When learning steps are missing, start from the SM-2 state

Closes https://github.com/open-spaced-repetition/fsrs-rs/issues/87

rslib/src/scheduler/answering/mod.rs

@@ -361,7 +361,7 @@ impl Collection {
                 // and will need its initial memory state to be calculated based on review
                 // history.
                 let revlog = self.revlog_for_srs(SearchNode::CardIds(card.id.to_string()))?;
-                let item = single_card_revlog_to_item(revlog, timing.next_day_at);
+                let item = single_card_revlog_to_item(&fsrs, revlog, timing.next_day_at);
                 card.set_memory_state(&fsrs, item);
             }
             let days_elapsed = self

rslib/src/scheduler/fsrs/memory_state.rs

@@ -5,12 +5,14 @@ use std::collections::HashMap;
 
 use anki_proto::scheduler::ComputeMemoryStateResponse;
 use fsrs::FSRSItem;
+use fsrs::MemoryState;
 use fsrs::FSRS;
 use itertools::Itertools;
 
 use crate::card::CardType;
 use crate::prelude::*;
 use crate::revlog::RevlogEntry;
+use crate::revlog::RevlogReviewKind;
 use crate::scheduler::fsrs::weights::single_card_revlog_to_items;
 use crate::scheduler::fsrs::weights::Weights;
 use crate::scheduler::states::fuzz::with_review_fuzz;
@@ -55,9 +57,9 @@ impl Collection {
             } else {
                 None
             };
-            let items = fsrs_items_for_memory_state(revlog, timing.next_day_at);
-            let desired_retention = req.as_ref().map(|w| w.desired_retention);
             let fsrs = FSRS::new(req.as_ref().map(|w| &w.weights[..]))?;
+            let items = fsrs_items_for_memory_state(&fsrs, revlog, timing.next_day_at);
+            let desired_retention = req.as_ref().map(|w| w.desired_retention);
             let mut progress = self.new_progress_handler::<ComputeMemoryProgress>();
             progress.update(false, |s| s.total_cards = items.len() as u32)?;
             for (idx, (card_id, item)) in items.into_iter().enumerate() {
@@ -65,7 +67,7 @@ impl Collection {
                 let mut card = self.storage.get_card(card_id)?.or_not_found(card_id)?;
                 let original = card.clone();
                 if let Some(req) = &req {
-                    card.set_memory_state(&fsrs, item.clone());
+                    card.set_memory_state(&fsrs, item);
                     card.desired_retention = desired_retention;
                     // if rescheduling
                     if let Some(reviews) = &last_reviews {
@@ -128,7 +130,7 @@ impl Collection {
         let desired_retention = config.inner.desired_retention;
         let fsrs = FSRS::new(Some(&config.inner.fsrs_weights))?;
         let revlog = self.revlog_for_srs(SearchNode::CardIds(card.id.to_string()))?;
-        let item = single_card_revlog_to_item(revlog, self.timing_today()?.next_day_at);
+        let item = single_card_revlog_to_item(&fsrs, revlog, self.timing_today()?.next_day_at);
         card.set_memory_state(&fsrs, item);
         Ok(ComputeMemoryStateResponse {
             state: card.memory_state.map(Into::into),
@@ -138,13 +140,18 @@ impl Collection {
 }
 
 impl Card {
-    pub(crate) fn set_memory_state(&mut self, fsrs: &FSRS, item: Option<FSRSItem>) {
+    pub(crate) fn set_memory_state(
+        &mut self,
+        fsrs: &FSRS,
+        item: Option<FsrsItemWithStartingState>,
+    ) {
         self.memory_state = item
-            .map(|i| fsrs.memory_state(i, None))
+            .map(|i| fsrs.memory_state(i.item, i.starting_state))
             .or_else(|| {
                 if self.ctype == CardType::New {
                     None
                 } else {
+                    // no valid revlog entries; infer state from current card state
                     Some(fsrs.memory_state_from_sm2(self.ease_factor(), self.interval as f32))
                 }
             })
@@ -152,12 +159,21 @@ impl Card {
     }
 }
 
+#[derive(Debug)]
+pub(crate) struct FsrsItemWithStartingState {
+    pub item: FSRSItem,
+    /// When revlogs have been truncated, this stores the initial state at first
+    /// review
+    pub starting_state: Option<MemoryState>,
+}
+
 /// When updating memory state, FSRS only requires the last FSRSItem that
 /// contains the full history.
 pub(crate) fn fsrs_items_for_memory_state(
+    fsrs: &FSRS,
     revlogs: Vec<RevlogEntry>,
     next_day_at: TimestampSecs,
-) -> Vec<(CardId, Option<FSRSItem>)> {
+) -> Vec<(CardId, Option<FsrsItemWithStartingState>)> {
     revlogs
         .into_iter()
         .group_by(|r| r.cid)
@@ -165,7 +181,7 @@ pub(crate) fn fsrs_items_for_memory_state(
         .map(|(card_id, group)| {
             (
                 card_id,
-                single_card_revlog_to_item(group.collect(), next_day_at),
+                single_card_revlog_to_item(fsrs, group.collect(), next_day_at),
             )
         })
         .collect()
@@ -190,42 +206,118 @@ fn get_last_reviews(revlogs: &[RevlogEntry]) -> HashMap<CardId, TimestampSecs> {
     out
 }
 
-/// When calculating memory state, only the last FSRSItem is required.
+/// When calculating memory state, only the last FSRSItem is required. If the
+/// revlog is non-empty and no learning steps have been detected (indicative of
+/// a truncated revlog), we return the starting state inferred from the first
+/// revlog entry, so that the first review is not treated as if started from
+/// scratch.
 pub(crate) fn single_card_revlog_to_item(
+    fsrs: &FSRS,
     entries: Vec<RevlogEntry>,
     next_day_at: TimestampSecs,
-) -> Option<FSRSItem> {
+) -> Option<FsrsItemWithStartingState> {
-    let items = single_card_revlog_to_items(entries, next_day_at, false);
+    let have_learning = entries
-    items.and_then(|mut i| i.pop())
+        .iter()
+        .any(|e| e.review_kind == RevlogReviewKind::Learning);
+    if have_learning {
+        let items = single_card_revlog_to_items(entries, next_day_at, false);
+        Some(FsrsItemWithStartingState {
+            item: items.unwrap().pop().unwrap(),
+            starting_state: None,
+        })
+    } else if let Some(first_review) = entries.iter().find(|e| e.button_chosen > 0) {
+        let ease_factor = if first_review.ease_factor == 0 {
+            2500
+        } else {
+            first_review.ease_factor
+        };
+        let interval = first_review.interval.max(1);
+        let starting_state =
+            fsrs.memory_state_from_sm2(ease_factor as f32 / 1000.0, interval as f32);
+        let items = single_card_revlog_to_items(entries, next_day_at, false);
+        items.and_then(|mut items| {
+            let mut item = items.pop().unwrap();
+            item.reviews.remove(0);
+            if item.reviews.is_empty() {
+                None
+            } else {
+                Some(FsrsItemWithStartingState {
+                    item,
+                    starting_state: Some(starting_state),
+                })
+            }
+        })
+    } else {
+        None
+    }
 }
 
 #[cfg(test)]
 mod tests {
     use fsrs::MemoryState;
 
-    use super::super::weights::tests::fsrs_items;
     use super::*;
+    use crate::card::FsrsMemoryState;
     use crate::revlog::RevlogReviewKind;
     use crate::scheduler::fsrs::weights::tests::convert;
-    use crate::scheduler::fsrs::weights::tests::review;
     use crate::scheduler::fsrs::weights::tests::revlog;
 
     #[test]
     fn bypassed_learning_is_handled() {
         // cards without any learning steps due to truncated history still have memory
         // state calculated
+        let fsrs = FSRS::new(Some(&[])).unwrap();
+        let item = single_card_revlog_to_item(
+            &fsrs,
+            vec![
+                RevlogEntry {
+                    ease_factor: 2500,
+                    interval: 100,
+                    ..revlog(RevlogReviewKind::Review, 100)
+                },
+                revlog(RevlogReviewKind::Review, 1),
+            ],
+            TimestampSecs::now(),
+        )
+        .unwrap();
         assert_eq!(
-            convert(
+            item.starting_state,
-                &[
+            Some(MemoryState {
-                    RevlogEntry {
+                stability: 100.,
-                        ease_factor: 2500,
+                difficulty: 4.4642878
-                        ..revlog(RevlogReviewKind::Manual, 7)
+            })
-                    },
+        );
-                    revlog(RevlogReviewKind::Review, 6),
+        let mut card = Card::default();
-                ],
+        card.set_memory_state(&fsrs, Some(item));
-                false,
+        assert_eq!(
-            ),
+            card.memory_state,
-            fsrs_items!([review(0)])
+            Some(FsrsMemoryState {
+                stability: 248.47879,
+                difficulty: 4.468945
+            })
+        );
+        // but if there's only a single revlog entry, we'll fall back on current card
+        // state
+        let item = single_card_revlog_to_item(
+            &fsrs,
+            vec![RevlogEntry {
+                ease_factor: 2500,
+                interval: 100,
+                ..revlog(RevlogReviewKind::Review, 100)
+            }],
+            TimestampSecs::now(),
+        );
+        assert!(item.is_none());
+        card.interval = 123;
+        card.ease_factor = 2000;
+        card.ctype = CardType::Review;
+        card.set_memory_state(&fsrs, item);
+        assert_eq!(
+            card.memory_state,
+            Some(FsrsMemoryState {
+                stability: 123.0,
+                difficulty: 6.5147324,
+            })
         );
     }