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fix: broken epix_merge operations #174

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Merged
dshemetov merged 3 commits into from
Mar 3, 2025
Merged

fix: broken epix_merge operations #174

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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
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5 changes: 4 additions & 1 deletion R/aux_data_utils.R
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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
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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
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