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Feat/Simulator learn affects review limit option #267

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merged 5 commits into from
Jan 1, 2025
Merged

Feat/Simulator learn affects review limit option #267

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01c6b3d
Simulator learn_affects_review_limit
Luc-Mcgrady Dec 28, 2024
4f790a1
Replaced with match statement
Luc-Mcgrady Dec 28, 2024
60c7fe4
Rename in line with Anki
Luc-Mcgrady Dec 29, 2024
ed93697
refactor: introduce SimulationResult struct for simulation output
L-M-Sherlock Dec 31, 2024
57f65c3
bump version
L-M-Sherlock Jan 1, 2025
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2 changes: 1 addition & 1 deletion Cargo.lock
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2 changes: 1 addition & 1 deletion Cargo.toml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
[package]
name = "fsrs"
version = "1.4.9"
version = "2.0.0"
authors = ["Open Spaced Repetition"]
categories = ["algorithms", "science"]
edition = "2021"
Expand Down
134 changes: 106 additions & 28 deletions src/optimal_retention.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,14 @@ use rayon::iter::IntoParallelIterator;
use rayon::iter::ParallelIterator;
use std::collections::HashMap;

#[derive(Debug)]
pub struct SimulationResult {
pub memorized_cnt_per_day: Array1<f32>,
pub review_cnt_per_day: Array1<usize>,
pub learn_cnt_per_day: Array1<usize>,
pub cost_per_day: Array1<f32>,
}

trait Round {
fn to_2_decimal(self) -> f32;
}
Expand Down Expand Up @@ -44,6 +52,7 @@ pub struct SimulatorConfig {
pub loss_aversion: f32,
pub learn_limit: usize,
pub review_limit: usize,
pub new_cards_ignore_review_limit: bool,
}

impl Default for SimulatorConfig {
Expand All @@ -64,6 +73,7 @@ impl Default for SimulatorConfig {
loss_aversion: 2.5,
learn_limit: usize::MAX,
review_limit: usize::MAX,
new_cards_ignore_review_limit: true,
}
}
}
Expand Down Expand Up @@ -127,14 +137,13 @@ pub struct Card {
pub due: f32,
}

#[allow(clippy::type_complexity)]
pub fn simulate(
config: &SimulatorConfig,
w: &Parameters,
desired_retention: f32,
seed: Option<u64>,
existing_cards: Option<Vec<Card>>,
) -> Result<(Array1<f32>, Array1<usize>, Array1<usize>, Array1<f32>), FSRSError> {
) -> Result<SimulationResult, FSRSError> {
let w = &check_and_fill_parameters(w)?;
let w = &clip_parameters(w);
let SimulatorConfig {
Expand All @@ -153,6 +162,7 @@ pub fn simulate(
loss_aversion,
learn_limit,
review_limit,
new_cards_ignore_review_limit,
} = config.clone();
if deck_size == 0 {
return Err(FSRSError::InvalidDeckSize);
Expand Down Expand Up @@ -238,9 +248,17 @@ pub fn simulate(
card_priorities.pop();
continue;
}
if (!is_learn && review_cnt_per_day[day_index] + 1 > review_limit)
|| (is_learn && learn_cnt_per_day[day_index] + 1 > learn_limit)
|| (cost_per_day[day_index] + fail_cost > max_cost_perday)

let todays_learn = learn_cnt_per_day[day_index];
let todays_review = review_cnt_per_day[day_index];

if match (new_cards_ignore_review_limit, is_learn) {
(true, true) => todays_learn + 1 > learn_limit,
(false, true) => {
todays_learn + todays_review + 1 > review_limit || todays_learn + 1 > learn_limit
}
(_, false) => todays_review + 1 > review_limit,
} || (cost_per_day[day_index] + fail_cost > max_cost_perday)
{
card.due = day_index as f32 + 1.0;
card_priorities.change_priority(&card_index, card_priority(card, is_learn));
Expand Down Expand Up @@ -338,12 +356,12 @@ pub fn simulate(
&cost_per_day[learn_span - 1],
));*/

Ok((
Ok(SimulationResult {
memorized_cnt_per_day,
review_cnt_per_day,
learn_cnt_per_day,
cost_per_day,
))
})
}

fn sample<F>(
Expand All @@ -362,7 +380,11 @@ where
let results: Result<Vec<f32>, FSRSError> = (0..n)
.into_par_iter()
.map(|i| {
let (memorized_cnt_per_day, _, _, cost_per_day) = simulate(
let SimulationResult {
memorized_cnt_per_day,
cost_per_day,
..
} = simulate(
config,
parameters,
desired_retention,
Expand Down Expand Up @@ -904,8 +926,10 @@ mod tests {
#[test]
fn simulator() -> Result<()> {
let config = SimulatorConfig::default();
let (memorized_cnt_per_day, _, _, _) =
simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, None)?;
let SimulationResult {
memorized_cnt_per_day,
..
} = simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, None)?;
assert_eq!(
memorized_cnt_per_day[memorized_cnt_per_day.len() - 1],
6781.493
Expand All @@ -924,16 +948,20 @@ mod tests {
deck_size: DECK_SIZE,
..Default::default()
};
let (_, review_cnt_per_day_lower, _, _) =
simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, None)?;
let SimulationResult {
review_cnt_per_day: review_cnt_per_day_lower,
..
} = simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, None)?;
let config = SimulatorConfig {
learn_span: LOWER + 10,
learn_limit: LEARN_LIMIT,
deck_size: DECK_SIZE,
..Default::default()
};
let (_, review_cnt_per_day_higher, _, _) =
simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, None)?;
let SimulationResult {
review_cnt_per_day: review_cnt_per_day_higher,
..
} = simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, None)?;
// Compare first LOWER items of review_cnt_per_day arrays
for i in 0..LOWER {
assert_eq!(
Expand Down Expand Up @@ -980,8 +1008,12 @@ mod tests {
due: -1.0,
},
];
let (memorized_cnt_per_day, review_cnt_per_day, learn_cnt_per_day, _) =
simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, Some(cards))?;
let SimulationResult {
memorized_cnt_per_day,
review_cnt_per_day,
learn_cnt_per_day,
..
} = simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, Some(cards))?;
assert_eq!(memorized_cnt_per_day[0], 63.9);
assert_eq!(review_cnt_per_day[0], 3);
assert_eq!(learn_cnt_per_day[0], 60);
Expand All @@ -1007,21 +1039,52 @@ mod tests {
9
];

let (_, _, learn_cnt_per_day, _) =
simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, Some(cards))?;
let SimulationResult {
learn_cnt_per_day, ..
} = simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, Some(cards))?;

assert_eq!(learn_cnt_per_day.to_vec(), vec![3, 3, 3]);

Ok(())
}

#[test]
fn simulate_with_new_affects_review_limit() -> Result<()> {
let config = SimulatorConfig {
learn_limit: 3,
review_limit: 10,
learn_span: 3,
new_cards_ignore_review_limit: false,
deck_size: 20,
..Default::default()
};

let cards = vec![
Card {
difficulty: 5.0,
stability: 500.0,
last_date: -5.0,
due: 0.0,
};
9
];

let SimulationResult {
learn_cnt_per_day, ..
} = simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, Some(cards))?;

assert_eq!(learn_cnt_per_day.to_vec(), vec![1, 3, 3]);

Ok(())
}

#[test]
fn simulator_learn_review_costs() -> Result<()> {
const LEARN_COST: f32 = 42.;
const REVIEW_COST: f32 = 43.;

let config = SimulatorConfig {
deck_size: 1, // 1 learn card, 1
deck_size: 1,
learn_costs: [LEARN_COST; 4],
review_costs: [REVIEW_COST; 4],
learn_span: 1,
Expand All @@ -1035,11 +1098,16 @@ mod tests {
due: 0.0,
}];

let (_, _, _, cost_per_day_learn) =
simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, None)?;
let SimulationResult {
cost_per_day: cost_per_day_learn,
..
} = simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, None)?;
assert_eq!(cost_per_day_learn[0], LEARN_COST);
let (_, _, _, cost_per_day_review) =
simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, Some(cards))?;

let SimulationResult {
cost_per_day: cost_per_day_review,
..
} = simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, Some(cards))?;
assert_eq!(cost_per_day_review[0], REVIEW_COST);
Ok(())
}
Expand All @@ -1053,16 +1121,20 @@ mod tests {
max_cost_perday: f32::INFINITY,
..Default::default()
};
let results = simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, None)?;
let SimulationResult {
review_cnt_per_day,
learn_cnt_per_day,
..
} = simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, None)?;
assert_eq!(
results.1.to_vec(),
review_cnt_per_day.to_vec(),
vec![
0, 15, 18, 38, 64, 64, 80, 89, 95, 95, 100, 96, 107, 118, 120, 114, 126, 123, 139,
167, 158, 156, 167, 161, 154, 178, 163, 151, 160, 151
]
);
assert_eq!(
results.2.to_vec(),
learn_cnt_per_day.to_vec(),
vec![config.learn_limit; config.learn_span]
);
Ok(())
Expand All @@ -1074,8 +1146,14 @@ mod tests {
max_ivl: 100.0,
..Default::default()
};
let results = simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, None)?;
assert_eq!(results.0[results.0.len() - 1], 6487.4004);
let SimulationResult {
memorized_cnt_per_day,
..
} = simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, None)?;
assert_eq!(
memorized_cnt_per_day[memorized_cnt_per_day.len() - 1],
6487.4004
);
Ok(())
}

Expand Down
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