Refactor R cross validation to match Py function structure

R/R/diagnostics.R

@@ -131,45 +131,61 @@ cross_validation <- function(
 
   predicts <- data.frame()
   for (i in 1:length(cutoffs)) {
-    # Copy the model
-    cutoff <- cutoffs[i]
-    m <- prophet_copy(model, cutoff)
-    # Train model
-    history.c <- dplyr::filter(df, ds <= cutoff)
-    if (nrow(history.c) < 2) {
-      stop('Less than two datapoints before cutoff. Increase initial window.')
-    }
-    fit.args <- c(list(m=m, df=history.c), model$fit.kwargs)
-    m <- do.call(fit.prophet, fit.args)
-    # Calculate yhat
-    df.predict <- dplyr::filter(df, ds > cutoff, ds <= cutoff + horizon.dt)
-    # Get the columns for the future dataframe
-    columns <- 'ds'
-    if (m$growth == 'logistic') {
-      columns <- c(columns, 'cap')
-      if (m$logistic.floor) {
-        columns <- c(columns, 'floor')
-      }
-    }
-    columns <- c(columns, names(m$extra_regressors))
-    for (name in names(m$seasonalities)) {
-      condition.name = m$seasonalities[[name]]$condition.name
-      if (!is.null(condition.name)) {
-        columns <- c(columns, condition.name)
-      }
-    }
-    future <- df.predict[columns]
-    yhat <- stats::predict(m, future)
-    # Merge yhat, y, and cutoff.
-    df.c <- dplyr::inner_join(df.predict, yhat[predict_columns], by = "ds")
-    df.c <- df.c[c(predict_columns, "y")]
-    df.c <- dplyr::select(df.c, y, predict_columns)
-    df.c$cutoff <- cutoff
+    df.c <- single_cutoff_forecast(df, model, cutoffs[i], horizon.dt, predict_columns)
     predicts <- rbind(predicts, df.c)
   }
   return(predicts)
 }
 
+#' Forecast for a single cutoff.
+
+#' Used in cross_validation function when evaluating for multiple cutoffs.
+#'
+#' @param df Dataframe with history for cutoff.
+#' @param model Prophet model object.
+#' @param cutoff Datetime of cutoff.
+#' @param horizon.dt timediff forecast horizon.
+#' @param predict_columns Array of names of columns to be returned in output.
+#'
+#' @return Dataframe with forecast, actual value, and cutoff.
+#'
+#' @keywords internal
+single_cutoff_forecast <- function(df, model, cutoff, horizon.dt, predict_columns){
+  m <- prophet_copy(model, cutoff)
+  # Train model
+  history.c <- dplyr::filter(df, ds <= cutoff)
+  if (nrow(history.c) < 2) {
+    stop('Less than two datapoints before cutoff. Increase initial window.')
+  }
+  fit.args <- c(list(m=m, df=history.c), model$fit.kwargs)
+  m <- do.call(fit.prophet, fit.args)
+  # Calculate yhat
+  df.predict <- dplyr::filter(df, ds > cutoff, ds <= cutoff + horizon.dt)
+  # Get the columns for the future dataframe
+  columns <- 'ds'
+  if (m$growth == 'logistic') {
+    columns <- c(columns, 'cap')
+    if (m$logistic.floor) {
+      columns <- c(columns, 'floor')
+    }
+  }
+  columns <- c(columns, names(m$extra_regressors))
+  for (name in names(m$seasonalities)) {
+    condition.name = m$seasonalities[[name]]$condition.name
+    if (!is.null(condition.name)) {
+      columns <- c(columns, condition.name)
+    }
+  }
+  future <- df.predict[columns]
+  yhat <- stats::predict(m, future)
+  # Merge yhat, y, and cutoff.
+  df.c <- dplyr::inner_join(df.predict, yhat[predict_columns], by = "ds")
+  df.c <- df.c[c(predict_columns, "y")]
+  df.c <- dplyr::select(df.c, y, predict_columns)
+  df.c$cutoff <- cutoff
+  return(df.c)
+}
+
 #' Copy Prophet object.
 #'
 #' @param m Prophet model object.