[Backfill corrections] Incorporate changes from 1806 and 1808

ansible/templates/backfillcorr-params-prod.json.j2

@@ -4,12 +4,14 @@
   "cache_dir": "./cache",
   "testing_window": 1,
   "training_days": 270,
-  "lag_pad":2,
+  "lag_pad": 2,
   "export_dir": "./receiving",
   "geo_levels": ["state", "county"],
   "value_types": ["count", "fraction"],
   "num_col": "num",
   "denom_col": "den",
+  "parallel": true,
+  "parallel_max_cores": 2,
   "post": {
     "aws_credentials": {
       "aws_access_key_id": "{{ backfillcorr_aws_access_key_id }}",

backfill_corrections/delphiBackfillCorrection/DESCRIPTION

@@ -27,7 +27,8 @@ Imports:
     parallel,
     purrr,
     vctrs,
-    RcppRoll
+    RcppRoll,
+    bettermc
 Suggests:
     knitr (>= 1.15),
     rmarkdown (>= 1.4),

backfill_corrections/delphiBackfillCorrection/NAMESPACE

@@ -23,6 +23,7 @@ export(run_backfill)
 import(covidcast)
 importFrom(RcppRoll,roll_mean)
 importFrom(arrow,read_parquet)
+importFrom(bettermc,mclapply)
 importFrom(dplyr,"%>%")
 importFrom(dplyr,across)
 importFrom(dplyr,arrange)
@@ -31,9 +32,11 @@ importFrom(dplyr,bind_rows)
 importFrom(dplyr,desc)
 importFrom(dplyr,everything)
 importFrom(dplyr,filter)
+importFrom(dplyr,full_join)
 importFrom(dplyr,group_by)
 importFrom(dplyr,group_split)
 importFrom(dplyr,if_else)
+importFrom(dplyr,left_join)
 importFrom(dplyr,pull)
 importFrom(dplyr,select)
 importFrom(dplyr,starts_with)
@@ -47,7 +50,10 @@ importFrom(lubridate,make_date)
 importFrom(lubridate,month)
 importFrom(lubridate,year)
 importFrom(parallel,detectCores)
+importFrom(purrr,map)
 importFrom(purrr,map_dfc)
+importFrom(purrr,map_dfr)
+importFrom(purrr,reduce)
 importFrom(quantgen,quantile_lasso)
 importFrom(readr,write_csv)
 importFrom(stats,coef)

backfill_corrections/delphiBackfillCorrection/R/main.R

@@ -11,6 +11,8 @@
 #' 
 #' @importFrom dplyr %>% filter group_by summarize across everything group_split ungroup
 #' @importFrom tidyr drop_na
+#' @importFrom purrr map map_dfr
+#' @importFrom bettermc mclapply
 #' 
 #' @export
 run_backfill <- function(df, params,
@@ -61,7 +63,12 @@ run_backfill <- function(df, params,
     group_dfs <- group_split(df, geo_value)
 
     # Build model for each location
-    for (subdf in group_dfs) {
+    apply_fn <- ifelse(params$parallel, mclapply, lapply)
+    result <- apply_fn(group_dfs, function(subdf) {
+      # Make a copy with the same structure.
+      state_test_data_list <- test_data_list
+      state_coef_list <- coef_list
+
       geo <- subdf$geo_value[1]
 
       msg_ts("Processing ", geo, " geo group")
@@ -163,7 +170,7 @@ run_backfill <- function(df, params,
             test_data <- filtered_data[[2]]
 
             if (nrow(train_data) == 0 || nrow(test_data) == 0) {
-              msg_ts("Not enough data to either train or test for test_lag ",
+              msg_ts("Not enough data to either train or test for test lag ",
                 test_lag, ", skipping")
               next
             }
@@ -180,7 +187,6 @@ run_backfill <- function(df, params,
             params_list <- c(YITL, as.vector(unlist(covariates)))
 
             # Model training and testing
-            msg_ts("Training or loading models")
             prediction_results <- model_training_and_testing(
               train_data, test_data, taus = params$taus, covariates = params_list,
               lp_solver = params$lp_solver,
@@ -197,28 +203,32 @@ run_backfill <- function(df, params,
             # Model objects are saved during training, so only need to export
             # output if making predictions/corrections
             if (params$make_predictions) {
-              msg_ts("Generating predictions")
               test_data <- prediction_results[[1]]
               coefs <- prediction_results[[2]]
               test_data <- evaluate(test_data, params$taus) %>%
                 exponentiate_preds(params$taus)
 
               key <- make_key(value_type, signal_suffix)
-              idx <- length(test_data_list[[key]]) + 1
-              test_data_list[[key]][[idx]] <- test_data
-              coef_list[[key]][[idx]] <- coefs
+              idx <- length(state_test_data_list[[key]]) + 1
+              state_test_data_list[[key]][[idx]] <- test_data
+              state_coef_list[[key]][[idx]] <- coefs
             }
           }# End for test lags
         }# End for value types
       }# End for signal suffixes
-    }# End for geo list
+
+      return(list(coefs = state_coef_list, test_data = state_test_data_list))
+    }) # End for geo list
+
+    test_data_list <- map(result, ~.x$test_data)
+    coef_list <- map(result, ~.x$coefs)
 
     if (params$make_predictions) {
       for (value_type in params$value_types) {
         for (signal_suffix in signal_suffixes) {
           key <- make_key(value_type, signal_suffix)
-          test_combined <- bind_rows(test_data_list[[key]]) 
-          coef_combined <- bind_rows(coef_list[[key]]) 
+          test_combined <- map_dfr(test_data_list, ~.x[[key]])
+          coef_combined <- map_dfr(coef_list, ~.x[[key]])
           export_test_result(test_combined, coef_combined, 
                              indicator=indicator, signal=signal,
                              signal_suffix=signal_suffix,

backfill_corrections/delphiBackfillCorrection/R/preprocessing.R

@@ -187,6 +187,8 @@ add_weekofmonth <- function(df, time_col, wm = WEEK_ISSUES) {
 #' @template lag_col-template
 #' @template ref_lag-template
 #' 
+#' @importFrom dplyr full_join left_join
+#' @importFrom purrr reduce
 #' @importFrom tidyr pivot_wider drop_na
 #' 
 #' @export
@@ -203,16 +205,21 @@ add_7davs_and_target <- function(df, value_col, refd_col, lag_col, ref_lag) {
   names(avg_df)[names(avg_df) == value_col] <- 'value_7dav'
   avg_df_prev7 <- add_shift(avg_df, 7, refd_col)
   names(avg_df_prev7)[names(avg_df_prev7) == 'value_7dav'] <- 'value_prev_7dav'
-
-  backfill_df <- Reduce(function(x, y) merge(x, y, all=TRUE), 
-                        list(df, avg_df, avg_df_prev7))
+
+  backfill_df <- reduce(
+    list(df, avg_df, avg_df_prev7),
+    full_join, by=c(refd_col, "issue_date")
+  )
 
   # Add target
   target_df <- df[df$lag==ref_lag, c(refd_col, value_col, "issue_date")]
   names(target_df)[names(target_df) == value_col] <- 'value_target'
   names(target_df)[names(target_df) == 'issue_date'] <- 'target_date'
 
-  backfill_df <- merge(backfill_df, target_df, by=refd_col, all.x=TRUE)
+  backfill_df <- left_join(backfill_df, target_df, by=c(refd_col))
+
+  # Remove invalid rows
+  backfill_df <- drop_na(backfill_df, c(lag_col))
 
   # Add log values
   backfill_df$log_value_raw = log(backfill_df$value_raw + 1)
@@ -221,9 +228,6 @@ add_7davs_and_target <- function(df, value_col, refd_col, lag_col, ref_lag) {
   backfill_df$log_value_prev_7dav = log(backfill_df$value_prev_7dav + 1)
   backfill_df$log_7dav_slope = backfill_df$log_value_7dav - backfill_df$log_value_prev_7dav
 
-  # Remove invalid rows
-  backfill_df <- drop_na(backfill_df, c(lag_col))
-
   return (as.data.frame(backfill_df))
 }
 

backfill_corrections/params.json.template

@@ -9,5 +9,7 @@
   "geo_levels": ["state", "county"],
   "value_types": ["count", "fraction"],
   "num_col": "num",
-  "denom_col": "den"
+  "denom_col": "den",
+  "parallel": false,
+  "parallel_max_cores": 2
 }