```{r}
#| label: fig-active-accounts
#| eval: false
#library(betareg)
library(lme4)
activity <- arrow::read_feather(
    "data/scratch/activity.feather",
    col_select = c("server", "logins")
  ) %>%
  arrange(desc(logins)) %>% 
  mutate(server_count = logins)

account_data <- inner_join(accounts, activity, by="server") %>%
  mutate(active = active_time >= 45)

a_data <- account_data %>%
  #mutate(active = active_time >= 45) %>%
  group_by(server) %>%
  summarize(active_prop = sum(active)/n(), active_count = sum(active), count=n()) %>%
  inner_join(., activity, by="server")

a_model <- glmer(active ~ log1p(logins) + (1|server), data=account_data, family=binomial)
  #betareg(active_prop ~ log10(count), data = a_data)

logins_seq <- seq(min(log1p(account_data$logins)), max(log1p(account_data$logins)), length.out = 100)
a_pred <- predict(
  a_model,
  newdata = data.frame(logins = logins_seq, server = factor(1)),
  type = "response",
  re.form = NA)
pred_data <- data.frame(logins = logins_seq, active_prop = a_pred)

a_data %>%
  mutate(logins = log1p(logins)) %>%
  ggplot(aes(y=active_prop, x=logins)) +
    geom_point(alpha=0.1) +
    # help here
    #geom_line(aes(y = a_pred)) +
    geom_line(data = pred_data, aes(x = logins, y = active_prop), color = "red") + # Use pred_data for line
    labs(
      y = "Active after 45 Days",
      x = "Accounts"
    ) +
    scale_x_continuous(labels = scales::comma) +
    #scale_y_log10() +
    theme_bw_small_labels()
```

```{r}
#| eval: false
library(fable)
#library(fable.binary)
library(tsibble)
library(lubridate)

ad_time <- account_data |>
  mutate(created_at = yearweek(created_at)) |>
  group_by(server, created_at) |>
  summarize(count = n(), active = sum(active)) |>
  as_tsibble(key="server", index=created_at)
```

```{r}
#| eval: false
fit <- ad_time |>
  model(
    logistic = LOGISTIC(active ~ fourier(K = 5, period = "year"))
  )
```

```{r}
#| eval: false
ad_time |>
  filter(server == "mastodon.social") |>
  sample_n(100) |>
  autoplot(active)
```


```{r}
#| label: fig-account-activity-prop
#| fig-cap: "Account Activity Over Time"
#| fig-height: 4
#| eval: false
study_period <- 45
last_day <- "2024-01-15"
#formerly accounts_processed_recent
#server_counts <- arrow::read_feather(
#    "data/scratch/accounts.feather", 
#    col_select=c("server", "username", "created_at", "bot")
#  ) %>%
#  filter(created_at <= "2023-03-01") %>%
#  filter(!bot) %>%
#  group_by(server) %>%
#  summarize(server_count = n()) %>%
#  arrange(desc(server_count)) %>%
#  mutate(server_count_bin = floor(log10(server_count)))

metadata <- arrow::read_feather("data/scratch/metadata.feather", col_select=c("server", "user_count")) %>%
  arrange(desc(user_count)) %>%
  mutate(server_count = user_count) %>%
  mutate(server_count_bin = floor(log10(server_count))) %>%
  mutate(server_count_bin = ifelse(server_count_bin >= 4, 4, server_count_bin)) %>%
  mutate(server_count_bin = ifelse(server_count_bin <= 2, 2, server_count_bin))

activity <- arrow::read_feather(
    "data/scratch/activity.feather",
    col_select = c("server", "logins")
  ) %>%
  arrange(desc(logins)) %>% 
  mutate(server_count = logins) %>% 
  mutate(server_count_bin = floor(log10(server_count))) %>%
  # Merge 4 and 5
  mutate(server_count_bin = ifelse(server_count_bin >= 5, 4, server_count_bin)) %>%
  # Merge below 2
  #mutate(server_count_bin = ifelse((server_count_bin <= 2) & (server_count_bin >= 1), 2, server_count_bin)) %>%
  mutate(server_count_bin = ifelse(server_count == 0, -1, server_count_bin))

jm <- arrow::read_feather("data/scratch/joinmastodon.feather")

a <- accounts %>%
  filter(!has_moved) %>%
  filter(locked == FALSE) %>%
  anti_join(., moved_to, by=c("username"="moved_acct", "server"="moved_server")) %>%
  filter(created_at >= "2022-08-14") %>%
  filter(created_at < "2022-10-03") %>%
  #filter(created_at >= "2023-10-15") %>%
  #filter(created_at < "2023-11-15") %>%
  inner_join(activity, by="server") %>%
  filter(created_at < last_status_at) %>%
  #mutate(large_server = server_count > 1000) %>%
  mutate(active_time = as.integer(active_time)) %>%
  mutate(active_time_weeks = active_time) %>%
  mutate(status = ifelse(active, 0, 1)) %>%
  mutate(jm = server %in% jm$domain) %>% 
  mutate(follows_someone = following_count == 0) %>%
  mutate(has_a_follower = followers_count > 0)
  #filter(server_count > 0)
  

survfit2(Surv(active_time_weeks, status) ~ strata(server_count_bin) + 1, data = a) %>% #  strata(server_count_bin)
  ggsurvfit() +
  add_confidence_interval() +
  scale_y_continuous(limits = c(0, 1)) +
  labs(
    y = "Overall survival probability",
    x = "Time (days)",
  ) +
  #scale_x_continuous(
  #  breaks = seq(0, max(a$active_time_weeks, na.rm = TRUE), by = 4), 
  #  labels = seq(0, max(a$active_time_weeks, na.rm = TRUE), by = 4)
  #) +
  theme_bw_small_labels() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

```{r}
#| eval: false
library(coxme)

sel_a <- a %>%
  #filter(server != "mastodon.social") %>%
  mutate(is_ms = server == "mastodon.social") %>%
  filter(!is_ms) %>%
  filter(jm) %>% inner_join(., jm, by=c("server"="domain")) %>%
  #mutate(is_general = category=="general") %>% 
  rowwise() %>%
  mutate(is_regional = "regional" %in% categories) %>%
  mutate(is_general = ("general" %in% categories)) %>%
  mutate(is_neither = !(is_regional | is_regional)) %>%
  mutate(is_en = language == "en") %>%
  mutate(n_categories = length(categories) - is_regional - is_general) %>%
  mutate(many_categories = n_categories > 0) %>%
  mutate(is_large = last_week_users >= 585) %>%
  #mutate(follows_someone = followers_count > 0) %>% filter(server_count > 1) %>%
  #filter(followers_count < 250) %>%
  ungroup

sel_a <- 
  inner_join(sel_a, (sel_a %>% group_by(server) %>% summarize(cohort_size=n())), by="server")
#cx <- coxme(Surv(active_time_weeks, status) ~ is_large + is_general + approval_required + (1|server), data = sel_a, x=TRUE) 
#cx <- coxph(Surv(active_time_weeks, status) ~ many_categories + is_general*is_regional + is_general:log1p(server_count), data = sel_a, x=TRUE) 
#coxme(Surv(active_time_weeks, status) ~ is_regional + many_categories + is_general*log10(server_count) + (1|server), data = sel_a, x=TRUE)

# coxme(Surv(active_time_weeks, status) ~ many_categories + is_general + is_regional + is_general*log10(cohort_size) + (1|server), data = sel_a, x=TRUE)

cx <- coxph(Surv(active_time_weeks, status) ~ many_categories + is_general + is_regional + is_general:log1p(cohort_size), data = sel_a, x=TRUE) # log10(server_count)
cz <- cox.zph(cx)
#plot(cz)
cx
cz
```

```{r}
#| eval: false
options(rf.cores=2, mc.cores=2)
for_data <- sel_a %>%
  filter(!is_general) %>%
  mutate(rn=row_number())

set.seed(123)

data_test <- for_data %>% slice_sample(n = floor(0.4 * nrow(for_data)))
data_train <- for_data %>% slice(-pull(data_test,rn))

obj <- rfsrc.fast(Surv(active_time_weeks, status) ~ server_count, data = data_train, ntree=100, forest=TRUE)

pred <- predict(obj, data_test)
reg.smp.o <- subsample(obj, B = 10, verbose = TRUE)#, subratio = .5)


obj <- rfsrc(Surv(active_time_weeks, status) ~ is_neither + is_general*server_count, data = for_data, ntree=50, forest=TRUE, importance=TRUE)
#predictions <- predict(obj, newdata = newData)$predicted
#plot(get.tree(obj, 1))
reg.smp.o <- subsample(obj, B = 10, verbose = TRUE)#, subratio = .5)
plot.subsample(reg.smp.o)
```