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Feat/calc SInc based on last d #111

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merged 5 commits into from
Oct 27, 2023
Merged

Feat/calc SInc based on last d #111

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0e6f216
Feat/calc SInc based on last_d
L-M-Sherlock Oct 26, 2023
4f71992
fix &rating
L-M-Sherlock Oct 26, 2023
6a44c2a
cargo fmt
L-M-Sherlock Oct 26, 2023
6384a79
use clamp
asukaminato0721 Oct 27, 2023
a9331df
one more clamp
asukaminato0721 Oct 27, 2023
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16 changes: 8 additions & 8 deletions src/inference.rs
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -244,7 +244,7 @@ pub struct ItemProgress {
fn get_bin(x: f32, bins: i32) -> i32 {
let log_base = (bins.add(1) as f32).ln();
let binned_x = (x * log_base).exp().floor().sub(1.0);
(binned_x as i32).min(bins - 1).max(0)
(binned_x as i32).clamp(0, bins - 1)
}

fn calibration_rmse(pred: &[f32], true_val: &[f32]) -> f32 {
Expand Down Expand Up @@ -387,13 +387,13 @@ mod tests {
let metrics = fsrs.evaluate(items.clone(), |_| true).unwrap();

Data::from([metrics.log_loss, metrics.rmse_bins])
.assert_approx_eq(&Data::from([0.20753297, 0.041_122_54]), 5);
.assert_approx_eq(&Data::from([0.20745006, 0.040_497_02]), 5);

let fsrs = FSRS::new(Some(WEIGHTS))?;
let metrics = fsrs.evaluate(items, |_| true).unwrap();

Data::from([metrics.log_loss, metrics.rmse_bins])
.assert_approx_eq(&Data::from([0.20320644, 0.016_822_13]), 5);
.assert_approx_eq(&Data::from([0.20321770, 0.015_836_29]), 5);
Ok(())
}

Expand Down Expand Up @@ -426,17 +426,17 @@ mod tests {
NextStates {
again: ItemState {
memory: MemoryState {
stability: 4.5604353,
stability: 4.5802255,
difficulty: 8.881129,
},
interval: 5
},
hard: ItemState {
memory: MemoryState {
stability: 26.111229,
stability: 27.7025,
difficulty: 7.9430957
},
interval: 26,
interval: 28,
},
good: ItemState {
memory: MemoryState {
Expand All @@ -447,10 +447,10 @@ mod tests {
},
easy: ItemState {
memory: MemoryState {
stability: 121.01552,
stability: 101.98282,
difficulty: 6.0670285
},
interval: 121,
interval: 102,
}
}
);
Expand Down
17 changes: 9 additions & 8 deletions src/model.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -69,7 +69,7 @@ impl<B: Backend> Model<B> {
fn stability_after_success(
&self,
last_s: Tensor<B, 1>,
new_d: Tensor<B, 1>,
last_d: Tensor<B, 1>,
r: Tensor<B, 1>,
rating: Tensor<B, 1>,
) -> Tensor<B, 1> {
Expand All @@ -81,7 +81,7 @@ impl<B: Backend> Model<B> {

last_s.clone()
* (self.w.get(8).exp()
* (-new_d + 11)
* (-last_d + 11)
* (last_s.pow(-self.w.get(9)))
* (((-r + 1) * self.w.get(10)).exp() - 1)
* hard_penalty
Expand All @@ -92,11 +92,11 @@ impl<B: Backend> Model<B> {
fn stability_after_failure(
&self,
last_s: Tensor<B, 1>,
new_d: Tensor<B, 1>,
last_d: Tensor<B, 1>,
r: Tensor<B, 1>,
) -> Tensor<B, 1> {
self.w.get(11)
* new_d.pow(-self.w.get(12))
* last_d.pow(-self.w.get(12))
* ((last_s + 1).pow(self.w.get(13)) - 1)
* ((-r + 1) * self.w.get(14)).exp()
}
Expand Down Expand Up @@ -125,21 +125,22 @@ impl<B: Backend> Model<B> {
) -> MemoryStateTensors<B> {
let (new_s, new_d) = if let Some(state) = state {
let retention = self.power_forgetting_curve(delta_t, state.stability.clone());
let mut new_difficulty = self.next_difficulty(state.difficulty.clone(), rating.clone());
new_difficulty = self.mean_reversion(new_difficulty).clamp(1.0, 10.0);
let stability_after_success = self.stability_after_success(
state.stability.clone(),
new_difficulty.clone(),
state.difficulty.clone(),
retention.clone(),
rating.clone(),
);
let stability_after_failure = self.stability_after_failure(
state.stability.clone(),
new_difficulty.clone(),
state.difficulty.clone(),
retention.clone(),
);
let mut new_stability = stability_after_success
.mask_where(rating.clone().equal_elem(1), stability_after_failure);

let mut new_difficulty = self.next_difficulty(state.difficulty.clone(), rating.clone());
new_difficulty = self.mean_reversion(new_difficulty).clamp(1.0, 10.0);
// mask padding zeros for rating
new_stability = new_stability.mask_where(rating.clone().equal_elem(0), state.stability);
new_difficulty = new_difficulty.mask_where(rating.equal_elem(0), state.difficulty);
Expand Down
46 changes: 29 additions & 17 deletions src/optimal_retention.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -76,8 +76,8 @@ impl Default for SimulatorConfig {
}

fn stability_after_success(w: &[f64], s: f64, r: f64, d: f64, response: usize) -> f64 {
let hard_penalty = if response == 1 { w[15] } else { 1.0 };
let easy_bonus = if response == 3 { w[16] } else { 1.0 };
let hard_penalty = if response == 2 { w[15] } else { 1.0 };
let easy_bonus = if response == 4 { w[16] } else { 1.0 };
s * (1.0
+ f64::exp(w[8])
* (11.0 - d)
Expand All @@ -88,8 +88,8 @@ fn stability_after_success(w: &[f64], s: f64, r: f64, d: f64, response: usize) -
}

fn stability_after_failure(w: &[f64], s: f64, r: f64, d: f64) -> f64 {
s.min(w[11] * d.powf(-w[12]) * ((s + 1.0).powf(w[13]) - 1.0) * f64::exp((1.0 - r) * w[14]))
.max(0.1)
(w[11] * d.powf(-w[12]) * ((s + 1.0).powf(w[13]) - 1.0) * f64::exp((1.0 - r) * w[14]))
.clamp(0.1, s)
}

fn simulate(config: &SimulatorConfig, w: &[f64], request_retention: f64, seed: Option<u64>) -> f64 {
Expand All @@ -116,10 +116,10 @@ fn simulate(config: &SimulatorConfig, w: &[f64], request_retention: f64, seed: O
// let mut learn_cnt_per_day = Array1::<f64>::zeros(learn_span);
let mut memorized_cnt_per_day = Array1::zeros(learn_span);

let first_rating_choices = [0, 1, 2, 3];
let first_rating_choices = [1, 2, 3, 4];
let first_rating_dist = WeightedIndex::new(first_rating_prob).unwrap();

let review_rating_choices = [1, 2, 3];
let review_rating_choices = [2, 3, 4];
let review_rating_dist = WeightedIndex::new(review_rating_prob).unwrap();

let mut rng = StdRng::seed_from_u64(seed.unwrap_or(42));
Expand Down Expand Up @@ -180,8 +180,8 @@ fn simulate(config: &SimulatorConfig, w: &[f64], request_retention: f64, seed: O

// Sample 'rating' for 'need_review' entries
let mut ratings = Array1::zeros(deck_size);
izip!(&mut ratings, &need_review)
.filter(|(_, &need_review_flag)| need_review_flag)
izip!(&mut ratings, &(&need_review & !&forget))
.filter(|(_, &condition)| condition)
.for_each(|(rating, _)| {
*rating = review_rating_choices[review_rating_dist.sample(&mut rng)]
});
Expand All @@ -193,12 +193,13 @@ fn simulate(config: &SimulatorConfig, w: &[f64], request_retention: f64, seed: O
*cost = if forget_flag {
forget_cost * loss_aversion
} else {
recall_costs[rating - 1]
recall_costs[rating - 2]
}
});

// Calculate cumulative sum of 'cost'
let mut cum_sum = Array1::<f64>::zeros(deck_size);
cum_sum[0] = cost[0];
for i in 1..deck_size {
cum_sum[i] = cum_sum[i - 1] + cost[i];
}
Expand All @@ -219,6 +220,7 @@ fn simulate(config: &SimulatorConfig, w: &[f64], request_retention: f64, seed: O
*cost = learn_cost;
});

cum_sum[0] = cost[0];
for i in 1..deck_size {
cum_sum[i] = cum_sum[i - 1] + cost[i];
}
Expand Down Expand Up @@ -276,9 +278,21 @@ fn simulate(config: &SimulatorConfig, w: &[f64], request_retention: f64, seed: O
izip!(&mut new_difficulty, &old_difficulty, &true_review, &forget)
.filter(|(.., &true_rev, &frgt)| true_rev && frgt)
.for_each(|(new_diff, &old_diff, ..)| {
*new_diff = (old_diff + 2.0 * w[6]).max(1.0).min(10.0);
*new_diff = (old_diff + 2.0 * w[6]).clamp(1.0, 10.0);
});

// Update the difficulty values based on the condition 'true_review & !forget'
izip!(
&mut new_difficulty,
&old_difficulty,
&ratings,
&(&true_review & !&forget)
)
.filter(|(.., &condition)| condition)
.for_each(|(new_diff, &old_diff, &rating, ..)| {
*new_diff = (old_diff - w[6] * (rating as f64 - 3.0)).clamp(1.0, 10.0);
});

// Update 'last_date' column where 'true_review' or 'true_learn' is true
let mut new_last_date = old_last_date.to_owned();
izip!(&mut new_last_date, &true_review, &true_learn)
Expand All @@ -295,8 +309,8 @@ fn simulate(config: &SimulatorConfig, w: &[f64], request_retention: f64, seed: O
)
.filter(|(.., &true_learn_flag)| true_learn_flag)
.for_each(|(new_stab, new_diff, &rating, _)| {
*new_stab = w[rating];
*new_diff = w[4] - w[5] * (rating as f64 - 3.0);
*new_stab = w[rating - 1];
*new_diff = (w[4] - w[5] * (rating as f64 - 3.0)).clamp(1.0, 10.0);
});
let old_interval = card_table.slice(s![Column::Interval, ..]);
let mut new_interval = old_interval.to_owned();
Expand All @@ -305,8 +319,7 @@ fn simulate(config: &SimulatorConfig, w: &[f64], request_retention: f64, seed: O
.for_each(|(new_ivl, &new_stab, ..)| {
*new_ivl = (9.0 * new_stab * (1.0 / request_retention - 1.0))
.round()
.min(max_ivl)
.max(1.0);
.clamp(1.0, max_ivl);
});

let old_due = card_table.slice(s![Column::Due, ..]);
Expand All @@ -331,7 +344,6 @@ fn simulate(config: &SimulatorConfig, w: &[f64], request_retention: f64, seed: O
card_table
.slice_mut(s![Column::Interval, ..])
.assign(&new_interval);

// Update the review_cnt_per_day, learn_cnt_per_day and memorized_cnt_per_day
// review_cnt_per_day[today] = true_review.iter().filter(|&&x| x).count() as f64;
// learn_cnt_per_day[today] = true_learn.iter().filter(|&&x| x).count() as f64;
Expand Down Expand Up @@ -436,15 +448,15 @@ mod tests {
0.9,
None,
);
assert_eq!(memorization, 2635.689850107157)
assert_eq!(memorization, 2633.365434092778)
}

#[test]
fn optimal_retention() -> Result<()> {
let config = SimulatorConfig::default();
let fsrs = FSRS::new(None)?;
let optimal_retention = fsrs.optimal_retention(&config, &[], |_v| true).unwrap();
assert_eq!(optimal_retention, 0.8633668648071942);
assert_eq!(optimal_retention, 0.8530025910684347);
assert!(fsrs.optimal_retention(&config, &[1.], |_v| true).is_err());
Ok(())
}
Expand Down