Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

fix: broken epix_merge operations #174

Merged
merged 3 commits into from
Mar 3, 2025
Merged

fix: broken epix_merge operations #174

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
e2b5b51
fix: broken epix_merge operations
dshemetov Mar 1, 2025
6d8b49a
fix: google symptoms, sum after whitening
dshemetov Mar 3, 2025
3965b84
fix: notebook coverage 80 -> 90
dshemetov Mar 3, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
5 changes: 4 additions & 1 deletion R/aux_data_utils.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -274,7 +274,10 @@ daily_to_weekly_archive <- function(epi_arch,
as_tibble()
}
) %>%
as_epi_archive(compactify = TRUE)
# Always convert to data.frame after dplyr operations on data.table.
# https://github.com/cmu-delphi/epiprocess/issues/618
as.data.frame() %>%
as_epi_archive(compactify = TRUE)
}


Expand Down
136 changes: 79 additions & 57 deletions scripts/covid_hosp_explore.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,8 +6,9 @@ source("scripts/targets-exploration-common.R")
hhs_signal <- "confirmed_admissions_covid_1d"
if (!exists("ref_time_values_")) {
# Alternatively you can let slide_forecaster figure out ref_time_values
start_date <- as.Date("2023-10-04")
start_date <- as.Date("2023-11-08")
end_date <- as.Date("2024-04-24")
# end_date <- start_date + 7
date_step <- 7L
ref_time_values_ <- seq.Date(start_date, end_date, by = date_step)
}
Expand Down Expand Up @@ -62,12 +63,7 @@ forecaster_parameter_combinations_ <- rlang::list2(
),
pop_scaling = FALSE,
scale_method = "quantile",
center_method = "median",
nonlin_method = "quart_root",
filter_source = "",
filter_agg_level = "",
n_training = Inf,
drop_non_seasons = FALSE,
n_training = Inf
),
expand_grid(
forecaster = "scaled_pop",
Expand All @@ -93,12 +89,7 @@ forecaster_parameter_combinations_ <- rlang::list2(
),
pop_scaling = FALSE,
scale_method = "quantile",
center_method = "median",
nonlin_method = "quart_root",
filter_source = "",
filter_agg_level = "",
n_training = Inf,
drop_non_seasons = FALSE,
n_training = Inf
),
expand_grid(
forecaster = "scaled_pop",
Expand All @@ -124,12 +115,7 @@ forecaster_parameter_combinations_ <- rlang::list2(
),
pop_scaling = FALSE,
scale_method = "quantile",
center_method = "median",
nonlin_method = "quart_root",
filter_source = "",
filter_agg_level = "",
n_training = Inf,
drop_non_seasons = FALSE,
n_training = Inf
)
),
scled_pop_season = tidyr::expand_grid(
Expand All @@ -141,7 +127,13 @@ forecaster_parameter_combinations_ <- rlang::list2(
),
pop_scaling = FALSE,
n_training = Inf,
seasonal_method = list(c("covid"), c("window"), c("covid", "window"), c("climatological"), c("climatological", "window"))
seasonal_method = list(
c("covid"),
c("window"),
c("covid", "window"),
c("climatological"),
c("climatological", "window")
)
)
) %>%
map(function(x) {
Expand Down Expand Up @@ -178,16 +170,16 @@ scaled_pop_scaled <- list(
smooth_scaled <- list(
forecaster = "smoothed_scaled",
trainer = "quantreg",
lags =
# list(smoothed, sd)
list(c(0, 7, 14, 21, 28), c(0)),
# lags = list(smoothed, sd)
lags = list(c(0, 7, 14, 21, 28), c(0)),
smooth_width = as.difftime(2, units = "weeks"),
sd_width = as.difftime(4, units = "weeks"),
sd_mean_width = as.difftime(2, units = "weeks"),
pop_scaling = TRUE,
n_training = Inf
)
# Human-readable object to be used for inspecting the ensembles in the pipeline.
# fmt: skip
ensemble_parameter_combinations_ <- tribble(
~ensemble, ~ensemble_args, ~forecasters,
# mean forecaster
Expand Down Expand Up @@ -240,7 +232,12 @@ ensemble_parameter_combinations_ <- tribble(
) %>%
add_id(exclude = "forecasters")
# spoofing ensembles for right now
ensemble_parameter_combinations_ <- tibble::tibble(id = character(), ensemble = character(), ensemble_args = character(), children_ids = character())
ensemble_parameter_combinations_ <- tibble::tibble(
id = character(),
ensemble = character(),
ensemble_args = character(),
children_ids = character()
)
# Check that every ensemble dependent is actually included.
missing_forecasters <- setdiff(
ensemble_parameter_combinations_ %>% pull(children_ids) %>% unlist() %>% unique(),
Expand Down Expand Up @@ -272,7 +269,7 @@ rlang::list2(
tar_target(
name = hhs_archive_data_asof,
command = {
get_health_data(as.Date(ref_time_values)) %>%
get_health_data(as.Date(ref_time_values), disease = "covid") %>%
mutate(version = as.Date(ref_time_values)) %>%
relocate(geo_value, time_value, version, hhs)
},
Expand Down Expand Up @@ -348,6 +345,9 @@ rlang::list2(
# weekly data is indexed from the start of the week
mutate(time_value = time_value + 6 - time_value_adjust) %>%
mutate(version = time_value) %>%
# Always convert to data.frame after dplyr operations on data.table.
# https://github.com/cmu-delphi/epiprocess/issues/618
as.data.frame() %>%
as_epi_archive(compactify = TRUE)
nssp_archive
}
Expand Down Expand Up @@ -380,39 +380,52 @@ rlang::list2(
geo_type = "hhs",
geo_values = "*"
)
google_symptoms_archive_min <-
google_symptoms_state_archive %>%
google_symptoms_archive_min <- google_symptoms_state_archive %>%
bind_rows(google_symptoms_hhs_archive) %>%
select(geo_value, time_value, value) %>%
daily_to_weekly() %>%
mutate(version = time_value) %>%
as_epi_archive(compactify = TRUE)
google_symptoms_archive_min$DT %>%
filter(!is.na(value)) %>%
relocate(geo_value, time_value, version, value) %>%
as.data.frame() %>%
as_epi_archive(compactify = TRUE)
})
all_of_them[[1]]$DT %<>% rename(google_symptoms_4_bronchitis = value)
all_of_them[[2]]$DT %<>% rename(google_symptoms_5_ageusia = value)
all_of_them[[1]] <- all_of_them[[1]]$DT %>%
rename(google_symptoms_4_bronchitis = value) %>%
# Always convert to data.frame after dplyr operations on data.table.
# https://github.com/cmu-delphi/epiprocess/issues/618
as.data.frame() %>%
as_epi_archive(compactify = TRUE)
all_of_them[[2]] <- all_of_them[[2]]$DT %>%
rename(google_symptoms_5_ageusia = value) %>%
# Always convert to data.frame after dplyr operations on data.table.
# https://github.com/cmu-delphi/epiprocess/issues/618
as.data.frame() %>%
as_epi_archive(compactify = TRUE)
google_symptoms_archive <- epix_merge(all_of_them[[1]], all_of_them[[2]])
google_symptoms_archive <- google_symptoms_archive$DT %>%
mutate(google_symptoms = google_symptoms_4_bronchitis + google_symptoms_5_ageusia) %>%
# Always convert to data.frame after dplyr operations on data.table.
# https://github.com/cmu-delphi/epiprocess/issues/618
as.data.frame() %>%
as_epi_archive(compactify = TRUE)
# not just using dplyr to allow for na.rm
google_symptoms_archive$DT$google_symptoms <-
rowSums(google_symptoms_archive$DT[, c("google_symptoms_4_bronchitis", "google_symptoms_5_ageusia")],
na.rm = TRUE
)
pre_pipeline <- google_symptoms_archive %>%
epix_as_of(as.Date("2023-10-04")) %>%
mutate(source = "none")
colnames <- c("google_symptoms_4_bronchitis", "google_symptoms_5_ageusia", "google_symptoms")
colnames <- c("google_symptoms_4_bronchitis", "google_symptoms_5_ageusia")
for (colname in colnames) {
learned_params <- calculate_whitening_params(pre_pipeline, colname = colname)
google_symptoms_archive$DT %<>% data_whitening(colname = colname, learned_params, join_cols = "geo_value")
}
google_symptoms_archive$DT %>%
mutate(
google_symptoms = ifelse(is.na(google_symptoms_4_bronchitis), 0, google_symptoms_4_bronchitis) +
ifelse(is.na(google_symptoms_5_ageusia), 0, google_symptoms_5_ageusia)
) %>%
select(-starts_with("source")) %>%
# Always convert to data.frame after dplyr operations on data.table
# https://github.com/cmu-delphi/epiprocess/issues/618
as.data.frame() %>%
as_epi_archive(compactify = TRUE)
}
),
Expand Down Expand Up @@ -479,8 +492,14 @@ rlang::list2(
nwss <- readr::read_csv(most_recent) %>%
rename(value = state_med_conc) %>%
arrange(geo_value, time_value)
state_code <- readr::read_csv(here::here("aux_data", "flusion_data", "state_codes_table.csv"), show_col_types = FALSE)
hhs_codes <- readr::read_csv(here::here("aux_data", "flusion_data", "state_code_hhs_table.csv"), show_col_types = FALSE)
state_code <- readr::read_csv(
here::here("aux_data", "flusion_data", "state_codes_table.csv"),
show_col_types = FALSE
)
hhs_codes <- readr::read_csv(
here::here("aux_data", "flusion_data", "state_code_hhs_table.csv"),
show_col_types = FALSE
)
state_to_hhs <- hhs_codes %>%
left_join(state_code, by = "state_code") %>%
select(hhs_region = hhs, geo_value = state_id)
Expand All @@ -489,8 +508,7 @@ rlang::list2(
drop_na() %>%
select(-agg_level, -year, -agg_level, -population, -density)
pop_data <- gen_pop_and_density_data()
nwss_hhs_region <-
nwss %>%
nwss_hhs_region <- nwss %>%
left_join(state_to_hhs, by = "geo_value") %>%
mutate(year = year(time_value)) %>%
left_join(pop_data, by = join_by(geo_value, year)) %>%
Expand All @@ -517,8 +535,12 @@ rlang::list2(
tar_target(
name = hhs_region,
command = {
hhs_region <- readr::read_csv("https://raw.githubusercontent.com/cmu-delphi/covidcast-indicators/refs/heads/main/_delphi_utils_python/delphi_utils/data/2020/state_code_hhs_table.csv")
state_id <- readr::read_csv("https://raw.githubusercontent.com/cmu-delphi/covidcast-indicators/refs/heads/main/_delphi_utils_python/delphi_utils/data/2020/state_codes_table.csv")
hhs_region <- readr::read_csv(
"https://raw.githubusercontent.com/cmu-delphi/covidcast-indicators/refs/heads/main/_delphi_utils_python/delphi_utils/data/2020/state_code_hhs_table.csv"
)
state_id <- readr::read_csv(
"https://raw.githubusercontent.com/cmu-delphi/covidcast-indicators/refs/heads/main/_delphi_utils_python/delphi_utils/data/2020/state_codes_table.csv"
)
hhs_region %>%
left_join(state_id, by = "state_code") %>%
select(hhs_region = hhs, geo_value = state_id) %>%
Expand All @@ -534,22 +556,22 @@ rlang::list2(
rename("hhs" := value) %>%
add_hhs_region_sum(hhs_region) %>%
filter(geo_value != "us") %>%
as_epi_archive(
compactify = TRUE
)
# Always convert to data.frame after dplyr operations on data.table
# https://github.com/cmu-delphi/epiprocess/issues/618
as.data.frame() %>%
as_epi_archive(compactify = TRUE)
joined_archive_data$geo_type <- "custom"
# drop aggregated geo_values
joined_archive_data <- joined_archive_data %>%
epix_merge(nwss_coarse, sync = "locf")
joined_archive_data$geo_type <- "custom"
# TODO: Maybe bring these back
# epix_merge(doctor_visits_weekly_archive, sync = "locf") %>%
joined_archive_data %<>%
epix_merge(nssp_archive, sync = "locf")
joined_archive_data <- joined_archive_data %>% epix_merge(nwss_coarse, sync = "locf")
joined_archive_data %<>% epix_merge(nssp_archive, sync = "locf")
joined_archive_data$geo_type <- "custom"
joined_archive_data %<>%
epix_merge(google_symptoms_archive, sync = "locf")
joined_archive_data$DT %<>% filter(grepl("[a-z]{2}", geo_value), !(geo_value %in% c("as", "pr", "vi", "gu", "mp")))
joined_archive_data %<>% epix_merge(google_symptoms_archive, sync = "locf")
joined_archive_data <- joined_archive_data$DT %>%
filter(grepl("[a-z]{2}", geo_value), !(geo_value %in% c("as", "pr", "vi", "gu", "mp"))) %>%
# Always convert to data.frame after dplyr operations on data.table
# https://github.com/cmu-delphi/epiprocess/issues/618
as.data.frame() %>%
as_epi_archive(compactify = TRUE)
joined_archive_data$geo_type <- "state"
slide_forecaster(
epi_archive = joined_archive_data,
Expand Down Expand Up @@ -591,7 +613,7 @@ rlang::list2(
rename(model = forecaster) %>%
rename(prediction = value) %>%
filter(!is.na(geo_value))
evaluate_predictions(predictions_cards = filtered_forecasts, truth_data = actual_eval_data) %>%
evaluate_predictions(forecasts = filtered_forecasts, truth_data = actual_eval_data) %>%
rename(forecaster = model)
}
),
Expand Down
Loading
Loading