## ----setup, include=FALSE-----------------------------------------------------
use_saved_results <- TRUE

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  echo = TRUE,
  eval = !use_saved_results,
  message = FALSE,
  warning = FALSE
)

if (use_saved_results) {
  results <- readRDS("vignette_fs.rds")
  model_df <- results$model_df
  boot_df <- results$boot_df
}

## ----eval=TRUE, class.source = "fold-hide"------------------------------------
library(dplyr); library(tidyr); library(purrr) # Data wrangling
library(ggplot2); library(stringr) # Plotting
library(tidyfit) # Model fitting

# Max model size
MODEL_SIZE <- 10

## ----eval=TRUE----------------------------------------------------------------
# Load the data
data <- readRDS("FRED-MD.rds")

## ----eval=TRUE----------------------------------------------------------------
data <- data %>% 
  arrange(date) %>% 
  # Shift the target by 1 month
  mutate(Target = lead(INDPRO)) %>% 
  drop_na %>% 
  select(-date)

data

## -----------------------------------------------------------------------------
#  # Correlation
#  algorithms_df <- data %>%
#    regress(Target ~ ., Correlation = m("cor"))

## -----------------------------------------------------------------------------
#  # RReliefF
#  algorithms_df <- algorithms_df %>%
#    bind_rows(
#      data %>%
#        regress(Target ~ ., RReliefF = m("relief"))
#    )

## -----------------------------------------------------------------------------
#  # Information Gain
#  algorithms_df <- algorithms_df %>%
#    bind_rows(
#      data %>%
#        # Split target into buckets
#        mutate(Target = as.factor(ntile(Target, 10))) %>%
#        regress(Target ~ .,
#                `Information Gain` = m("relief", estimator = "InfGain"))
#    )

## -----------------------------------------------------------------------------
#  # Forward Selection
#  algorithms_df <- algorithms_df %>%
#    bind_rows(
#      data %>%
#        regress(Target ~ .,
#                `Forward Selection` = m("subset", method = "forward", nvmax = MODEL_SIZE))
#    )

## -----------------------------------------------------------------------------
#  # Backward Elimination
#  algorithms_df <- algorithms_df %>%
#    bind_rows(
#      data %>%
#        regress(Target ~ .,
#                `Backward Elimination` = m("subset", method = "backward", nvmax = MODEL_SIZE))
#    )

## -----------------------------------------------------------------------------
#  # MRMR
#  algorithms_df <- algorithms_df %>%
#    bind_rows(
#      data %>%
#        regress(Target ~ ., MRMR = m("mrmr", feature_count = MODEL_SIZE))
#    )

## -----------------------------------------------------------------------------
#  # LASSO
#  algorithms_df <- algorithms_df %>%
#    bind_rows(
#      data %>%
#        regress(Target ~ .,
#                `LASSO` = m("lasso", pmax = MODEL_SIZE + 1),
#                .cv = "rolling_origin",
#                .cv_args = list(initial = 120, assess = 24, skip = 23)
#                )
#    )

## -----------------------------------------------------------------------------
#  # BMA
#  algorithms_df <- algorithms_df %>%
#    bind_rows(
#      data %>%
#        regress(Target ~ .,
#                BMA = m("bma", burn = 10000, iter = 100000,
#                        mprior.size = MODEL_SIZE, mcmc = "rev.jump"))
#    )

## -----------------------------------------------------------------------------
#  # Random Forest Importance
#  nonlinear_algorithms_df <- data %>%
#        regress(Target ~ ., `RF Importance` = m("rf"))

## -----------------------------------------------------------------------------
#  coef_df <- coef(algorithms_df) %>%
#    unnest(model_info) %>%
#    bind_rows(explain(nonlinear_algorithms_df))

## -----------------------------------------------------------------------------
#  model_df <- coef_df %>%
#    # Always remove the intercept
#    filter(term != "(Intercept)") %>%
#  
#    mutate(selected = case_when(
#      # Extract top 10 largest scores
#      model %in% c("Correlation", "RReliefF", "Information Gain") ~
#        rank(-abs(estimate)) <= MODEL_SIZE,
#      # BMA features are selected using the posterior inclusion probability
#      model == "BMA" ~ rank(-pip) <= MODEL_SIZE,
#      # The RF importance is stored in a separate column (%IncMSE)
#      model == "RF Importance" ~ rank(-importance) <= MODEL_SIZE,
#      # For all other methods keep all features
#      TRUE ~ TRUE
#    )) %>%
#  
#    # Keep only included terms
#    filter(selected) %>%
#    select(model, term)

## -----------------------------------------------------------------------------
#  model_df <- model_df %>%
#    bind_rows(tibble(
#      model = "Domain Expert",
#      term = c("NAPMNOI", "ANDENOx", "CLAIMSx", "ACOGNO",
#               "S&P 500", "T10YFFM", "PERMIT", "AWHMAN")
#    ))

## ----eval=T, class.source = 'fold-hide', fig.width=7, fig.height=2.5, fig.align='center'----
model_df %>% 
  # Add 'FALSE' entries, when a feature is not selected
  mutate(selected = TRUE) %>% 
  spread(term, selected) %>% 
  gather("term", "selected", -model) %>% 
  # Plotting color
  mutate(selected = ifelse(is.na(selected), "white", "darkblue")) %>% 
  # Fix plotting order
  group_by(term) %>% 
  mutate(selected_sum = sum(selected=="darkblue")) %>% 
  ungroup %>% 
  arrange(desc(selected_sum)) %>% 
  mutate(term = factor(term, levels = unique(term))) %>% 
  mutate(model = factor(model, levels = unique(model_df$model))) %>% 
  ggplot(aes(term, model)) +
  geom_tile(aes(fill = selected)) +
  theme_bw(8) +
  scale_fill_identity() +
  xlab(element_blank()) + ylab(element_blank()) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

## -----------------------------------------------------------------------------
#  model_names <- unique(model_df$model)
#  
#  # Retrieve selected variables
#  selected_vars_list <- model_names %>%
#    map(function(mod) {
#      model_df %>%
#        filter(model == mod) %>%
#        pull(term)
#    })
#  names(selected_vars_list) <- model_names
#  
#  # Bootstrap resampling & regression
#  boot_models_df <- selected_vars_list %>%
#    map_dfr(function(selected_vars) {
#      data %>%
#        select(all_of(c("Target", selected_vars))) %>%
#  
#        regress(Target ~ .,
#                # Use linear regression
#                m("lm"),
#                # Bootstrap settings (see ?rsample::bootstraps)
#                .cv = "bootstraps", .cv_args = list(times = 100),
#                # Make sure the results for each slice are returned
#                .force_cv = T, .return_slices = T)
#    }, .id = "model")
#  
#  # Finally, extract R2 from the model results
#  boot_df <- boot_models_df %>%
#    mutate(R2 = map_dbl(model_object, function(obj) summary(obj)$r.squared)) %>%
#    select(model, R2)

## ----eval=T, class.source = 'fold-hide', fig.width=7, fig.height=2.5, fig.align='center'----
boot_df %>% 
  group_by(model) %>% 
  mutate(upper = mean(R2) + 2 * sd(R2) / sqrt(n()),
         lower = mean(R2) - 2 * sd(R2) / sqrt(n())) %>% 
  mutate(model = str_wrap(model, 10)) %>% 
  mutate(model = factor(model, levels = str_wrap(unique(model_df$model), 10))) %>% 
  ggplot(aes(model)) +
  geom_errorbar(aes(ymin = lower, ymax = upper), linewidth = 0.25, width = 0.25) +
  theme_bw(8) +
  xlab(element_blank()) + ylab("R2 statistic")

