---
title: "Monotonicity and Precision-Recall Paths"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Monotonicity and Precision-Recall Paths}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
```

The extraction helpers make path diagnostics independent of any plotting
backend. A selection surface can be checked directly for monotonicity over
`c0` or `q`.

```{r}
library(SelectBoost.FDA)

surface <- data.frame(
  feature = c("a", "a", "b", "b"),
  predictor = "signal",
  level = "feature",
  c0 = c(0.2, 0.6, 0.2, 0.6),
  selection = c(0.8, 0.9, 0.4, 0.2),
  stringsAsFactors = FALSE
)

paths <- as_monotonicity_path_data(surface, axis = "c0", level = "feature")
diagnostic <- check_selection_monotonicity(
  surface,
  axis = "c0",
  direction = "nonincreasing",
  level = "feature"
)

paths
diagnostic
summarise_monotonicity(diagnostic)
```

Monotone post-processing returns data, not a modified fit object:

```{r}
enforce_monotone_selection(
  surface,
  axis = "c0",
  direction = "nonincreasing",
  method = "cummin",
  level = "feature"
)
```

Precision-recall paths use the same surface data and a mapped truth object.

```{r}
truth <- list(
  active_features = "a",
  feature_universe = c("a", "b")
)

pr <- precision_recall_curve_fda(
  surface,
  truth = truth,
  level = "feature",
  threshold_grid = c(0, 0.5, 0.75)
)

pr
best_threshold_fda(pr, metric = "f1")
summarise_precision_recall_fda(pr)
```