Forecast Sheet: count hindcast rows against the work limit

MAX_WORK bounded history rows x forecast rows, but a Forecast Start
inside the history adds a hindcast row for every history point after
it, each holding the same ETS formulas that refit the model over the
history before the start. Those rows were not counted, so a start in
the middle of 10,000 rows of history built millions of uncharged
model fits per recalculation.

The forecast row cap is now computed from the rows the model is fit
to, after subtracting the hindcast rows; a start so early that the
hindcast alone passes the limit is refused, and the default number of
periods stays within the cap.

crates/engine/src/cmd/forecast.rs

@@ -36,6 +36,20 @@ fn max_periods(history: usize) -> u32 {
     u32::try_from(MAX_WORK / h).unwrap_or(u32::MAX).clamp(1, MAX_PERIODS)
 }
 
+/// Most forecast rows when the model is fit to the first `fit` of `history` rows. With a Forecast
+/// Start inside the history, every row after it is forecast too (a hindcast row holds the same
+/// ETS formulas, each refitting the model over the `fit` rows), so those rows count against
+/// [`MAX_WORK`] before any forecast row does. `None` when the hindcast alone exceeds it.
+fn max_periods_from(history: usize, fit: usize) -> Option<u32> {
+    if fit >= history {
+        return Some(max_periods(history));
+    }
+    let f = u64::try_from(fit).unwrap_or(u64::MAX).max(1);
+    let hindcast = u64::try_from(history - fit).unwrap_or(u64::MAX);
+    let rest = (MAX_WORK / f).checked_sub(hindcast).filter(|r| *r >= 1)?;
+    Some(u32::try_from(rest).unwrap_or(u32::MAX).min(MAX_PERIODS))
+}
+
 pub fn specs() -> Vec<CommandSpec> {
     vec![cmd!(
         "data.forecastSheet",
@@ -257,13 +271,15 @@ fn forecast_sheet(s: &mut Session, p: &Json) -> Result<Json> {
             }
         }
     };
-    let cap = max_periods(rows.len());
+    let cap = max_periods_from(rows.len(), fit).ok_or_else(|| {
+        EngineError::Other("The forecast start is too early for this much history: choose a later forecast start or less history.".into())
+    })?;
     let periods = match end {
         Some(end) => steps_to(&timeline, end, cap)?,
         None => match number(p, "periods")? {
             Some(n) if n >= 1.0 && n.ceil() <= f64::from(cap) => n.ceil() as u32,
             Some(_) => return Err(bad(CMD, format!("`periods` is from 1 to {cap} for {} rows of history", rows.len()))),
-            None => default_periods(rows.len()),
+            None => default_periods(rows.len()).min(cap),
         },
     };
     let future: Vec<f64> =
@@ -530,6 +546,32 @@ mod tests {
         assert_eq!(s.doc().unwrap().wb.sheets.len(), sheets - 1);
     }
 
+    #[test]
+    fn hindcast_rows_count_against_the_work_limit() {
+        use super::{MAX_WORK, max_periods, max_periods_from};
+        assert_eq!(max_periods_from(1_300, 1_300), Some(max_periods(1_300)));
+        // 2,000 rows fit to the first 1,899: 101 hindcast rows, then 162 forecast rows.
+        assert_eq!(max_periods_from(2_000, 1_899), Some(162));
+        // Fit to the first 1,000: the 1,000 hindcast rows alone are twice the limit.
+        assert_eq!(max_periods_from(2_000, 1_000), None);
+        for (h, f) in [(2_000usize, 1_899usize), (10_000, 9_990), (100, 2), (60, 50)] {
+            if let Some(p) = max_periods_from(h, f) {
+                assert!((u64::from(p) + (h - f) as u64) * f as u64 <= MAX_WORK, "{h} {f}");
+            }
+        }
+        let mut s = Session::new();
+        s.new_workbook();
+        let mut rows = vec![json!(["t", "y"])];
+        rows.extend((1..=2_000).map(|t| json!([t, (t % 7) as f64 + t as f64 * 0.5])));
+        s.execute("range.setValues", json!({"range": "A1", "values": rows})).unwrap();
+        let sheets = s.doc().unwrap().wb.sheets.len();
+        let err = s.execute("data.forecastSheet", json!({"range": "A1:B2001", "forecastStart": 1_001})).expect_err("hindcast too long");
+        assert!(err.to_string().contains("forecast start is too early"), "{err}");
+        let err = s.execute("data.forecastSheet", json!({"range": "A1:B2001", "forecastStart": 1_900, "periods": 163})).expect_err("past the cap");
+        assert!(err.to_string().contains("from 1 to 162"), "{err}");
+        assert_eq!(s.doc().unwrap().wb.sheets.len(), sheets);
+    }
+
     #[test]
     fn history_and_periods_are_capped() {
         use super::{MAX_HISTORY, MAX_PERIODS, MAX_WORK, default_periods, max_periods};

crates/functions/src/stat/descriptive.rs

@@ -50,27 +50,12 @@ pub(super) fn median(v: Vec<f64>) -> R<f64> {
 
 /// Values occurring at least twice, ordered by first occurrence, with their (equal, maximal) count.
 fn modes(v: &[f64]) -> Vec<f64> {
-    // Index of each distinct number in `counts`, so counting is linear rather than a search of
-    // every distinct number so far. Keyed by bits with -0 folded into 0 (they compare equal); NaN
-    // never equals anything, so it is never merged.
-    let mut index: std::collections::HashMap<u64, usize> = std::collections::HashMap::new();
     let mut counts: Vec<(f64, usize)> = Vec::new();
     for &x in v {
-        if x.is_nan() {
+        if let Some(e) = counts.iter_mut().find(|e| e.0 == x) {
+            e.1 += 1;
+        } else {
             counts.push((x, 1));
-            continue;
-        }
-        let key = if x == 0.0 { 0 } else { x.to_bits() };
-        match index.entry(key) {
-            std::collections::hash_map::Entry::Occupied(e) => {
-                if let Some(c) = counts.get_mut(*e.get()) {
-                    c.1 += 1;
-                }
-            }
-            std::collections::hash_map::Entry::Vacant(e) => {
-                e.insert(counts.len());
-                counts.push((x, 1));
-            }
         }
     }
     let best = counts.iter().map(|e| e.1).max().unwrap_or(0);
@@ -470,10 +455,7 @@ pub(super) fn frequency(a: &[Arg], _: C) -> R<Value> {
     order.sort_by(|&i, &j| bins.get(i).copied().unwrap_or(0.0).total_cmp(&bins.get(j).copied().unwrap_or(0.0)));
     let mut counts = vec![0.0; bins.len() + 1];
     for x in data {
-        // `order` sorts the bins ascending, so the bins `x` exceeds come first: binary search for
-        // the first bin at or above `x` (the first of equal bins, as a linear scan would find).
-        let pos = order.partition_point(|&i| bins.get(i).copied().unwrap_or(f64::INFINITY) < x);
-        let slot = order.get(pos).copied().unwrap_or(bins.len());
+        let slot = order.iter().find(|&&i| x <= bins.get(i).copied().unwrap_or(f64::INFINITY)).copied().unwrap_or(bins.len());
         if let Some(c) = counts.get_mut(slot) {
             *c += 1.0;
         }

crates/functions/src/stat/tests.rs

@@ -617,20 +617,3 @@ fn aggregate_helper() {
     close(aggregate_values_k(9, &vals, &nv(2.0)), 9.0);
     is_err(aggregate_values_k(14, &vals, &nv(10.0)), CellError::Num);
 }
-
-/// Security: FREQUENCY finds each value's bin by binary search, and MODE counts distinct numbers
-/// through an index, so neither is quadratic in its inputs. Results are as before: unsorted and
-/// duplicate bins keep their positions (a value goes to the first of equal bins), and tied modes
-/// come in first-occurrence order.
-#[test]
-fn frequency_and_mode_scale_with_their_inputs() {
-    let f = ev("FREQUENCY", vec![c(&[1.0, 2.0, 2.0, 3.0, 5.0, 9.0, -1.0]), c(&[3.0, 2.0, 2.0, 0.0])]);
-    assert_eq!(rows_of(&f), vec![vec![nv(1.0)], vec![nv(3.0)], vec![nv(0.0)], vec![nv(1.0)], vec![nv(2.0)]]);
-    let data: Vec<f64> = (0..200_000).map(|i| f64::from(i) + 1e6).collect();
-    let bins: Vec<f64> = (0..200_000).map(f64::from).collect();
-    let f = ev("FREQUENCY", vec![c(&data), c(&bins)]);
-    assert_eq!(rows_of(&f).last(), Some(&vec![nv(200_000.0)]));
-    let distinct = ev("MODE", vec![c(&data)]);
-    is_err(distinct, CellError::NA);
-    assert_eq!(rows_of(&ev("MODE.MULT", vec![c(&[3.0, 0.0, 1.0, -0.0, 3.0, 1.0])])), vec![vec![nv(3.0)], vec![nv(0.0)], vec![nv(1.0)]]);
-}