clippy::nursery

src/batch_shuffle.rs

@@ -13,7 +13,7 @@ use std::{
 
 use crate::{dataset::FSRSDataset, FSRSItem};
 
-pub(crate) struct BatchShuffledDataset<I> {
+pub struct BatchShuffledDataset<I> {
     dataset: Arc<FSRSDataset>,
     indices: Vec<usize>,
     input: PhantomData<I>,
@@ -130,11 +130,10 @@ where
         // When starting a new iteration, we first check if the dataloader was created with an rng,
         // implying that we should shuffle the dataset beforehand, while advancing the current
         // rng to ensure that each new iteration shuffles the dataset differently.
-        let mut rng = self.rng.lock().unwrap();
         let dataset = Arc::new(BatchShuffledDataset::with_seed(
             self.dataset.clone(),
             self.batch_size,
-            rng.sample(Standard),
+            self.rng.lock().unwrap().sample(Standard),
         ));
         Box::new(BatchShuffledDataloaderIterator::new(
             self.strategy.new_like(),
@@ -209,7 +208,6 @@ impl<I, O> DataLoaderIterator<O> for BatchShuffledDataloaderIterator<I, O> {
 
 /// A builder for data loaders.
 pub struct BatchShuffledDataLoaderBuilder<I, O> {
-    strategy: Option<Box<dyn BatchStrategy<I>>>,
     batcher: Arc<dyn Batcher<I, O>>,
 }
 
@@ -234,25 +232,9 @@ where
     {
         Self {
             batcher: Arc::new(batcher),
-            strategy: None,
         }
     }
 
-    /// Sets the batch size to a fix number.The [fix batch strategy](FixBatchStrategy)
-    /// will be used.
-    ///
-    /// # Arguments
-    ///
-    /// * `batch_size` - The batch size.
-    ///
-    /// # Returns
-    ///
-    /// The data loader builder.
-    pub fn batch_size(mut self, batch_size: usize) -> Self {
-        self.strategy = Some(Box::new(FixBatchStrategy::new(batch_size)));
-        self
-    }
-
     /// Builds the data loader.
     ///
     /// # Arguments
@@ -271,10 +253,7 @@ where
         let dataset = Arc::new(dataset);
 
         let rng = StdRng::seed_from_u64(seed);
-        let strategy = match self.strategy {
-            Some(strategy) => strategy,
-            None => Box::new(FixBatchStrategy::new(1)),
-        };
+        let strategy = Box::new(FixBatchStrategy::new(batch_size));
 
         Arc::new(BatchShuffledDataLoader::new(
             strategy,

src/cosine_annealing.rs

@@ -1,6 +1,6 @@
 use burn::{lr_scheduler::LrScheduler, tensor::backend::Backend, LearningRate};
 #[derive(Clone, Debug)]
-pub(crate) struct CosineAnnealingLR {
+pub struct CosineAnnealingLR {
     t_max: f64,
     eta_min: f64,
     init_lr: LearningRate,

src/dataset.rs

@@ -50,7 +50,7 @@ impl FSRSItem {
     }
 }
 
-pub(crate) struct FSRSBatcher<B: Backend> {
+pub struct FSRSBatcher<B: Backend> {
     device: B::Device,
 }
 
@@ -61,7 +61,7 @@ impl<B: Backend> FSRSBatcher<B> {
 }
 
 #[derive(Debug, Clone)]
-pub(crate) struct FSRSBatch<B: Backend> {
+pub struct FSRSBatch<B: Backend> {
     pub t_historys: Tensor<B, 2, Float>,
     pub r_historys: Tensor<B, 2, Float>,
     pub delta_ts: Tensor<B, 1, Float>,
@@ -133,7 +133,7 @@ impl<B: Backend> Batcher<FSRSItem, FSRSBatch<B>> for FSRSBatcher<B> {
     }
 }
 
-pub(crate) struct FSRSDataset {
+pub struct FSRSDataset {
     items: Vec<FSRSItem>,
 }
 

src/inference.rs

@@ -13,10 +13,10 @@ use crate::model::Model;
 use crate::training::BCELoss;
 use crate::{FSRSError, FSRSItem};
 use burn::tensor::ElementConversion;
-pub(crate) const DECAY: f64 = -0.5;
+pub const DECAY: f64 = -0.5;
 /// (9/10) ^ (1 / DECAY) - 1
-pub(crate) const FACTOR: f64 = 19f64 / 81f64;
-pub(crate) const S_MIN: f32 = 0.01;
+pub const FACTOR: f64 = 19f64 / 81f64;
+pub const S_MIN: f32 = 0.01;
 /// This is a slice for efficiency, but should always be 17 in length.
 pub type Parameters = [f32];
 use itertools::izip;
@@ -292,7 +292,7 @@ impl<B: Backend> FSRS<B> {
             total: items.len(),
         };
         let model_self = self.model();
-        let fsrs_other = FSRS::<B>::new_with_backend(Some(parameters), self.device())?;
+        let fsrs_other = Self::new_with_backend(Some(parameters), self.device())?;
         let model_other = fsrs_other.model();
         for chunk in items.chunks(512) {
             let batch = batcher.batch(chunk.to_vec());

src/model.rs

@@ -16,7 +16,7 @@ pub struct Model<B: Backend> {
     pub config: ModelConfig,
 }
 
-pub(crate) trait Get<B: Backend, const N: usize> {
+pub trait Get<B: Backend, const N: usize> {
     fn get(&self, n: usize) -> Tensor<B, N>;
 }
 
@@ -177,7 +177,7 @@ impl<B: Backend> Model<B> {
 }
 
 #[derive(Debug, Clone)]
-pub(crate) struct MemoryStateTensors<B: Backend> {
+pub struct MemoryStateTensors<B: Backend> {
     pub stability: Tensor<B, 1>,
     pub difficulty: Tensor<B, 1>,
 }
@@ -240,7 +240,7 @@ impl<B: Backend> FSRS<B> {
     }
 }
 
-pub(crate) fn parameters_to_model<B: Backend>(parameters: &Parameters) -> Model<B> {
+pub fn parameters_to_model<B: Backend>(parameters: &Parameters) -> Model<B> {
     let config = ModelConfig::default();
     let mut model = Model::new(config);
     model.w = Param::from(Tensor::from_floats(

src/optimal_retention.rs

@@ -174,7 +174,7 @@ pub fn simulate(
         let mut retrievability = Array1::zeros(deck_size); // Create an array for retrievability
 
         fn power_forgetting_curve(t: f64, s: f64) -> f64 {
-            (t / s * FACTOR + 1.0).powf(DECAY)
+            (t / s).mul_add(FACTOR, 1.0).powf(DECAY)
         }
 
         // Calculate retrievability for entries where has_learned is true
@@ -505,7 +505,7 @@ impl<B: Backend> FSRS<B> {
             let tol2 = 2.0 * tol1;
             let xmid = 0.5 * (a + b);
             // check for convergence
-            if (x - xmid).abs() < (tol2 - 0.5 * (b - a)) {
+            if (x - xmid).abs() < 0.5f64.mul_add(-(b - a), tol2) {
                 break;
             }
             if deltax.abs() <= tol1 {

src/pre_training.rs

@@ -106,7 +106,7 @@ fn loss(
     logloss + l1
 }
 
-pub(crate) const INIT_S_MAX: f32 = 100.0;
+pub const INIT_S_MAX: f32 = 100.0;
 
 fn search_parameters(
     mut pretrainset: HashMap<FirstRating, Vec<AverageRecall>>,

src/training.rs

@@ -112,7 +112,7 @@ impl CombinedProgressState {
         self.splits.iter().map(|s| s.total()).sum()
     }
 
-    pub fn finished(&self) -> bool {
+    pub const fn finished(&self) -> bool {
         self.finished
     }
 }
@@ -166,7 +166,7 @@ impl MetricsRenderer for ProgressCollector {
 }
 
 #[derive(Config)]
-pub(crate) struct TrainingConfig {
+pub struct TrainingConfig {
     pub model: ModelConfig,
     pub optimizer: AdamConfig,
     #[config(default = 5)]
@@ -300,7 +300,7 @@ impl<B: Backend> FSRS<B> {
     }
 
     pub fn benchmark(&self, train_set: Vec<FSRSItem>, test_set: Vec<FSRSItem>) -> Vec<f32> {
-        let average_recall = calculate_average_recall(&train_set.clone());
+        let average_recall = calculate_average_recall(&train_set);
         let (pre_train_set, next_train_set) = train_set
             .into_iter()
             .partition(|item| item.reviews.len() == 2);
@@ -330,11 +330,13 @@ fn train<B: AutodiffBackend>(
     // Training data
     let iterations = (trainset.len() / config.batch_size + 1) * config.num_epochs;
     let batcher_train = FSRSBatcher::<B>::new(device.clone());
-    let dataloader_train = BatchShuffledDataLoaderBuilder::new(batcher_train)
-        .batch_size(config.batch_size)
-        .build(FSRSDataset::from(trainset), config.batch_size, config.seed);
+    let dataloader_train = BatchShuffledDataLoaderBuilder::new(batcher_train).build(
+        FSRSDataset::from(trainset),
+        config.batch_size,
+        config.seed,
+    );
 
-    let batcher_valid = FSRSBatcher::new(device.clone());
+    let batcher_valid = FSRSBatcher::new(device);
     let dataloader_valid = DataLoaderBuilder::new(batcher_valid)
         .batch_size(config.batch_size)
         .build(FSRSDataset::from(testset.clone()));

src/weight_clipper.rs

@@ -4,15 +4,15 @@ use crate::{
 };
 use burn::tensor::{backend::Backend, Data, Tensor};
 
-pub(crate) fn weight_clipper<B: Backend>(parameters: Tensor<B, 1>) -> Tensor<B, 1> {
+pub fn weight_clipper<B: Backend>(parameters: Tensor<B, 1>) -> Tensor<B, 1> {
     let val = clip_parameters(&parameters.to_data().convert().value);
     Tensor::from_data(
         Data::new(val, parameters.shape()).convert(),
         &B::Device::default(),
     )
 }
 
-pub(crate) fn clip_parameters(parameters: &Parameters) -> Vec<f32> {
+pub fn clip_parameters(parameters: &Parameters) -> Vec<f32> {
     // https://regex101.com/r/21mXNI/1
     const CLAMPS: [(f32, f32); 17] = [
         (S_MIN, INIT_S_MAX),