library(tidyverse)

# load in the human labels and for each task filer, @ the task level
# GET the proportion of Observed bug behavior  + Expected Behavior
# GET the proportion of Solution Discussion  + Solution Usage 
human_csv <-"~/dsl/092225_info_matt_labels.csv"
human_df <- read.csv(human_csv, header = TRUE) 

#task_authors <- human_df %>%
#  filter(comment_type == "task_description") %>%
#  select(TaskPHID, AuthorPHID) %>%
#  rename(Task_AuthorPHID = AuthorPHID)

#result <- task_authors %>%
#  rowwise() %>%
#  mutate(
#    bug_prop = {
#      rows_by_author <- human_df %>% filter(AuthorPHID == task_authorPHID)
#      mean(rows_by_author$label %in% c("Observed bug behavior", "Expected behavior"))
#    }
#  ) %>%
#  ungroup()
human_result <- human_df %>%
  group_by(TaskPHID) %>%
  summarise(
    human_BE_prop = mean(human_label %in% c("OBSERVED BUG BEHAVIOR", "EXPECTED BEHAVIOR")),
    human_SOL_prop = mean(human_label %in% c("SOLUTION DISCUSSION", "SOLUTION USAGE"))
  )

# load in the existing df and @ the task level get 
# for each task filer, 
# GET the proportion of Observed bug behavior  + Expected Behavior
# GET the proportion of Solution Discussion  + Solution Usage 
main_csv <-"~/analysis_data/100625_unified_w_affil.csv"
main_df <- read.csv(main_csv, header = TRUE) 

closed_relevance_summary <- main_df %>%
  filter(comment_type == "task_description") %>%
  select(TaskPHID, resolution_outcome, priority_score, priority, source, phase, week_index, isAuthorWMF)

# TODO: need to get the TaskAuthor's comments, not just the big picture
machine_result <- main_df %>%
  mutate(olmo_label = str_extract_all(olmo_sentence_categories, "(?<=')[^']+(?=')")) |>
  unnest(olmo_label) |>
  group_by(TaskPHID) |>
  summarise(
    olmo_BE_prop = mean(olmo_label %in% c("OBSERVED BUG BEHAVIOR", "EXPECTED BEHAVIOR")),
    olmo_SOL_prop = mean(olmo_label %in% c("SOLUTION DISCUSSION", "SOLUTION USAGE")),
    median_gerrit_delta = median(gerrit_code_insertions + gerrit_code_deletions, na.rm = TRUE),
    median_gerrit_reviewers = median(gerrit_reviewer_count, na.rm = TRUE),
    n_comments = sum(comment_type == "task_subcomment")
  ) |>
  left_join(closed_relevance_summary, by = "TaskPHID") |>
  mutate(dsl_score = ifelse(resolution_outcome == "TRUE", 1, 0))

output_df <- machine_result |>
  left_join(human_result, by = "TaskPHID")

write.csv(machine_result, "100725_bivariate_data.csv", row.names = FALSE)


# [ ] add sampling probabilities