## -----------------------------------------------------------------------------
library(nowcastr)
nc_eval_obj <-
  nowcast_demo %>%
  nowcast_eval(
    n_past = 10,
    max_delay = 5,
    max_reportunits = 8,
    do_model_fitting = FALSE,
    col_date_occurrence = date_occurrence,
    col_date_reporting = date_report,
    col_value = value,
    group_cols = "group",
    time_units = "weeks"
  )


## -----------------------------------------------------------------------------
nc_eval_obj@detail
nc_eval_obj@summary


## ----fig.width=9, fig.asp=5.5/10, out.width="100%"----------------------------
#| warning: false
plot_nowcast_eval(nc_eval_obj, delay = 0)


## ----fig.width=9, fig.asp=5.5/10, out.width="100%"----------------------------
#| warning: false
# library(ggplot2)
plot_nowcast_eval_by_delay(nc_eval_obj, indicator = "smape_diff_med") +
  ggplot2::facet_wrap(. ~ group, scales = "free_y")


## ----fig.width=9, fig.asp=5.5/10, out.width="100%"----------------------------
#| warning: false
plot_nowcast_eval_detail(nc_eval_obj, delay = 0)


## ----fig.width=9, fig.asp=5.5/10, out.width="100%"----------------------------
#| warning: false

nc_eval_obj_with_fill <-
  nowcast_demo %>%
  fill_future_reported_values(
    col_date_occurrence = date_occurrence,
    col_date_reporting = date_report,
    col_value = value,
    group_cols = "group",
    max_delay = "auto"
  ) %>%
  nowcast_eval(
    n_past = 10,
    max_delay = 5,
    max_reportunits = 8,
    do_model_fitting = FALSE,
    col_date_occurrence = date_occurrence,
    col_date_reporting = date_report,
    col_value = value,
    group_cols = "group",
    time_units = "weeks"
  )


## ----fig.width=9, fig.asp=5.5/10, out.width="100%"----------------------------
#| warning: false

library(dplyr)
indicator <- "smape_diff_med"

scenario_a <- nc_eval_obj@summary %>%
  dplyr::select("group", "delay", "smape_diff_med") %>%
  dplyr::mutate(scenario = "No fill")

scenario_b <- nc_eval_obj_with_fill@summary %>%
  dplyr::select("group", "delay", "smape_diff_med") %>%
  dplyr::mutate(scenario = "Fill")

## quick mean of everything
dplyr::bind_rows(scenario_a, scenario_b) %>%
  dplyr::filter(delay <= 3) %>% ## predictions for older data are not that interesting
  dplyr::summarise(
    .by = c(scenario),
    avg_smape_diff_med = mean(smape_diff_med, na.rm = T)
  ) %>%
  dplyr::mutate(tag = dplyr::if_else(avg_smape_diff_med == max(avg_smape_diff_med), "best overall", "")) %>%
  print()


## ----fig.width=9, fig.asp=5.5/10, out.width="100%"----------------------------
#| warning: false

## Comparison plot
library(ggplot2)
dplyr::bind_rows(scenario_a, scenario_b) %>%
  dplyr::summarise(
    .by = c(delay, scenario, group),
    smape_diff_med = mean(smape_diff_med, na.rm = T)
  ) %>%
  ggplot(aes(x = delay, y = smape_diff_med, color = scenario)) +
  geom_point() +
  geom_line() +
  facet_wrap(~group, scales = "free_y") +
  # ggplot2::scale_y_continuous(labels = scales::label_percent()) +
  theme_nowcastr() +
  labs(
    y = "Differential sMAPE",
    x = "Delay",
    color = "Scenario",
    title = "Compare Differential sMAPE of 2 scenarios",
    subtitle = "Higher is better"
  )


## ----fig.width=9, fig.asp=5.5/10, out.width="100%"----------------------------
#| warning: false

# devtools::load_all("~/gh/nowcastr")

library(dplyr)
{
  results <- tibble()
  results_details <- tibble()
  for (ii in seq(2, 15, 1)) {
    nc_eval_obj_i <-
      nowcast_demo %>%
      filter(group == "Syndromic ILI") %>%
      nowcast_eval(
        n_past = 99,
        max_delay = 3,
        max_reportunits = ii, ## this varies
        do_model_fitting = FALSE,
        col_date_occurrence = date_occurrence,
        col_date_reporting = date_report,
        col_value = value,
        group_cols = "group",
        time_units = "weeks"
      )
    res_i <- nc_eval_obj_i@summary %>% mutate(max_reportunits = ii)
    res_d_i <- nc_eval_obj_i@detail %>% mutate(max_reportunits = ii)

    results <- bind_rows(results, res_i)
    results_details <- bind_rows(results_details, res_d_i)

    # avg <- mean(res_i$smape_diff_med) |> signif(2)
    # message(max_reportunits, ": ", avg)
  }
}

library(ggplot2)
results %>%
  filter(group == "Syndromic ILI") %>%
  filter(delay < 3) %>%
  summarise(
    .by = c(group, max_reportunits, delay),
    avg_smape_diff_med = mean(smape_diff_med, na.rm = T)
  ) %>%
  mutate(
    .by = c(group, delay),
    best = if_else(avg_smape_diff_med == max(avg_smape_diff_med), "best", "")
  ) %>%
  mutate(delay = factor(delay)) %>%
  ggplot(aes(x = max_reportunits, y = avg_smape_diff_med, color = delay, group = delay)) +
  geom_line() +
  geom_point() +
  theme_nowcastr() +
  # geom_text(aes(label = best)) +
  # geom_point(color='red') +
  # geom_point(aes(fill = best == 'best')) +
  geom_point(shape = 21, aes(fill = best == "best")) +
  scale_fill_manual(values = c("FALSE" = "transparent", "TRUE" = "red"), guide = "none") +
  scale_color_viridis_d(option = "mako", begin = .4, end = .8) +
  # ggplot2::scale_y_continuous(labels = scales::label_percent()) +
  # facet_wrap(. ~ delay, scales = "free_y") +
  facet_wrap(. ~ group, scales = "free_y") +
  labs(y = "Differential sMAPE", x = "max_reportunits", color = "Delay")


results %>%
  filter(group == "Syndromic ILI") %>%
  filter(delay < 3) %>%
  summarise(
    .by = c(max_reportunits),
    n = n(),
    avg_smape_diff_med = mean(smape_diff_med, na.rm = T)
  ) %>%
  mutate(tag = if_else(avg_smape_diff_med == max(avg_smape_diff_med), "best overall", "")) %>%
  print()