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Fix/set mean reversion target to d0(4) #205

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Jul 28, 2024
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Fix/set mean reversion target to d0(4) #205

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081039a
Fix/set mean reversion target to D0(4)
L-M-Sherlock Jul 26, 2024
94671f2
reuse init_difficulty in mean_reversion
L-M-Sherlock Jul 27, 2024
83f68d7
bump version
L-M-Sherlock Jul 27, 2024
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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 = "1.1.3"
version = "1.1.4"
authors = ["Open Spaced Repetition"]
categories = ["algorithms", "science"]
edition = "2021"
Expand Down
68 changes: 42 additions & 26 deletions src/inference.rs
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Expand Up @@ -22,8 +22,8 @@ pub type Parameters = [f32];
use itertools::izip;

pub static DEFAULT_PARAMETERS: [f32; 19] = [
0.4197, 1.1869, 3.0412, 15.2441, 7.1434, 0.6477, 1.0007, 0.0674, 1.6597, 0.1712, 1.1178,
2.0225, 0.0904, 0.3025, 2.1214, 0.2498, 2.9466, 0.4891, 0.6468,
0.4072, 1.1829, 3.1262, 15.4722, 7.2102, 0.5316, 1.0651, 0.0234, 1.616, 0.1544, 1.0824, 1.9813,
0.0953, 0.2975, 2.2042, 0.2407, 2.9466, 0.5034, 0.6567,
];

fn infer<B: Backend>(
Expand Down Expand Up @@ -377,9 +377,25 @@ mod tests {
};

static PARAMETERS: &[f32] = &[
0.72466177, 1.6790825, 4.562257, 10.0608635, 7.7002444, 0.912309, 1.0909119, 0.03472257,
1.4395499, 0.1712, 0.8977034, 2.1090207, 0.0904, 0.3025, 2.3506782, 0.23486121, 3.1349943,
0.18253358, 0.13885707,
0.6845422,
1.6790825,
4.7349424,
10.042885,
7.4410233,
0.64219797,
1.071918,
0.0025195254,
1.432437,
0.1544,
0.8692766,
2.0696752,
0.0953,
0.2975,
2.4691248,
0.19542035,
3.201072,
0.18046261,
0.121442534,
];

#[test]
Expand Down Expand Up @@ -427,8 +443,8 @@ mod tests {
assert_eq!(
fsrs.memory_state(item, None).unwrap(),
MemoryState {
stability: 29.448196,
difficulty: 7.7002444
stability: 31.722975,
difficulty: 7.382128
}
);

Expand All @@ -445,8 +461,8 @@ mod tests {
.good
.memory,
MemoryState {
stability: 40.669125,
difficulty: 7.0292006
stability: 40.874535,
difficulty: 6.9913807
}
);
Ok(())
Expand Down Expand Up @@ -478,26 +494,26 @@ mod tests {
let metrics = fsrs.evaluate(items.clone(), |_| true).unwrap();

Data::from([metrics.log_loss, metrics.rmse_bins])
.assert_approx_eq(&Data::from([0.202137, 0.025207]), 5);
.assert_approx_eq(&Data::from([0.210983, 0.037216]), 5);

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

Data::from([metrics.log_loss, metrics.rmse_bins])
.assert_approx_eq(&Data::from([0.216539, 0.039671]), 5);
.assert_approx_eq(&Data::from([0.217689, 0.039710]), 5);

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

Data::from([metrics.log_loss, metrics.rmse_bins])
.assert_approx_eq(&Data::from([0.202754, 0.026365]), 5);
.assert_approx_eq(&Data::from([0.203235, 0.026295]), 5);

let (self_by_other, other_by_self) = fsrs
.universal_metrics(items.clone(), &DEFAULT_PARAMETERS, |_| true)
.unwrap();

Data::from([self_by_other, other_by_self])
.assert_approx_eq(&Data::from([0.015230, 0.032233]), 5);
.assert_approx_eq(&Data::from([0.014476, 0.031874]), 5);

Ok(())
}
Expand Down Expand Up @@ -531,31 +547,31 @@ mod tests {
NextStates {
again: ItemState {
memory: MemoryState {
stability: 3.0271175,
difficulty: 9.80631
stability: 2.969144,
difficulty: 9.520562
},
interval: 3
},
hard: ItemState {
memory: MemoryState {
stability: 16.725859,
difficulty: 8.753278
stability: 17.091442,
difficulty: 8.4513445
},
interval: 17
},
good: ItemState {
memory: MemoryState {
stability: 29.448196,
difficulty: 7.7002444
stability: 31.722975,
difficulty: 7.382128
},
interval: 29
interval: 32
},
easy: ItemState {
memory: MemoryState {
stability: 64.947784,
difficulty: 6.647212
stability: 71.75015,
difficulty: 6.3129106
},
interval: 65
interval: 72
}
}
);
Expand All @@ -581,13 +597,13 @@ mod tests {
let fsrs = FSRS::new(Some(&[]))?;
let memory_state = fsrs.memory_state_from_sm2(2.5, 10.0, 0.9).unwrap();
Data::from([memory_state.stability, memory_state.difficulty])
.assert_approx_eq(&Data::from([9.999996, 7.422087]), 5);
.assert_approx_eq(&Data::from([9.999996, 7.279789]), 5);
let memory_state = fsrs.memory_state_from_sm2(2.5, 10.0, 0.8).unwrap();
Data::from([memory_state.stability, memory_state.difficulty])
.assert_approx_eq(&Data::from([4.170096, 9.545_82]), 5);
.assert_approx_eq(&Data::from([4.170096, 9.462736]), 5);
let memory_state = fsrs.memory_state_from_sm2(2.5, 10.0, 0.95).unwrap();
Data::from([memory_state.stability, memory_state.difficulty])
.assert_approx_eq(&Data::from([21.712555, 2.593589]), 5);
.assert_approx_eq(&Data::from([21.712555, 2.380210]), 5);
let memory_state = fsrs.memory_state_from_sm2(1.3, 20.0, 0.9).unwrap();
Data::from([memory_state.stability, memory_state.difficulty])
.assert_approx_eq(&Data::from([19.999992, 10.0]), 5);
Expand Down
13 changes: 7 additions & 6 deletions src/model.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -92,7 +92,8 @@ impl<B: Backend> Model<B> {
}

fn mean_reversion(&self, new_d: Tensor<B, 1>) -> Tensor<B, 1> {
self.w.get(7) * (self.w.get(4) - new_d.clone()) + new_d
let rating = Tensor::from_floats([4.0], &B::Device::default());
self.w.get(7) * (self.init_difficulty(rating) - new_d.clone()) + new_d
}

pub(crate) fn init_stability(&self, rating: Tensor<B, 1>) -> Tensor<B, 1> {
Expand Down Expand Up @@ -358,7 +359,7 @@ mod tests {
next_difficulty.clone().backward();
assert_eq!(
next_difficulty.to_data(),
Data::from([7.0109706, 6.077718, 5.144465, 4.211212])
Data::from([7.040172, 5.999995, 4.959819, 3.9196422])
)
}

Expand All @@ -379,24 +380,24 @@ mod tests {
s_recall.clone().backward();
assert_eq!(
s_recall.to_data(),
Data::from([28.603035, 16.240442, 68.610886, 237.08693])
Data::from([27.43741, 15.276875, 65.24019, 224.3506])
);
let s_forget = model.stability_after_failure(stability.clone(), difficulty, retention);
s_forget.clone().backward();
assert_eq!(
s_forget.to_data(),
Data::from([1.7989675, 2.089014, 2.4897401, 2.9990985])
Data::from([1.7390966, 2.029377, 2.433932, 2.9520853])
);
let next_stability = s_recall.mask_where(rating.clone().equal_elem(1), s_forget);
next_stability.clone().backward();
assert_eq!(
next_stability.to_data(),
Data::from([1.7989675, 16.240442, 68.610886, 237.08693])
Data::from([1.7390966, 15.276875, 65.24019, 224.3506])
);
let next_stability = model.stability_short_term(stability, rating);
assert_eq!(
next_stability.to_data(),
Data::from([2.5794802, 4.206739, 6.8605514, 11.188516])
Data::from([2.542685, 4.2064567, 6.958895, 11.512355])
)
}

Expand Down
22 changes: 13 additions & 9 deletions src/optimal_retention.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -118,14 +118,18 @@ fn stability_short_term(w: &[f32], s: f32, rating_offset: f32, session_len: f32)
s * (w[17] * (rating_offset + session_len * w[18])).exp()
}

fn init_d(w: &[f32], rating: usize, rating_offset: f32) -> f32 {
let new_d = w[4] - (w[5] * (rating - 1) as f32).exp() + 1.0 - w[6] * rating_offset;
fn init_d(w: &[f32], rating: usize) -> f32 {
w[4] - (w[5] * (rating - 1) as f32).exp() + 1.0
}

fn init_d_with_short_term(w: &[f32], rating: usize, rating_offset: f32) -> f32 {
let new_d = init_d(w, rating) - w[6] * rating_offset;
new_d.clamp(1.0, 10.0)
}

fn next_d(w: &[f32], d: f32, rating: usize) -> f32 {
let new_d = d - w[6] * (rating as f32 - 3.0);
mean_reversion(w, w[4], new_d).clamp(1.0, 10.0)
mean_reversion(w, init_d(w, 4), new_d).clamp(1.0, 10.0)
}

fn mean_reversion(w: &[f32], init: f32, current: f32) -> f32 {
Expand Down Expand Up @@ -394,7 +398,7 @@ pub fn simulate(
first_rating_offsets[rating - 1],
first_session_lens[rating - 1],
);
*new_diff = init_d(w, rating, first_rating_offsets[rating - 1]);
*new_diff = init_d_with_short_term(w, rating, first_rating_offsets[rating - 1]);
});
let old_interval = card_table.slice(s![Column::Interval, ..]);
let mut new_interval = old_interval.to_owned();
Expand Down Expand Up @@ -1004,7 +1008,7 @@ mod tests {
simulate(&config, &DEFAULT_PARAMETERS, 0.9, None, None);
assert_eq!(
memorized_cnt_per_day[memorized_cnt_per_day.len() - 1],
7020.523
6521.068
)
}

Expand Down Expand Up @@ -1048,8 +1052,8 @@ mod tests {
assert_eq!(
results.1.to_vec(),
vec![
0, 15, 16, 17, 70, 73, 77, 82, 75, 87, 86, 111, 113, 110, 105, 112, 124, 131, 127,
119, 122, 163, 145, 150, 171, 150, 136, 163, 167, 156
0, 15, 16, 27, 69, 66, 81, 83, 75, 85, 90, 102, 107, 105, 122, 120, 141, 117, 132,
153, 153, 154, 152, 152, 149, 157, 166, 165, 184, 174
]
);
assert_eq!(
Expand All @@ -1071,7 +1075,7 @@ mod tests {
..Default::default()
};
let optimal_retention = fsrs.optimal_retention(&config, &[], |_v| true).unwrap();
assert_eq!(optimal_retention, 0.8263932);
assert_eq!(optimal_retention, 0.7921062);
assert!(fsrs.optimal_retention(&config, &[1.], |_v| true).is_err());
Ok(())
}
Expand All @@ -1091,7 +1095,7 @@ mod tests {
let optimal_retention = fsrs
.optimal_retention(&config, &DEFAULT_PARAMETERS[..17], |_v| true)
.unwrap();
assert_eq!(optimal_retention, 0.81812924);
assert_eq!(optimal_retention, 0.8430037);
Ok(())
}

Expand Down
6 changes: 3 additions & 3 deletions src/pre_training.rs
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Expand Up @@ -296,9 +296,9 @@ mod tests {
let count = Array1::from(vec![435.0, 97.0, 63.0, 38.0, 28.0]);
let default_s0 = DEFAULT_PARAMETERS[0] as f64;
let actual = loss(&delta_t, &recall, &count, 1.017056, default_s0);
assert_eq!(actual, 280.7489989949864);
assert_eq!(actual, 280.7497802442413);
let actual = loss(&delta_t, &recall, &count, 1.017011, default_s0);
assert_eq!(actual, 280.74866497463466);
assert_eq!(actual, 280.7494462238896);
}

#[test]
Expand Down Expand Up @@ -363,6 +363,6 @@ mod tests {
let mut rating_stability = HashMap::from([(2, 0.35)]);
let rating_count = HashMap::from([(2, 1)]);
let actual = smooth_and_fill(&mut rating_stability, &rating_count).unwrap();
assert_eq!(actual, [0.123763576, 0.34999996, 0.8968067, 4.495269]);
assert_eq!(actual, [0.12048356, 0.35, 0.9249894, 4.577961]);
}
}
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