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feat: allocator's new scenario for ROAS target #648

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merged 22 commits into from
Mar 22, 2023
Merged

feat: allocator's new scenario for ROAS target #648

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3eac6af
feat: added scenario hit target roas
gufengzhou Mar 10, 2023
beb15bc
feat: adapted onepager for target roas
gufengzhou Mar 10, 2023
550c66d
feat: no need to define select_model when there's only one
laresbernardo Mar 10, 2023
4b8d6a3
docs update + simpler scenarios: max_response and target_roas
laresbernardo Mar 10, 2023
7b0df9b
feat: target cpa added into scenario
gufengzhou Mar 13, 2023
ffa7415
fix: update scenario for backwards compatibility
gufengzhou Mar 13, 2023
fd63dcb
docs: updates demo and params - 3.10.1 [dev]
laresbernardo Mar 13, 2023
9ce0321
fix: error when both CPA scenarios are same
gufengzhou Mar 14, 2023
00d781a
doc: update demo.R
gufengzhou Mar 14, 2023
d3a570b
fix: channel constraint NULL error
gufengzhou Mar 15, 2023
34d8b7d
recode: finetune plot labels
gufengzhou Mar 16, 2023
9a600c2
fix: pass export parameter correctly on allocator
laresbernardo Mar 17, 2023
49113a4
recode: finetune onepager & default target_value_ext
gufengzhou Mar 20, 2023
90f24cb
fix: NaN values set as 0
laresbernardo Mar 21, 2023
b748204
site: yarn upgrade
laresbernardo Mar 21, 2023
8288d26
docs: get rid of metric_ds => date_range
laresbernardo Mar 21, 2023
67cf946
recode: styler applied
laresbernardo Mar 21, 2023
8b76ee8
docs: export str_to_title and case_when
laresbernardo Mar 22, 2023
60780cc
recode: update demo.R
gufengzhou Mar 22, 2023
fd8d7ad
doc: remove old and redundant guide
gufengzhou Mar 22, 2023
b9eb87e
docs: fixed % escape + decomp_plot()
laresbernardo Mar 22, 2023
c8d87c1
Merge branch 'hit_roas_target' of https://github.com/facebookexperime…
laresbernardo Mar 22, 2023
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6 changes: 3 additions & 3 deletions R/R/allocator.R
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Original file line number Diff line number Diff line change
Expand Up @@ -600,13 +600,13 @@ robyn_allocator <- function(robyn_object = NULL,
} else if (scenario == "target_efficiency") {
if (dep_var_type == "revenue") {
levs1 <- c(
"Initial", paste0("Hit ROAS x", round(target_value, 2)),
paste0("Hit ROAS x", target_value_ext)
"Initial", paste0("Hit ROAS $", round(target_value, 2)),
paste0("Hit ROAS $", target_value_ext)
)
} else {
levs1 <- c(
"Initial", paste0("Hit CPA $", round(target_value, 2)),
paste0("Hit CPA $", target_value_ext)
paste0("Hit CPA $", round(target_value_ext, 2))
)
}
}
Expand Down
23 changes: 16 additions & 7 deletions R/R/plots.R
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Expand Up @@ -730,7 +730,7 @@ allocation_plots <- function(InputCollect, OutputCollect, dt_optimOut, select_mo
pivot_longer(cols = !"type") %>%
left_join(resp_metric, "type") %>%
mutate(
name = factor(.data$name, levels = c("spend", "response")),
name = factor(paste("total", .data$name), levels = c("total spend", "total response")),
name_label = factor(
paste(.data$type, .data$name, sep = "\n"),
levels = paste(.data$type, .data$name, sep = "\n")
Expand All @@ -750,7 +750,7 @@ allocation_plots <- function(InputCollect, OutputCollect, dt_optimOut, select_mo
df_roi$labs <- factor(rep(labs, each = 2), levels = labs)

outputs[["p1"]] <- p1 <- df_roi %>%
ggplot(aes(x = .data$name_label, y = .data$value, fill = .data$type)) +
ggplot(aes(x = .data$name, y = .data$value, fill = .data$type)) +
facet_grid(. ~ .data$labs, scales = "free") +
scale_fill_manual(values = c("grey", "steelblue", "darkgoldenrod4")) +
geom_bar(stat = "identity", width = 0.6, alpha = 0.7) +
Expand Down Expand Up @@ -862,24 +862,33 @@ allocation_plots <- function(InputCollect, OutputCollect, dt_optimOut, select_mo
values = round(.data$values, 4),
# Deal with extreme cases divided by almost 0
values = ifelse((.data$values > 1e15 & .data$metric %in% c("ROAS", "mROAS")), 0, .data$values),
values_label = dplyr::case_when(
values_label = suppressWarnings(dplyr::case_when(
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.data$metric %in% c("ROAS", "mROAS") ~ paste0("x", round(.data$values, 2)),
.data$metric %in% c("CPA", "mCPA") ~ formatNum(.data$values, 2, abbr = TRUE, pre = "$"),
TRUE ~ paste0(round(100 * .data$values, 1), "%")
),
)),
# Better fill scale colours
values_label = ifelse(grepl("NA|NaN", .data$values_label), "-", .data$values_label),
values = ifelse((is.nan(.data$values) | is.na(.data$values)), 0, .data$values)
values = ifelse((is.nan(.data$values) | is.na(.data$values)), 0, .data$values),
) %>%
mutate(
channel = factor(.data$channel, levels = rev(unique(.data$channel))),
metric = factor(
dplyr::case_when(
.data$metric %in% c("spend", "response") ~ paste0(.data$metric, "%"),
TRUE ~ .data$metric
),
levels = paste0(unique(.data$metric), c("%", "%", "", ""))
)
) %>%
mutate(channel = factor(.data$channel, levels = rev(unique(.data$channel)))) %>%
group_by(.data$name_label) %>%
mutate(
values_norm = lares::normalize(.data$values),
values_norm = ifelse(is.nan(.data$values_norm), 0, .data$values_norm)
)

outputs[["p2"]] <- p2 <- df_plot_share %>%
ggplot(aes(x = .data$name_label, y = .data$channel, fill = .data$type)) +
ggplot(aes(x = .data$metric, y = .data$channel, fill = .data$type)) +
geom_tile(aes(alpha = .data$values_norm), color = "white") +
scale_fill_manual(values = c("grey50", "steelblue", "darkgoldenrod4")) +
scale_alpha_continuous(range = c(0.6, 1)) +
Expand Down