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Feat/linear damping && w[7] >= 0.001 (#143)
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* Feat/linear damping

* update default parameters

* update formula of simulator

* add more tests

* add test for loss and grad

* update ParameterClipper

* bump version
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@L-M-Sherlock
L-M-Sherlock authored Oct 22, 2024
1 parent 6f1d4a9 commit 4032352
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Showing 5 changed files with 181 additions and 53 deletions.
2 changes: 1 addition & 1 deletion pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

[project]
name = "FSRS-Optimizer"
version = "5.2.1"
version = "5.2.2"
readme = "README.md"
dependencies = [
"matplotlib>=3.7.0",
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85 changes: 36 additions & 49 deletions src/fsrs_optimizer/fsrs_optimizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
import numpy as np
import os
import math
from typing import List, Optional
from typing import List, Optional, Tuple
from datetime import timedelta, datetime
from collections import defaultdict
import statsmodels.api as sm # type: ignore
Expand Down Expand Up @@ -42,25 +42,25 @@
Relearning = 3

DEFAULT_PARAMETER = [
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,
0.40255,
1.18385,
3.173,
15.69105,
7.1949,
0.5345,
1.4604,
0.0046,
1.54575,
0.1192,
1.01925,
1.9395,
0.11,
0.29605,
2.2698,
0.2315,
2.9898,
0.51655,
0.6621,
]

S_MIN = 0.01
Expand Down Expand Up @@ -105,8 +105,12 @@ def init_d(self, rating: Tensor) -> Tensor:
new_d = self.w[4] - torch.exp(self.w[5] * (rating - 1)) + 1
return new_d

def linear_damping(self, delta_d: Tensor, old_d: Tensor) -> Tensor:
return delta_d * (10 - old_d) / 9

def next_d(self, state: Tensor, rating: Tensor) -> Tensor:
new_d = state[:, 1] - self.w[6] * (rating - 3)
delta_d = -self.w[6] * (rating - 3)
new_d = state[:, 1] + self.linear_damping(delta_d, state[:, 1])
new_d = self.mean_reversion(self.init_d(4), new_d)
return new_d

Expand Down Expand Up @@ -151,7 +155,9 @@ def step(self, X: Tensor, state: Tensor) -> Tensor:
new_s = new_s.clamp(S_MIN, 36500)
return torch.stack([new_s, new_d], dim=1)

def forward(self, inputs: Tensor, state: Optional[Tensor] = None) -> Tensor:
def forward(
self, inputs: Tensor, state: Optional[Tensor] = None
) -> Tuple[Tensor, Tensor]:
"""
:param inputs: shape[seq_len, batch_size, 2]
"""
Expand Down Expand Up @@ -179,16 +185,16 @@ def __call__(self, module):
w[2] = w[2].clamp(S_MIN, 100)
w[3] = w[3].clamp(S_MIN, 100)
w[4] = w[4].clamp(1, 10)
w[5] = w[5].clamp(0.01, 4)
w[6] = w[6].clamp(0.01, 4)
w[7] = w[7].clamp(0, 0.75)
w[5] = w[5].clamp(0.001, 4)
w[6] = w[6].clamp(0.001, 4)
w[7] = w[7].clamp(0.001, 0.75)
w[8] = w[8].clamp(0, 4.5)
w[9] = w[9].clamp(0, 0.8)
w[10] = w[10].clamp(0.01, 3.5)
w[11] = w[11].clamp(0.1, 5)
w[12] = w[12].clamp(0.01, 0.25)
w[13] = w[13].clamp(0.01, 0.9)
w[14] = w[14].clamp(0.01, 4)
w[10] = w[10].clamp(0.001, 3.5)
w[11] = w[11].clamp(0.001, 5)
w[12] = w[12].clamp(0.001, 0.25)
w[13] = w[13].clamp(0.001, 0.9)
w[14] = w[14].clamp(0, 4)
w[15] = w[15].clamp(0, 1)
w[16] = w[16].clamp(1, 6)
w[17] = w[17].clamp(0, 2)
Expand Down Expand Up @@ -2075,22 +2081,3 @@ def wrap_short_term_ratings(r_history, t_history):
else:
result.pop()
return "".join(result)


if __name__ == "__main__":
model = FSRS(DEFAULT_PARAMETER)
stability = torch.tensor([5.0] * 4)
difficulty = torch.tensor([1.0, 2.0, 3.0, 4.0])
retention = torch.tensor([0.9, 0.8, 0.7, 0.6])
rating = torch.tensor([1, 2, 3, 4])
state = torch.stack([stability, difficulty]).unsqueeze(0)
s_recall = model.stability_after_success(state, retention, rating)
print(s_recall)
s_forget = model.stability_after_failure(state, retention)
print(s_forget)

retentions = torch.tensor([0.1, 0.2, 0.3, 0.4])
labels = torch.tensor([0.0, 1.0, 0.0, 1.0])
loss_fn = nn.BCELoss()
loss = loss_fn(retentions, labels)
print(loss)
6 changes: 5 additions & 1 deletion src/fsrs_optimizer/fsrs_simulator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -127,8 +127,12 @@ def init_d_with_short_term(rating):
new_d = init_d(rating) - w[6] * rating_offset
return np.clip(new_d, 1, 10)

def linear_damping(delta_d, old_d):
return delta_d * (10 - old_d) / 9

def next_d(d, rating):
new_d = d - w[6] * (rating - 3)
delta_d = -w[6] * (rating - 3)
new_d = d + linear_damping(delta_d, d)
new_d = mean_reversion(init_d(4), new_d)
return np.clip(new_d, 1, 10)

Expand Down
137 changes: 137 additions & 0 deletions tests/model_test.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,137 @@
from src.fsrs_optimizer import *


class Test_Model:
def test_next_stability(self):
model = FSRS(DEFAULT_PARAMETER)
stability = torch.tensor([5.0] * 4)
difficulty = torch.tensor([1.0, 2.0, 3.0, 4.0])
retention = torch.tensor([0.9, 0.8, 0.7, 0.6])
rating = torch.tensor([1, 2, 3, 4])
state = torch.stack([stability, difficulty]).unsqueeze(0)
s_recall = model.stability_after_success(state, retention, rating)
assert torch.allclose(
s_recall, torch.tensor([25.7761, 14.1219, 60.4044, 208.9760]), atol=1e-4
)
s_forget = model.stability_after_failure(state, retention)
assert torch.allclose(
s_forget, torch.tensor([1.7029, 1.9799, 2.3760, 2.8885]), atol=1e-4
)
s_short_term = model.stability_short_term(state, rating)
assert torch.allclose(
s_short_term, torch.tensor([2.5051, 4.1992, 7.0389, 11.7988]), atol=1e-4
)

def test_next_difficulty(self):
model = FSRS(DEFAULT_PARAMETER)
stability = torch.tensor([5.0] * 4)
difficulty = torch.tensor([5.0] * 4)
rating = torch.tensor([1, 2, 3, 4])
state = torch.stack([stability, difficulty]).unsqueeze(0)
d_recall = model.next_d(state, rating)
assert torch.allclose(
d_recall,
torch.tensor([6.6070, 5.7994, 4.9918, 4.1842]),
atol=1e-4,
)

def test_power_forgetting_curve(self):
delta_t = torch.tensor([0, 1, 2, 3, 4, 5])
stability = torch.tensor([1, 2, 3, 4, 4, 2])
retention = power_forgetting_curve(delta_t, stability)
assert torch.allclose(
retention,
torch.tensor([1.0, 0.946059, 0.9299294, 0.9221679, 0.90000004, 0.79394597]),
atol=1e-4,
)

def test_forward(self):
model = FSRS(DEFAULT_PARAMETER)
delta_ts = torch.tensor(
[
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[1.0, 1.0, 1.0, 1.0, 2.0, 2.0],
]
)
ratings = torch.tensor(
[
[1.0, 2.0, 3.0, 4.0, 1.0, 2.0],
[1.0, 2.0, 3.0, 4.0, 1.0, 2.0],
]
)
inputs = torch.stack([delta_ts, ratings], dim=2)
_, state = model.forward(inputs)
stability = state[:, 0]
difficulty = state[:, 1]
assert torch.allclose(
stability,
torch.tensor([0.2619, 1.7073, 5.8691, 25.0123, 0.3403, 2.1482]),
atol=1e-4,
)
assert torch.allclose(
difficulty,
torch.tensor([8.0827, 7.0405, 5.2729, 2.1301, 8.0827, 7.0405]),
atol=1e-4,
)

def test_loss_and_grad(self):
model = FSRS(DEFAULT_PARAMETER)
loss_fn = nn.BCELoss(reduction="none")
t_histories = torch.tensor(
[
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0],
[0.0, 1.0, 1.0, 3.0],
[1.0, 3.0, 3.0, 5.0],
[3.0, 6.0, 6.0, 12.0],
]
)
r_histories = torch.tensor(
[
[1.0, 2.0, 3.0, 4.0],
[3.0, 4.0, 2.0, 4.0],
[1.0, 4.0, 4.0, 3.0],
[4.0, 3.0, 3.0, 3.0],
[3.0, 1.0, 3.0, 3.0],
[2.0, 3.0, 3.0, 4.0],
]
)
delta_ts = torch.tensor([4.0, 11.0, 12.0, 23.0])
labels = torch.tensor([1, 1, 1, 0], dtype=torch.float32, requires_grad=False)
inputs = torch.stack([t_histories, r_histories], dim=2)
seq_lens = inputs.shape[0]
real_batch_size = inputs.shape[1]
outputs, _ = model.forward(inputs)
stabilities = outputs[seq_lens - 1, torch.arange(real_batch_size), 0]
retentions = power_forgetting_curve(delta_ts, stabilities)
loss = loss_fn(retentions, labels).sum()
assert round(loss.item(), 4) == 4.4467
loss.backward()
assert torch.allclose(
model.w.grad,
torch.tensor(
[
-0.0583,
-0.0068,
-0.0026,
0.0105,
-0.0513,
1.3643,
0.0837,
-0.9502,
0.5345,
-2.8929,
0.5142,
-0.0131,
0.0419,
-0.1183,
-0.0009,
-0.1445,
0.2024,
0.2141,
0.0323,
]
),
atol=1e-4,
)
4 changes: 2 additions & 2 deletions tests/simulator_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@ def test_simulate(self):
memorized_cnt_per_day,
cost_per_day,
) = simulate(w=DEFAULT_PARAMETER, request_retention=0.9)
assert memorized_cnt_per_day[-1] == 5918.574208243532
assert memorized_cnt_per_day[-1] == 5875.025236206539

def test_optimal_retention(self):
default_params = {
Expand All @@ -24,4 +24,4 @@ def test_optimal_retention(self):
"loss_aversion": 2.5,
}
r = optimal_retention(**default_params)
assert r == 0.8346739534878145
assert r == 0.8263932

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