Replace "weight" with "parameter".py (#113)

* Update fsrs_optimizer.py * Update simulator_test.py
open-spaced-repetition · May 22, 2024 · 91b9fc7 · 91b9fc7
1 parent e8b71f1
commit 91b9fc7
Show file tree

Hide file tree

Showing 2 changed files with 9 additions and 9 deletions.
diff --git a/src/fsrs_optimizer/fsrs_optimizer.py b/src/fsrs_optimizer/fsrs_optimizer.py
@@ -47,7 +47,7 @@
 Review = 2
 Relearning = 3
 
-DEFAULT_WEIGHT = [
+DEFAULT_PARAMETER = [
     0.4872,
     1.4003,
     3.7145,
@@ -145,7 +145,7 @@ def mean_reversion(self, init: Tensor, current: Tensor) -> Tensor:
         return self.w[7] * init + (1 - self.w[7]) * current
 
 
-class WeightClipper:
+class ParameterClipper:
     def __init__(self, frequency: int = 1):
         self.frequency = frequency
 
@@ -255,7 +255,7 @@ def __init__(
     ) -> None:
         self.model = FSRS(init_w)
         self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)
-        self.clipper = WeightClipper()
+        self.clipper = ParameterClipper()
         self.batch_size = batch_size
         self.build_dataset(train_set, test_set)
         self.n_epoch = n_epoch
@@ -804,7 +804,7 @@ def cal_stability(group: pd.DataFrame) -> pd.DataFrame:
 
     def define_model(self):
         """Step 3"""
-        self.init_w = DEFAULT_WEIGHT.copy()
+        self.init_w = DEFAULT_PARAMETER.copy()
         """
         For details about the parameters, please see: 
         https://github.com/open-spaced-repetition/fsrs4anki/wiki/The-Algorithm
@@ -831,7 +831,7 @@ def pretrain(self, dataset=None, verbose=True):
         rating_count = {}
         average_recall = self.dataset["y"].mean()
         plots = []
-        r_s0_default = {str(i): DEFAULT_WEIGHT[i - 1] for i in range(1, 5)}
+        r_s0_default = {str(i): DEFAULT_PARAMETER[i - 1] for i in range(1, 5)}
 
         for first_rating in ("1", "2", "3", "4"):
             group = self.S0_dataset_group[
@@ -1322,7 +1322,7 @@ def moving_average(data, window_size=365 // 20):
         return (fig1, fig2, fig3, fig4, fig5, fig6)
 
     def evaluate(self, save_to_file=True):
-        my_collection = Collection(DEFAULT_WEIGHT)
+        my_collection = Collection(DEFAULT_PARAMETER)
         stabilities, difficulties = my_collection.batch_predict(self.dataset)
         self.dataset["stability"] = stabilities
         self.dataset["difficulty"] = difficulties
@@ -1853,7 +1853,7 @@ def rmse_matrix(df):
 
 
 if __name__ == "__main__":
-    model = FSRS(DEFAULT_WEIGHT)
+    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])

diff --git a/tests/simulator_test.py b/tests/simulator_test.py
@@ -4,13 +4,13 @@
 class Test_Simulator:
     def test_simulate(self):
         (_, _, _, memorized_cnt_per_day, _) = simulate(
-            w=DEFAULT_WEIGHT, forget_cost=125
+            w=DEFAULT_PARAMETER, forget_cost=125
         )
         assert memorized_cnt_per_day[-1] == 3145.3779679589484
 
     def test_optimal_retention(self):
         default_params = {
-            "w": DEFAULT_WEIGHT,
+            "w": DEFAULT_PARAMETER,
             "deck_size": 10000,
             "learn_span": 1000,
             "max_cost_perday": math.inf,