Anki/rslib/src/scheduler/fsrs/memory_state.rs

347 lines
13 KiB
Rust

// Copyright: Ankitects Pty Ltd and contributors
// License: GNU AGPL, version 3 or later; http://www.gnu.org/licenses/agpl.html
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;
use crate::search::JoinSearches;
use crate::search::Negated;
use crate::search::SearchNode;
use crate::search::StateKind;
#[derive(Debug, Clone, Copy, Default)]
pub struct ComputeMemoryProgress {
pub current_cards: u32,
pub total_cards: u32,
}
#[derive(Debug)]
pub(crate) struct UpdateMemoryStateRequest {
pub weights: Weights,
pub desired_retention: f32,
pub max_interval: u32,
pub reschedule: bool,
}
impl Collection {
/// For each provided set of weights, locate cards with the provided search,
/// and update their memory state.
/// Should be called inside a transaction.
/// If Weights are None, it means the user disabled FSRS, and the existing
/// memory state should be removed.
pub(crate) fn update_memory_state(
&mut self,
entries: Vec<(Option<UpdateMemoryStateRequest>, Vec<SearchNode>)>,
) -> Result<()> {
let timing = self.timing_today()?;
let usn = self.usn()?;
for (req, search) in entries {
let search = SearchBuilder::any(search.into_iter())
.and(SearchNode::State(StateKind::New).negated());
let revlog = self.revlog_for_srs(search)?;
let reschedule = req.as_ref().map(|e| e.reschedule).unwrap_or_default();
let last_reviews = if reschedule {
Some(get_last_reviews(&revlog))
} else {
None
};
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() {
progress.update(true, |state| state.current_cards = idx as u32 + 1)?;
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);
card.desired_retention = desired_retention;
// if rescheduling
if let Some(reviews) = &last_reviews {
// and we have a last review time for the card
if let Some(last_review) = reviews.get(&card.id) {
let days_elapsed =
timing.next_day_at.elapsed_days_since(*last_review) as i32;
// and the card's not new
if let Some(state) = &card.memory_state {
// or in (re)learning
if card.ctype == CardType::Review {
// reschedule it
let original_interval = card.interval;
let interval = fsrs.next_interval(
Some(state.stability),
card.desired_retention.unwrap(),
0,
) as f32;
card.interval = with_review_fuzz(
card.get_fuzz_factor(),
interval,
1,
req.max_interval,
);
let due = if card.original_due != 0 {
&mut card.original_due
} else {
&mut card.due
};
*due = (timing.days_elapsed as i32) - days_elapsed
+ card.interval as i32;
self.log_manually_scheduled_review(
&card,
original_interval,
usn,
)?;
}
}
}
}
} else {
card.memory_state = None;
card.desired_retention = None;
}
self.update_card_inner(&mut card, original, usn)?;
}
}
Ok(())
}
pub fn compute_memory_state(&mut self, card_id: CardId) -> Result<ComputeMemoryStateResponse> {
let mut card = self.storage.get_card(card_id)?.or_not_found(card_id)?;
let deck_id = card.original_deck_id.or(card.deck_id);
let deck = self.get_deck(deck_id)?.or_not_found(card.deck_id)?;
let conf_id = DeckConfigId(deck.normal()?.config_id);
let config = self
.storage
.get_deck_config(conf_id)?
.or_not_found(conf_id)?;
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(&fsrs, revlog, self.timing_today()?.next_day_at);
card.set_memory_state(&fsrs, item);
Ok(ComputeMemoryStateResponse {
state: card.memory_state.map(Into::into),
desired_retention,
})
}
}
impl Card {
pub(crate) fn set_memory_state(
&mut self,
fsrs: &FSRS,
item: Option<FsrsItemWithStartingState>,
) {
self.memory_state = item
.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))
}
})
.map(Into::into);
}
}
#[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<FsrsItemWithStartingState>)> {
revlogs
.into_iter()
.group_by(|r| r.cid)
.into_iter()
.map(|(card_id, group)| {
(
card_id,
single_card_revlog_to_item(fsrs, group.collect(), next_day_at),
)
})
.collect()
}
/// Return a map of cards to the last time they were reviewed.
fn get_last_reviews(revlogs: &[RevlogEntry]) -> HashMap<CardId, TimestampSecs> {
let mut out = HashMap::new();
revlogs
.iter()
.group_by(|r| r.cid)
.into_iter()
.for_each(|(card_id, group)| {
let mut last_ts = TimestampSecs::zero();
for entry in group.into_iter().filter(|r| r.button_chosen >= 1) {
last_ts = entry.id.as_secs();
}
if last_ts != TimestampSecs::zero() {
out.insert(card_id, last_ts);
}
});
out
}
/// 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<FsrsItemWithStartingState> {
let have_learning = entries
.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::*;
use crate::card::FsrsMemoryState;
use crate::revlog::RevlogReviewKind;
use crate::scheduler::fsrs::weights::tests::convert;
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!(
item.starting_state,
Some(MemoryState {
stability: 100.,
difficulty: 4.4642878
})
);
let mut card = Card::default();
card.set_memory_state(&fsrs, Some(item));
assert_eq!(
card.memory_state,
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,
})
);
}
#[test]
fn zero_history_is_handled() {
// when the history is empty, no items are produced
assert_eq!(convert(&[], false), None);
// but memory state should still be inferred, by using the card's current state
let mut card = Card {
ctype: CardType::Review,
interval: 100,
ease_factor: 1300,
..Default::default()
};
card.set_memory_state(&FSRS::new(Some(&[])).unwrap(), None);
assert_eq!(
card.memory_state,
Some(
MemoryState {
stability: 100.0,
difficulty: 9.692858
}
.into()
)
);
}
}