MAE.nhood%>%
filter(model=="lm") %>%
ggplot() +
# geom_sf(data = nhoods, fill = "grey40") +
geom_sf(aes(fill = q5(MAPE))) +
scale_fill_brewer(palette = "Blues",
aesthetics = "fill",
labels=qBr(MAE.nhood,"MAPE"),
name="Quintile\nBreaks, (%)") +
labs(title="MAPE of lm in Neighborhoods") +
mapTheme()
#Map: MAPE of glmnet
MAE.nhood%>%
filter(model=="glmnet") %>%
ggplot() +
# geom_sf(data = nhoods, fill = "grey40") +
geom_sf(aes(fill = q5(MAPE))) +
scale_fill_brewer(palette = "Blues",
aesthetics = "fill",
labels=qBr(MAE.nhood,"MAPE"),
name="Quintile\nBreaks, (%)") +
labs(title="MAPE of glmnet in Neighborhoods") +
mapTheme()
#MAPE of rf
MAE.nhood%>%
filter(model=="rf") %>%
ggplot() +
# geom_sf(data = nhoods, fill = "grey40") +
geom_sf(aes(fill = q5(MAPE))) +
scale_fill_brewer(palette = "Blues",
aesthetics = "fill",
labels=qBr(MAE.nhood,"MAPE"),
name="Quintile\nBreaks, (%)") +
labs(title="MAPE of rf in Neighborhoods") +
mapTheme()
#MAPE of xgb
MAE.nhood%>%
filter(model=="xgb") %>%
ggplot() +
# geom_sf(data = nhoods, fill = "grey40") +
geom_sf(aes(fill = q5(MAPE))) +
scale_fill_brewer(palette = "Blues",
aesthetics = "fill",
labels=qBr(MAE.nhood,"MAPE"),
name="Quintile\nBreaks, (%)") +
labs(title="MAPE of xgb in Neighborhoods") +
mapTheme()
########################Test on school District##########
#Create recipe
model_rec <- recipe(mean_on ~ ., data = bus_train) %>% #the "." means using every variable we have in the training dataset
update_role(TRUSTEE, new_role = "TRUSTEE") %>% #This is more like to keep the neighborhood variable out of the model
step_other(TRUSTEE, threshold = 0.005) %>%
step_dummy(all_nominal(), -TRUSTEE) %>%
step_log(mean_on) %>%
step_zv(all_predictors()) %>%
step_center(all_predictors(), -mean_on) %>%
step_scale(all_predictors(), -mean_on) #%>% #put on standard deviation scale
#step_ns(Latitude, Longitude, options = list(df = 4))
model_rec
#Build the model
lm_plan <-
parsnip::linear_reg() %>%
parsnip::set_engine("lm")
glmnet_plan <-
parsnip::linear_reg() %>%
parsnip::set_args(penalty = tune()) %>%
parsnip::set_args(mixture = tune()) %>%
parsnip::set_engine("glmnet")
rf_plan <- parsnip::rand_forest() %>%
parsnip::set_args(mtry = tune()) %>%
parsnip::set_args(min_n = tune()) %>%
parsnip::set_args(trees = 1000) %>%
parsnip::set_engine("ranger", importance = "impurity") %>%
parsnip::set_mode("regression")
XGB_plan <- parsnip::boost_tree() %>%
parsnip::set_args(mtry = tune()) %>%
parsnip::set_args(min_n = tune()) %>%
parsnip::set_args(trees = 100) %>%
parsnip::set_engine("xgboost") %>%
parsnip::set_mode("regression")
#
glmnet_grid <- expand.grid(penalty = seq(0, 1, by = .25),
mixture = seq(0,1,0.25))
rf_grid <- expand.grid(mtry = c(2,5),
min_n = c(1,5))
xgb_grid <- expand.grid(mtry = c(3,5),
min_n = c(1,5))
#Create workflow
lm_wf <-
workflows::workflow() %>%
add_recipe(model_rec) %>%
add_model(lm_plan)
glmnet_wf <-
workflow() %>%
add_recipe(model_rec) %>%
add_model(glmnet_plan)
rf_wf <-
workflow() %>%
add_recipe(model_rec) %>%
add_model(rf_plan)
xgb_wf <-
workflow() %>%
add_recipe(model_rec) %>%
add_model(XGB_plan)
# fit model to workflow and calculate metrics
control <- tune::control_resamples(save_pred = TRUE, verbose = TRUE)
library(tune)
library(yardstick)
?tune_grid
?metric_set
lm_tuned <- lm_wf %>%
fit_resamples(.,
resamples = cv_splits_geo,
control = control,
metrics = metric_set(rmse, rsq))
glmnet_tuned <- glmnet_wf %>%
tune_grid(.,
resamples = cv_splits_geo,
grid = glmnet_grid,
control = control,
metrics = metric_set(rmse, rsq))
rf_tuned <- rf_wf %>%
tune::tune_grid(.,
resamples = cv_splits_geo,
grid = rf_grid,
control = control,
metrics = metric_set(rmse, rsq))
xgb_tuned <- xgb_wf %>%
tune::tune_grid(.,
resamples = cv_splits_geo,
grid = xgb_grid,
control = control,
metrics = metric_set(rmse, rsq))
show_best(lm_tuned, metric = "rmse", n = 15, maximize = FALSE)
show_best(glmnet_tuned, metric = "rmse", n = 15, maximize = FALSE)
show_best(rf_tuned, metric = "rmse", n = 15, maximize = FALSE)
show_best(xgb_tuned, metric = "rmse", n = 15, maximize = FALSE)
lm_best_params <- select_best(lm_tuned, metric = "rmse", maximize = FALSE)
glmnet_best_params <- select_best(glmnet_tuned, metric = "rmse", maximize = FALSE)
rf_best_params <- select_best(rf_tuned, metric = "rmse", maximize = FALSE)
xgb_best_params <- select_best(xgb_tuned, metric = "rmse", maximize = FALSE)
#Final workflow
lm_best_wf <- finalize_workflow(lm_wf, lm_best_params)
glmnet_best_wf <- finalize_workflow(glmnet_wf, glmnet_best_params)
rf_best_wf <- finalize_workflow(rf_wf, rf_best_params)
xgb_best_wf <- finalize_workflow(xgb_wf, xgb_best_params)
lm_val_fit_geo <- lm_best_wf %>%
last_fit(split = data_split,
control = control,
metrics = metric_set(rmse, rsq))
glmnet_val_fit_geo <- glmnet_best_wf %>%
last_fit(split = data_split,
control = control,
metrics = metric_set(rmse, rsq))
rf_val_fit_geo <- rf_best_wf %>%
last_fit(split = data_split,
control = control,
metrics = metric_set(rmse, rsq))
xgb_val_fit_geo <- xgb_best_wf %>%
last_fit(split = data_split,
control = control,
metrics = metric_set(rmse, rsq))
####################################Model Validation
# Pull best hyperparam preds from out-of-fold predictions
lm_best_OOF_preds <- collect_predictions(lm_tuned)
glmnet_best_OOF_preds <- collect_predictions(glmnet_tuned) %>%
filter(penalty == glmnet_best_params$penalty[1] & mixture == glmnet_best_params$mixture[1])
rf_best_OOF_preds <- collect_predictions(rf_tuned) %>%
filter(mtry == rf_best_params$mtry[1] & min_n == rf_best_params$min_n[1])
xgb_best_OOF_preds <- collect_predictions(xgb_tuned) %>%
filter(mtry == xgb_best_params$mtry[1] & min_n == xgb_best_params$min_n[1])
# collect validation set predictions from last_fit model
lm_val_pred_geo <- collect_predictions(lm_val_fit_geo)
glmnet_val_pred_geo <- collect_predictions(glmnet_val_fit_geo)
rf_val_pred_geo <- collect_predictions(rf_val_fit_geo)
xgb_val_pred_geo <- collect_predictions(xgb_val_fit_geo)
# Aggregate OOF predictions (they do not overlap with Validation prediction set)
OOF_preds <- rbind(data.frame(dplyr::select(lm_best_OOF_preds, .pred, mean_on), model = "lm"),
data.frame(dplyr::select(glmnet_best_OOF_preds, .pred, mean_on), model = "glmnet"),
data.frame(dplyr::select(rf_best_OOF_preds, .pred, mean_on), model = "RF"),
data.frame(dplyr::select(xgb_best_OOF_preds, .pred, mean_on), model = "xgb")) %>%
group_by(model) %>%
mutate(.pred = exp(.pred),
mean_on = exp(mean_on),
RMSE = yardstick::rmse_vec(mean_on, .pred),
MAE = yardstick::mae_vec(mean_on, .pred),
MAPE = yardstick::mape_vec(mean_on, .pred)) %>%
ungroup()
# Aggregate predictions from Validation set
val_preds <- rbind(data.frame(lm_val_pred_geo, model = "lm"),
data.frame(glmnet_val_pred_geo, model = "glmnet"),
data.frame(rf_val_pred_geo, model = "rf"),
data.frame(xgb_val_pred_geo, model = "xgb")) %>%
left_join(., data %>%
rowid_to_column(var = ".row") %>%
dplyr::select(TRUSTEE, .row),
by = ".row") %>%
group_by(model) %>%
mutate(.pred = exp(.pred),
mean_on = exp(mean_on),
RMSE = yardstick::rmse_vec(mean_on, .pred),
MAE = yardstick::mae_vec(mean_on, .pred),
MAPE = yardstick::mape_vec(mean_on, .pred)) %>%
ungroup()
###################################Modelling part ends here, below are the visualizations##############
#MAPE chart
ggplot(data = val_preds %>%
dplyr::select(model, MAPE) %>%
distinct() ,
aes(x = model, y = MAPE, group = 1)) +
geom_path(color = "blue") +
geom_label(aes(label = paste0(round(MAPE,1),"%"))) +
labs(title= "MAPE of each model on the testing set")
theme_bw()
#MAE chart
ggplot(data = val_preds %>%
dplyr::select(model, MAE) %>%
distinct() ,
aes(x = model, y = MAE, group = 1)) +
geom_path(color = "blue") +
geom_label(aes(label = paste0(round(MAE,1)))) +
labs(title= "MAE of each model on the testing set")
theme_bw()
#Predicted vs Observed
ggplot(val_preds, aes(x =.pred, y = mean_on, group = model)) +
geom_point() +
geom_abline(linetype = "dashed", color = "red") +
geom_smooth(method = "lm", color = "blue") +
coord_equal() +
facet_wrap(~model, nrow = 2) +
labs(title="Predicted vs Observed on the testing set", subtitle= "blue line is predicted value")
theme_bw()
#Neighborhood validation
val_MAPE_by_district <- val_preds %>%
group_by(TRUSTEE, model) %>%
summarise(RMSE = yardstick::rmse_vec(mean_on, .pred),
MAE = yardstick::mae_vec(mean_on, .pred),
MAPE = yardstick::mape_vec(mean_on, .pred)) %>%
ungroup()
#Barchart of the MAE in each neighborhood
plotTheme <- function(base_size = 20) {
theme(
text = element_text( color = "black"),
plot.title = element_text(size = 50,colour = "black"),
plot.subtitle = element_text(face="italic"),
plot.caption = element_text(hjust=0),
axis.ticks = element_blank(),
panel.background = element_blank(),
panel.grid.major = element_line("grey80", size = 0.1),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=2),
strip.background = element_rect(fill = "grey80", color = "white"),
strip.text = element_text(size=30),
axis.title = element_text(size=30),
axis.text = element_text(size=20),
plot.background = element_blank(),
legend.background = element_blank(),
legend.title = element_text(colour = "black", face = "italic", size= 50),
legend.text = element_text(colour = "black", face = "italic",size = 50),
strip.text.x = element_text(size = 30)
)
}
val_MAPE_by_district %>%
dplyr::select(TRUSTEE, model, MAE) %>%
gather(Variable, MAE, -model, -TRUSTEE) %>%
ggplot(aes(TRUSTEE, MAE)) +
geom_bar(aes(fill = model), position = "dodge", stat="identity") +
scale_fill_manual(values = palette4) +
facet_wrap(~TRUSTEE,scales="free", ncol=8)+
ylim(0,200)+
labs(title = "Mean Absolute Errors by model specification and school districts") +
plotTheme()
#Map of MAE in each school district
#Load school district data
TrusteeDist <-
st_read("C:/Upenn/Practicum/Data/Trustee_Boundaries/Trustee.shp")%>%
st_transform(2278)
#Add geometry to the MAE
MAE.district <- merge(TrusteeDist, val_MAPE_by_district, by.x="TRUSTEE", by.y="TRUSTEE", all.y=TRUE)
#Produce the map
#Map: MAPE of lm
MAE.district%>%
filter(model=="lm") %>%
ggplot() +
# geom_sf(data = nhoods, fill = "grey40") +
geom_sf(aes(fill = q5(MAPE))) +
scale_fill_brewer(palette = "Blues",
aesthetics = "fill",
labels=qBr(MAE.district,"MAPE"),
name="Quintile\nBreaks, (%)") +
labs(title="MAPE of lm in School Districts") +
mapTheme()
#Map: MAPE of glmnet
MAE.district%>%
filter(model=="glmnet") %>%
ggplot() +
# geom_sf(data = nhoods, fill = "grey40") +
geom_sf(aes(fill = q5(MAPE))) +
scale_fill_brewer(palette = "Blues",
aesthetics = "fill",
labels=qBr(MAE.district,"MAPE"),
name="Quintile\nBreaks, (%)") +
labs(title="MAPE of glmnet in School Districts") +
mapTheme()
#MAPE of rf
MAE.district%>%
filter(model=="rf") %>%
ggplot() +
# geom_sf(data = nhoods, fill = "grey40") +
geom_sf(aes(fill = q5(MAPE))) +
scale_fill_brewer(palette = "Blues",
aesthetics = "fill",
labels=qBr(MAE.district,"MAPE"),
name="Quintile\nBreaks, (%)") +
labs(title="MAPE of rf in School Districts") +
mapTheme()
#MAPE of xgb
MAE.district%>%
filter(model=="xgb") %>%
ggplot() +
# geom_sf(data = nhoods, fill = "grey40") +
geom_sf(aes(fill = q5(MAPE))) +
scale_fill_brewer(palette = "Blues",
aesthetics = "fill",
labels=qBr(MAE.district,"MAPE"),
name="Quintile\nBreaks, (%)") +
labs(title="MAPE of xgb in School Districts") +
mapTheme()
#####################################################################Modeling Improvement################################3
sum(is.na(all))
which(is.na(all))
which(is.na(all_2))
data2 <- all%>%
drop_na()
#Slipt the data into training and testing sets
data_split2 <- rsample::initial_split(data2, strata = "mean_on", prop = 0.75)
bus_train2 <- rsample::training(data_split2)
bus_test2 <- rsample::testing(data_split2)
table(bus_train2$label)
cv_splits_geo <- rsample::group_vfold_cv(bus_train2, strata = "mean_on", group = "label")
print(cv_splits_geo)
#Create recipe
model_rec <- recipe(mean_on ~ ., data = bus_train2) %>% #the "." means using every variable we have in the training dataset
update_role(label, new_role = "label") %>% #This is more like to keep the neighborhood variable out of the model
step_other(label, threshold = 0.005) %>%
step_dummy(all_nominal(), -label) %>%
step_log(mean_on) %>%
step_zv(all_predictors()) %>%
step_center(all_predictors(), -mean_on) %>%
step_scale(all_predictors(), -mean_on) #%>% #put on standard deviation scale
#step_ns(Latitude, Longitude, options = list(df = 4))
model_rec
#Build the model
lm_plan <-
parsnip::linear_reg() %>%
parsnip::set_engine("lm")
glmnet_plan <-
parsnip::linear_reg() %>%
parsnip::set_args(penalty = tune()) %>%
parsnip::set_args(mixture = tune()) %>%
parsnip::set_engine("glmnet")
rf_plan <- parsnip::rand_forest() %>%
parsnip::set_args(mtry = tune()) %>%
parsnip::set_args(min_n = tune()) %>%
parsnip::set_args(trees = 1000) %>%
parsnip::set_engine("ranger", importance = "impurity") %>%
parsnip::set_mode("regression")
XGB_plan <- parsnip::boost_tree() %>%
parsnip::set_args(mtry = tune()) %>%
parsnip::set_args(min_n = tune()) %>%
parsnip::set_args(trees = 100) %>%
parsnip::set_engine("xgboost") %>%
parsnip::set_mode("regression")
#
glmnet_grid <- expand.grid(penalty = seq(0, 1, by = .25),
mixture = seq(0,1,0.25))
rf_grid <- expand.grid(mtry = c(2,5),
min_n = c(1,5))
xgb_grid <- expand.grid(mtry = c(3,5),
min_n = c(1,5))
#Create workflow
lm_wf <-
workflows::workflow() %>%
add_recipe(model_rec) %>%
add_model(lm_plan)
glmnet_wf <-
workflow() %>%
add_recipe(model_rec) %>%
add_model(glmnet_plan)
rf_wf <-
workflow() %>%
add_recipe(model_rec) %>%
add_model(rf_plan)
xgb_wf <-
workflow() %>%
add_recipe(model_rec) %>%
add_model(XGB_plan)
# fit model to workflow and calculate metrics
control <- tune::control_resamples(save_pred = TRUE, verbose = TRUE)
library(tune)
library(yardstick)
?tune_grid
?metric_set
lm_tuned <- lm_wf %>%
fit_resamples(.,
resamples = cv_splits_geo,
control = control,
metrics = metric_set(rmse, rsq))
glmnet_tuned <- glmnet_wf %>%
tune_grid(.,
resamples = cv_splits_geo,
grid = glmnet_grid,
control = control,
metrics = metric_set(rmse, rsq))
rf_tuned <- rf_wf %>%
tune::tune_grid(.,
resamples = cv_splits_geo,
grid = rf_grid,
control = control,
metrics = metric_set(rmse, rsq))
xgb_tuned <- xgb_wf %>%
tune::tune_grid(.,
resamples = cv_splits_geo,
grid = xgb_grid,
control = control,
metrics = metric_set(rmse, rsq))
show_best(lm_tuned, metric = "rmse", n = 15, maximize = FALSE)
show_best(glmnet_tuned, metric = "rmse", n = 15, maximize = FALSE)
show_best(rf_tuned, metric = "rmse", n = 15, maximize = FALSE)
show_best(xgb_tuned, metric = "rmse", n = 15, maximize = FALSE)
lm_best_params <- select_best(lm_tuned, metric = "rmse", maximize = FALSE)
glmnet_best_params <- select_best(glmnet_tuned, metric = "rmse", maximize = FALSE)
rf_best_params <- select_best(rf_tuned, metric = "rmse", maximize = FALSE)
xgb_best_params <- select_best(xgb_tuned, metric = "rmse", maximize = FALSE)
#Final workflow
lm_best_wf <- finalize_workflow(lm_wf, lm_best_params)
glmnet_best_wf <- finalize_workflow(glmnet_wf, glmnet_best_params)
rf_best_wf <- finalize_workflow(rf_wf, rf_best_params)
xgb_best_wf <- finalize_workflow(xgb_wf, xgb_best_params)
lm_val_fit_geo <- lm_best_wf %>%
last_fit(split = data_split2,
control = control,
metrics = metric_set(rmse, rsq))
glmnet_val_fit_geo <- glmnet_best_wf %>%
last_fit(split = data_split2,
control = control,
metrics = metric_set(rmse, rsq))
rf_val_fit_geo <- rf_best_wf %>%
last_fit(split = data_split2,
control = control,
metrics = metric_set(rmse, rsq))
xgb_val_fit_geo <- xgb_best_wf %>%
last_fit(split = data_split2,
control = control,
metrics = metric_set(rmse, rsq))
####################################Model Validation
# Pull best hyperparam preds from out-of-fold predictions
lm_best_OOF_preds <- collect_predictions(lm_tuned)
glmnet_best_OOF_preds <- collect_predictions(glmnet_tuned) %>%
filter(penalty == glmnet_best_params$penalty[1] & mixture == glmnet_best_params$mixture[1])
rf_best_OOF_preds <- collect_predictions(rf_tuned) %>%
filter(mtry == rf_best_params$mtry[1] & min_n == rf_best_params$min_n[1])
xgb_best_OOF_preds <- collect_predictions(xgb_tuned) %>%
filter(mtry == xgb_best_params$mtry[1] & min_n == xgb_best_params$min_n[1])
# collect validation set predictions from last_fit model
lm_val_pred_geo <- collect_predictions(lm_val_fit_geo)
glmnet_val_pred_geo <- collect_predictions(glmnet_val_fit_geo)
rf_val_pred_geo <- collect_predictions(rf_val_fit_geo)
xgb_val_pred_geo <- collect_predictions(xgb_val_fit_geo)
# Aggregate OOF predictions (they do not overlap with Validation prediction set)
OOF_preds <- rbind(data.frame(dplyr::select(lm_best_OOF_preds, .pred, mean_on), model = "lm"),
data.frame(dplyr::select(glmnet_best_OOF_preds, .pred, mean_on), model = "glmnet"),
data.frame(dplyr::select(rf_best_OOF_preds, .pred, mean_on), model = "RF"),
data.frame(dplyr::select(xgb_best_OOF_preds, .pred, mean_on), model = "xgb")) %>%
group_by(model) %>%
mutate(.pred = exp(.pred),
mean_on = exp(mean_on),
RMSE = yardstick::rmse_vec(mean_on, .pred),
MAE = yardstick::mae_vec(mean_on, .pred),
MAPE = yardstick::mape_vec(mean_on, .pred)) %>%
ungroup()
# Aggregate predictions from Validation set
val_preds <- rbind(data.frame(lm_val_pred_geo, model = "lm"),
data.frame(glmnet_val_pred_geo, model = "glmnet"),
data.frame(rf_val_pred_geo, model = "rf"),
data.frame(xgb_val_pred_geo, model = "xgb")) %>%
left_join(., data2 %>%
rowid_to_column(var = ".row") %>%
dplyr::select(label, .row),
by = ".row") %>%
group_by(model) %>%
mutate(.pred = exp(.pred),
mean_on = exp(mean_on),
RMSE = yardstick::rmse_vec(mean_on, .pred),
MAE = yardstick::mae_vec(mean_on, .pred),
MAPE = yardstick::mape_vec(mean_on, .pred)) %>%
ungroup()
###################################Modelling part ends here, below are the visualizations##############
#MAPE chart
ggplot(data = val_preds %>%
dplyr::select(model, MAPE) %>%
distinct() ,
aes(x = model, y = MAPE, group = 1)) +
geom_path(color = "blue") +
geom_label(aes(label = paste0(round(MAPE,1),"%"))) +
labs(title= "MAPE of each model on the testing set")
theme_bw()
#MAE chart
ggplot(data = val_preds %>%
dplyr::select(model, MAE) %>%
distinct() ,
aes(x = model, y = MAE, group = 1)) +
geom_path(color = "blue") +
geom_label(aes(label = paste0(round(MAE,1)))) +
labs(title= "MAE of each model on the testing set")
theme_bw()
#Predicted vs Observed
ggplot(val_preds, aes(x =.pred, y = mean_on, group = model)) +
geom_point() +
geom_abline(linetype = "dashed", color = "red") +
geom_smooth(method = "lm", color = "blue") +
coord_equal() +
facet_wrap(~model, nrow = 2) +
labs(title="Predicted vs Observed on the testing set", subtitle= "blue line is predicted value")
theme_bw()
#Neighborhood validation
val_MAPE_by_hood <- val_preds %>%
group_by(label, model) %>%
summarise(RMSE = yardstick::rmse_vec(mean_on, .pred),
MAE = yardstick::mae_vec(mean_on, .pred),
MAPE = yardstick::mape_vec(mean_on, .pred)) %>%
ungroup()
#Barchart of the MAE in each neighborhood
plotTheme <- function(base_size = 20) {
theme(
text = element_text( color = "black"),
plot.title = element_text(size = 80,colour = "black"),
plot.subtitle = element_text(face="italic"),
plot.caption = element_text(hjust=0),
axis.ticks = element_blank(),
panel.background = element_blank(),
panel.grid.major = element_line("grey80", size = 0.1),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=2),
strip.background = element_rect(fill = "grey80", color = "white"),
strip.text = element_text(size=60),
axis.title = element_text(size=60),
axis.text = element_text(size=45),
plot.background = element_blank(),
legend.background = element_blank(),
legend.title = element_text(colour = "black", face = "italic", size= 80),
legend.text = element_text(colour = "black", face = "italic",size = 80),
strip.text.x = element_text(size = 50)
)
}
palette4 <- c("#eff3ff", "#bdd7e7","#6baed6","#2171b5")
val_MAPE_by_hood %>%
dplyr::select(label, model, MAE) %>%
gather(Variable, MAE, -model, -label) %>%
ggplot(aes(label, MAE)) +
geom_bar(aes(fill = model), position = "dodge", stat="identity") +
scale_fill_manual(values = "Blues") +
facet_wrap(~label,scales="free", ncol=6)+
labs(title = "Mean Absolute Errors by model specification and neighborhood") +
plotTheme()
#Map of MAE in each neighborhood
#Load neighborhood data
nhood <- st_read("https://data.austintexas.gov/resource/nz5f-3t2e.geojson")%>%
st_set_crs(4326)%>%
st_transform(2278)
#Add geometry to the MAE
MAE.nhood <- merge(nhood, val_MAPE_by_hood, by.x="label", by.y="label", all.y=TRUE)