chore: release v0.3.0

.github/workflows/ci.yml

@@ -42,9 +42,7 @@ jobs:
       matrix:
         features:
           - ""
-          - "serde"
           - "async"
-          - "serde,async"
     steps:
       - uses: actions/checkout@v6
       - name: Install Rust
@@ -197,6 +195,27 @@ jobs:
       - name: Run tests
         run: cargo test --all-features
 
+  cross-targets:
+    name: ${{ matrix.target }}
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        target:
+          - aarch64-unknown-linux-gnu
+          - i686-unknown-linux-gnu
+          - powerpc64le-unknown-linux-gnu
+          - s390x-unknown-linux-gnu
+    steps:
+      - uses: actions/checkout@v6
+      - name: Install Rust
+        run: |
+          rustup override set stable
+          rustup update stable
+          rustup target add ${{ matrix.target }}
+      - uses: Swatinem/rust-cache@v2
+      - name: Check compilation
+        run: cargo check --all-features --target ${{ matrix.target }}
 
   typos:
     name: Typos
@@ -230,6 +249,7 @@ jobs:
       - semver
       - minimal-versions
       - cross-platform
+      - cross-targets
       - typos
     steps:
       - uses: actions/checkout@v6

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "optimizer"
-version = "0.1.1"
+version = "0.3.0"
 edition = "2024"
 rust-version = "1.88"
 license = "MIT"
@@ -13,15 +13,11 @@ repository = "https://github.com/raimannma/rust-optimizer"
 rand = "0.9"
 thiserror = "2"
 parking_lot = "0.12"
-ordered-float = "5"
-serde = { version = "1", features = ["derive"], optional = true }
 tokio = { version = "1", features = ["sync", "rt-multi-thread"], optional = true }
 
 [features]
 default = []
-serde = ["dep:serde", "ordered-float/serde"]
 async = ["dep:tokio"]
 
 [dev-dependencies]
-serde_json = "1"
 tokio = { version = "1", features = ["rt-multi-thread", "macros"] }

README.md

@@ -12,7 +12,6 @@ A Rust library for black-box optimization using Tree-Parzen Estimator (TPE).
 - Float, integer, and categorical parameter types
 - Log-scale and stepped parameter sampling
 - Sync and async optimization with parallel trial evaluation
-- Serialization support for saving/loading study state
 
 ## Quick Start
 
@@ -35,7 +34,6 @@ println!("Best value: {} at x={:?}", best.value, best.params);
 
 ## Feature Flags
 
-- `serde` - Enable serialization/deserialization of studies and trials
 - `async` - Enable async optimization methods (requires tokio)
 
 ## Documentation

src/distribution.rs

@@ -1,11 +1,7 @@
 //! Parameter distribution types.
 
-#[cfg(feature = "serde")]
-use serde::{Deserialize, Serialize};
-
 /// Distribution for floating-point parameters.
 #[derive(Clone, Debug, PartialEq)]
-#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub struct FloatDistribution {
     /// Lower bound (inclusive).
     pub low: f64,
@@ -19,7 +15,6 @@ pub struct FloatDistribution {
 
 /// Distribution for integer parameters.
 #[derive(Clone, Debug, PartialEq)]
-#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub struct IntDistribution {
     /// Lower bound (inclusive).
     pub low: i64,
@@ -33,7 +28,6 @@ pub struct IntDistribution {
 
 /// Distribution for categorical parameters.
 #[derive(Clone, Debug, PartialEq)]
-#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub struct CategoricalDistribution {
     /// Number of choices available.
     pub n_choices: usize,
@@ -41,7 +35,6 @@ pub struct CategoricalDistribution {
 
 /// Enum wrapping all parameter distribution types.
 #[derive(Clone, Debug, PartialEq)]
-#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub enum Distribution {
     /// A floating-point distribution.
     Float(FloatDistribution),

src/error.rs

@@ -38,7 +38,28 @@ pub enum TpeError {
     /// Returned when requesting the best trial but no trials have completed.
     #[error("no completed trials available")]
     NoCompletedTrials,
+
+    /// Returned when gamma is not in the valid range (0.0, 1.0).
+    #[error("invalid gamma: {0} must be in (0.0, 1.0)")]
+    InvalidGamma(f64),
+
+    /// Returned when bandwidth is not positive.
+    #[error("invalid bandwidth: {0} must be positive")]
+    InvalidBandwidth(f64),
+
+    /// Returned when KDE is created with empty samples.
+    #[error("KDE requires at least one sample")]
+    EmptySamples,
+
+    /// Returned when an internal invariant is violated.
+    #[error("internal error: {0}")]
+    Internal(&'static str),
+
+    /// Returned when an async task fails.
+    #[cfg(feature = "async")]
+    #[error("async task error: {0}")]
+    TaskError(String),
 }
 
 /// A specialized Result type for TPE operations.
-pub type Result<T> = std::result::Result<T, TpeError>;
+pub type Result<T> = core::result::Result<T, TpeError>;

src/kde.rs

@@ -5,6 +5,8 @@
 
 use rand::Rng;
 
+use crate::error::{Result, TpeError};
+
 /// A Gaussian kernel density estimator for continuous distributions.
 ///
 /// KDE estimates a probability density function from a set of samples by
@@ -28,7 +30,7 @@ use rand::Rng;
 /// let sample = kde.sample(&mut rng);
 /// ```
 #[derive(Clone, Debug)]
-pub struct KernelDensityEstimator {
+pub(crate) struct KernelDensityEstimator {
     /// The sample points used to construct the KDE.
     samples: Vec<f64>,
     /// The bandwidth (standard deviation) of the Gaussian kernels.
@@ -38,37 +40,45 @@ pub struct KernelDensityEstimator {
 impl KernelDensityEstimator {
     /// Creates a new KDE with automatic bandwidth selection using Scott's rule.
     ///
-    /// Scott's rule sets bandwidth = n^(-1/5) * std_dev, which works well
+    /// Scott's rule sets bandwidth = n^(-1/5) * `std_dev`, which works well
     /// for unimodal distributions close to normal.
     ///
-    /// # Panics
+    /// # Errors
     ///
-    /// Panics if `samples` is empty.
+    /// Returns `TpeError::EmptySamples` if `samples` is empty.
-    pub fn new(samples: Vec<f64>) -> Self {
+    pub(crate) fn new(samples: Vec<f64>) -> Result<Self> {
-        assert!(!samples.is_empty(), "KDE requires at least one sample");
+        if samples.is_empty() {
+            return Err(TpeError::EmptySamples);
+        }
 
         let bandwidth = Self::scotts_rule(&samples);
-        Self { samples, bandwidth }
+        Ok(Self { samples, bandwidth })
     }
 
     /// Creates a new KDE with a specified bandwidth.
     ///
     /// Use this when you want explicit control over the smoothing parameter.
     ///
-    /// # Panics
+    /// # Errors
     ///
-    /// Panics if `samples` is empty or `bandwidth` is not positive.
+    /// Returns `TpeError::EmptySamples` if `samples` is empty.
-    pub(crate) fn with_bandwidth(samples: Vec<f64>, bandwidth: f64) -> Self {
+    /// Returns `TpeError::InvalidBandwidth` if `bandwidth` is not positive.
-        assert!(!samples.is_empty(), "KDE requires at least one sample");
+    pub(crate) fn with_bandwidth(samples: Vec<f64>, bandwidth: f64) -> Result<Self> {
-        assert!(bandwidth > 0.0, "Bandwidth must be positive");
+        if samples.is_empty() {
+            return Err(TpeError::EmptySamples);
+        }
+        if bandwidth <= 0.0 {
+            return Err(TpeError::InvalidBandwidth(bandwidth));
+        }
 
-        Self { samples, bandwidth }
+        Ok(Self { samples, bandwidth })
     }
 
     /// Computes bandwidth using Scott's rule.
     ///
     /// Scott's rule: h = n^(-1/5) * sigma
     /// where sigma is the sample standard deviation.
+    #[allow(clippy::cast_precision_loss)]
     fn scotts_rule(samples: &[f64]) -> f64 {
         let n = samples.len() as f64;
         let std_dev = Self::sample_std_dev(samples);
@@ -83,6 +93,7 @@ impl KernelDensityEstimator {
     }
 
     /// Computes the sample standard deviation.
+    #[allow(clippy::cast_precision_loss)]
     fn sample_std_dev(samples: &[f64]) -> f64 {
         let n = samples.len() as f64;
         let mean = samples.iter().sum::<f64>() / n;
@@ -95,13 +106,14 @@ impl KernelDensityEstimator {
     /// The density is computed as the average of Gaussian kernels centered
     /// at each sample point:
     ///
-    /// f(x) = (1/n) * sum_i K((x - x_i) / h)
+    /// f(x) = (1/n) * `sum_i` K((x - `x_i`) / h)
     ///
     /// where K is the standard Gaussian kernel and h is the bandwidth.
-    pub fn pdf(&self, x: f64) -> f64 {
+    #[allow(clippy::cast_precision_loss)]
+    pub(crate) fn pdf(&self, x: f64) -> f64 {
         let n = self.samples.len() as f64;
         let inv_bandwidth = 1.0 / self.bandwidth;
-        let normalization = inv_bandwidth / (2.0 * std::f64::consts::PI).sqrt();
+        let normalization = inv_bandwidth / (2.0 * core::f64::consts::PI).sqrt();
 
         let density: f64 = self
             .samples
@@ -120,7 +132,7 @@ impl KernelDensityEstimator {
     /// Sampling works by:
     /// 1. Uniformly selecting one of the kernel centers (samples)
     /// 2. Adding Gaussian noise with the bandwidth as standard deviation
-    pub fn sample<R: Rng>(&self, rng: &mut R) -> f64 {
+    pub(crate) fn sample<R: Rng>(&self, rng: &mut R) -> f64 {
         // Select a random sample to center the kernel on
         let idx = rng.random_range(0..self.samples.len());
         let center = self.samples[idx];
@@ -130,7 +142,7 @@ impl KernelDensityEstimator {
         let u1: f64 = rng.random();
         let u2: f64 = rng.random();
 
-        let z = (-2.0 * u1.ln()).sqrt() * (2.0 * std::f64::consts::PI * u2).cos();
+        let z = (-2.0 * u1.ln()).sqrt() * (2.0 * core::f64::consts::PI * u2).cos();
         center + z * self.bandwidth
     }
 
@@ -148,7 +160,7 @@ mod tests {
     #[test]
     fn test_kde_pdf_basic() {
         let samples = vec![0.0, 1.0, 2.0];
-        let kde = KernelDensityEstimator::new(samples);
+        let kde = KernelDensityEstimator::new(samples).unwrap();
 
         // Density should be positive everywhere
         assert!(kde.pdf(0.0) > 0.0);
@@ -164,17 +176,17 @@ mod tests {
     #[test]
     fn test_kde_pdf_integrates_to_one() {
         let samples = vec![0.0, 1.0, 2.0, 3.0, 4.0];
-        let kde = KernelDensityEstimator::new(samples);
+        let kde = KernelDensityEstimator::new(samples).unwrap();
 
         // Numerical integration over a wide range
         let n_points = 10000;
         let low = -10.0;
         let high = 15.0;
-        let dx = (high - low) / n_points as f64;
+        let dx = (high - low) / f64::from(n_points);
 
         let integral: f64 = (0..n_points)
             .map(|i| {
-                let x = low + (i as f64 + 0.5) * dx;
+                let x = low + (f64::from(i) + 0.5) * dx;
                 kde.pdf(x) * dx
             })
             .sum();
@@ -189,16 +201,16 @@ mod tests {
     #[test]
     fn test_kde_with_bandwidth() {
         let samples = vec![0.0, 1.0, 2.0];
-        let kde = KernelDensityEstimator::with_bandwidth(samples, 0.5);
+        let kde = KernelDensityEstimator::with_bandwidth(samples, 0.5).unwrap();
 
-        assert_eq!(kde.bandwidth(), 0.5);
+        assert!((kde.bandwidth() - 0.5).abs() < f64::EPSILON);
         assert!(kde.pdf(1.0) > 0.0);
     }
 
     #[test]
     fn test_kde_sample_in_reasonable_range() {
         let samples = vec![0.0, 1.0, 2.0, 3.0, 4.0];
-        let kde = KernelDensityEstimator::new(samples);
+        let kde = KernelDensityEstimator::new(samples).unwrap();
         let mut rng = rand::rng();
 
         // Samples should generally be in a reasonable range around the data
@@ -213,7 +225,7 @@ mod tests {
     #[test]
     fn test_kde_single_sample() {
         let samples = vec![5.0];
-        let kde = KernelDensityEstimator::new(samples);
+        let kde = KernelDensityEstimator::new(samples).unwrap();
 
         // Should have positive density near the sample
         assert!(kde.pdf(5.0) > 0.0);
@@ -223,7 +235,7 @@ mod tests {
     #[test]
     fn test_kde_identical_samples() {
         let samples = vec![3.0, 3.0, 3.0, 3.0];
-        let kde = KernelDensityEstimator::new(samples);
+        let kde = KernelDensityEstimator::new(samples).unwrap();
 
         // Should handle degenerate case with identical samples
         assert!(kde.bandwidth() > 0.0);
@@ -233,7 +245,7 @@ mod tests {
     #[test]
     fn test_scotts_rule_bandwidth() {
         let samples = vec![0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0];
-        let kde = KernelDensityEstimator::new(samples);
+        let kde = KernelDensityEstimator::new(samples).unwrap();
 
         // n = 10, n^(-1/5) ≈ 0.631
         // std_dev ≈ 2.87
@@ -246,23 +258,23 @@ mod tests {
     }
 
     #[test]
-    #[should_panic(expected = "KDE requires at least one sample")]
     fn test_kde_empty_samples() {
         let samples: Vec<f64> = vec![];
-        KernelDensityEstimator::new(samples);
+        let result = KernelDensityEstimator::new(samples);
+        assert!(matches!(result, Err(TpeError::EmptySamples)));
     }
 
     #[test]
-    #[should_panic(expected = "Bandwidth must be positive")]
     fn test_kde_zero_bandwidth() {
         let samples = vec![1.0, 2.0, 3.0];
-        KernelDensityEstimator::with_bandwidth(samples, 0.0);
+        let result = KernelDensityEstimator::with_bandwidth(samples, 0.0);
+        assert!(matches!(result, Err(TpeError::InvalidBandwidth(_))));
     }
 
     #[test]
-    #[should_panic(expected = "Bandwidth must be positive")]
     fn test_kde_negative_bandwidth() {
         let samples = vec![1.0, 2.0, 3.0];
-        KernelDensityEstimator::with_bandwidth(samples, -1.0);
+        let result = KernelDensityEstimator::with_bandwidth(samples, -1.0);
+        assert!(matches!(result, Err(TpeError::InvalidBandwidth(_))));
     }
 }

src/lib.rs

@@ -1,3 +1,14 @@
+#![forbid(unsafe_code)]
+#![deny(clippy::all)]
+#![deny(unreachable_pub)]
+#![deny(clippy::correctness)]
+#![deny(clippy::suspicious)]
+#![deny(clippy::style)]
+#![deny(clippy::complexity)]
+#![deny(clippy::perf)]
+#![deny(clippy::pedantic)]
+#![deny(clippy::std_instead_of_core)]
+
 //! A Tree-Parzen Estimator (TPE) library for black-box optimization.
 //!
 //! This library provides an Optuna-like API for hyperparameter optimization
@@ -7,15 +18,15 @@
 //! - Log-scale and stepped parameter sampling
 //! - Synchronous and async optimization
 //! - Parallel trial evaluation with bounded concurrency
-//! - Serialization for saving/loading study state
 //!
 //! # Quick Start
 //!
 //! ```
-//! use optimizer::{Direction, Study, TpeSampler};
+//! use optimizer::sampler::tpe::TpeSampler;
+//! use optimizer::{Direction, Study};
 //!
 //! // Create a study with TPE sampler
-//! let sampler = TpeSampler::builder().seed(42).build();
+//! let sampler = TpeSampler::builder().seed(42).build().unwrap();
 //! let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 //!
 //! // Optimize x^2 for 20 trials
@@ -36,7 +47,9 @@
 //! A [`Study`] manages optimization trials. Create one with an optimization direction:
 //!
 //! ```
-//! use optimizer::{Direction, RandomSampler, Study, TpeSampler};
+//! use optimizer::sampler::random::RandomSampler;
+//! use optimizer::sampler::tpe::TpeSampler;
+//! use optimizer::{Direction, Study};
 //!
 //! // Minimize with default random sampler
 //! let study: Study<f64> = Study::new(Direction::Minimize);
@@ -80,17 +93,18 @@
 //!
 //! # Configuring TPE
 //!
-//! The [`TpeSampler`] can be configured using the builder pattern:
+//! The [`sampler::tpe::TpeSampler`] can be configured using the builder pattern:
 //!
 //! ```
-//! use optimizer::TpeSampler;
+//! use optimizer::sampler::tpe::TpeSampler;
 //!
 //! let sampler = TpeSampler::builder()
 //!     .gamma(0.15)           // Quantile for good/bad split
 //!     .n_startup_trials(20)  // Random trials before TPE
 //!     .n_ei_candidates(32)   // Candidates to evaluate
 //!     .seed(42)              // Reproducibility
-//!     .build();
+//!     .build()
+//!     .unwrap();
 //! ```
 //!
 //! # Async and Parallel Optimization
@@ -113,39 +127,21 @@
 //! }).await?;
 //! ```
 //!
-//! # Serialization
-//!
-//! With the `serde` feature enabled, studies can be serialized:
-//!
-//! ```ignore
-//! use optimizer::{Study, Direction, TpeSampler};
-//!
-//! // Save study state
-//! let study: Study<f64> = Study::new(Direction::Minimize);
-//! let json = serde_json::to_string(&study)?;
-//!
-//! // Load and continue
-//! let mut study: Study<f64> = serde_json::from_str(&json)?;
-//! study.set_sampler(TpeSampler::new());  // Restore sampler
-//! study.optimize_with_sampler(10, |trial| { /* ... */ }).unwrap();
-//! ```
-//!
 //! # Feature Flags
 //!
-//! - `serde`: Enable serialization/deserialization of studies and trials
 //! - `async`: Enable async optimization methods (requires tokio)
 
 mod distribution;
 mod error;
 mod kde;
 mod param;
-mod sampler;
+pub mod sampler;
 mod study;
 mod trial;
 mod types;
 
 pub use error::{Result, TpeError};
-pub use sampler::{CompletedTrial, RandomSampler, Sampler, TpeSampler, TpeSamplerBuilder};
+pub use param::ParamValue;
 pub use study::Study;
 pub use trial::Trial;
 pub use types::{Direction, TrialState};

src/param.rs

@@ -1,15 +1,11 @@
 //! Parameter value storage types.
 
-#[cfg(feature = "serde")]
-use serde::{Deserialize, Serialize};
-
 /// Represents a sampled parameter value.
 ///
 /// This enum stores different parameter value types uniformly.
 /// For categorical parameters, the `Categorical` variant stores
 /// the index into the choices array.
 #[derive(Clone, Debug, PartialEq)]
-#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub enum ParamValue {
     /// A floating-point parameter value.
     Float(f64),

src/sampler/mod.rs

@@ -1,15 +1,10 @@
 //! Sampler trait and implementations for parameter sampling.
 
-mod random;
+pub mod random;
 pub mod tpe;
 
 use std::collections::HashMap;
 
-pub use random::RandomSampler;
-#[cfg(feature = "serde")]
-use serde::{Deserialize, Serialize};
-pub use tpe::{TpeSampler, TpeSamplerBuilder};
-
 use crate::distribution::Distribution;
 use crate::param::ParamValue;
 
@@ -19,7 +14,6 @@ use crate::param::ParamValue;
 /// parameter values, their distributions, and the objective value returned
 /// by the objective function.
 #[derive(Clone, Debug)]
-#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub struct CompletedTrial<V = f64> {
     /// The unique identifier for this trial.
     pub id: u64,

src/sampler/random.rs

@@ -17,7 +17,7 @@ use crate::sampler::{CompletedTrial, Sampler};
 /// # Examples
 ///
 /// ```
-/// use optimizer::RandomSampler;
+/// use optimizer::sampler::random::RandomSampler;
 ///
 /// // Create with default RNG
 /// let sampler = RandomSampler::new();
@@ -31,6 +31,7 @@ pub struct RandomSampler {
 
 impl RandomSampler {
     /// Creates a new random sampler with a default random seed.
+    #[must_use]
     pub fn new() -> Self {
         Self {
             rng: Mutex::new(StdRng::from_os_rng()),
@@ -40,6 +41,7 @@ impl RandomSampler {
     /// Creates a new random sampler with a fixed seed for reproducibility.
     ///
     /// Using the same seed will produce the same sequence of sampled values.
+    #[must_use]
     pub fn with_seed(seed: u64) -> Self {
         Self {
             rng: Mutex::new(StdRng::seed_from_u64(seed)),
@@ -54,6 +56,7 @@ impl Default for RandomSampler {
 }
 
 impl Sampler for RandomSampler {
+    #[allow(clippy::cast_possible_truncation, clippy::cast_precision_loss)]
     fn sample(
         &self,
         distribution: &Distribution,
@@ -110,6 +113,7 @@ impl Sampler for RandomSampler {
 }
 
 #[cfg(test)]
+#[allow(clippy::cast_possible_truncation, clippy::cast_precision_loss)]
 mod tests {
     use super::*;
     use crate::distribution::{CategoricalDistribution, FloatDistribution, IntDistribution};

src/sampler/tpe.rs

@@ -9,6 +9,7 @@ use rand::rngs::StdRng;
 use rand::{Rng, SeedableRng};
 
 use crate::distribution::Distribution;
+use crate::error::{Result, TpeError};
 use crate::kde::KernelDensityEstimator;
 use crate::param::ParamValue;
 use crate::sampler::{CompletedTrial, Sampler};
@@ -27,7 +28,7 @@ use crate::sampler::{CompletedTrial, Sampler};
 /// # Examples
 ///
 /// ```
-/// use optimizer::TpeSampler;
+/// use optimizer::sampler::tpe::TpeSampler;
 ///
 /// // Create with default settings
 /// let sampler = TpeSampler::new();
@@ -38,7 +39,8 @@ use crate::sampler::{CompletedTrial, Sampler};
 ///     .n_startup_trials(20)
 ///     .n_ei_candidates(32)
 ///     .seed(42)
-///     .build();
+///     .build()
+///     .unwrap();
 /// ```
 pub struct TpeSampler {
     /// Fraction of trials to consider as "good" (gamma quantile).
@@ -58,9 +60,10 @@ impl TpeSampler {
     ///
     /// Default settings:
     /// - gamma: 0.25 (top 25% of trials are considered "good")
-    /// - n_startup_trials: 10 (random sampling for first 10 trials)
+    /// - `n_startup_trials`: 10 (random sampling for first 10 trials)
-    /// - n_ei_candidates: 24 (evaluate 24 candidates per sample)
+    /// - `n_ei_candidates`: 24 (evaluate 24 candidates per sample)
-    /// - kde_bandwidth: None (uses Scott's rule for automatic bandwidth)
+    /// - `kde_bandwidth`: None (uses Scott's rule for automatic bandwidth)
+    #[must_use]
     pub fn new() -> Self {
         Self {
             gamma: 0.25,
@@ -76,15 +79,17 @@ impl TpeSampler {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::TpeSampler;
+    /// use optimizer::sampler::tpe::TpeSampler;
     ///
     /// let sampler = TpeSampler::builder()
     ///     .gamma(0.15)
     ///     .n_startup_trials(20)
     ///     .n_ei_candidates(32)
     ///     .seed(42)
-    ///     .build();
+    ///     .build()
+    ///     .unwrap();
     /// ```
+    #[must_use]
     pub fn builder() -> TpeSamplerBuilder {
         TpeSamplerBuilder::new()
     }
@@ -99,22 +104,24 @@ impl TpeSampler {
     /// * `kde_bandwidth` - Optional fixed bandwidth for KDE. If None, uses Scott's rule.
     /// * `seed` - Optional seed for reproducibility.
     ///
-    /// # Panics
+    /// # Errors
     ///
-    /// Panics if gamma is not in (0.0, 1.0) or if kde_bandwidth is Some but not positive.
+    /// Returns `TpeError::InvalidGamma` if gamma is not in (0.0, 1.0).
+    /// Returns `TpeError::InvalidBandwidth` if `kde_bandwidth` is Some but not positive.
     pub fn with_config(
         gamma: f64,
         n_startup_trials: usize,
         n_ei_candidates: usize,
         kde_bandwidth: Option<f64>,
         seed: Option<u64>,
-    ) -> Self {
+    ) -> Result<Self> {
-        assert!(
+        if gamma <= 0.0 || gamma >= 1.0 {
-            gamma > 0.0 && gamma < 1.0,
+            return Err(TpeError::InvalidGamma(gamma));
-            "gamma must be in (0.0, 1.0), got {gamma}"
+        }
-        );
+        if let Some(bw) = kde_bandwidth
-        if let Some(bw) = kde_bandwidth {
+            && bw <= 0.0
-            assert!(bw > 0.0, "kde_bandwidth must be positive, got {bw}");
+        {
+            return Err(TpeError::InvalidBandwidth(bw));
         }
 
         let rng = match seed {
@@ -122,19 +129,24 @@ impl TpeSampler {
             None => StdRng::from_os_rng(),
         };
 
-        Self {
+        Ok(Self {
             gamma,
             n_startup_trials,
             n_ei_candidates,
             kde_bandwidth,
             rng: Mutex::new(rng),
-        }
+        })
     }
 
     /// Splits trials into good and bad groups based on the gamma quantile.
     ///
-    /// Returns (good_trials, bad_trials) where good_trials contains trials
+    /// Returns (`good_trials`, `bad_trials`) where `good_trials` contains trials
     /// with values below the gamma quantile (for minimization).
+    #[allow(
+        clippy::cast_precision_loss,
+        clippy::cast_possible_truncation,
+        clippy::cast_sign_loss
+    )]
     fn split_trials<'a>(
         &self,
         history: &'a [CompletedTrial],
@@ -149,7 +161,7 @@ impl TpeSampler {
             history[a]
                 .value
                 .partial_cmp(&history[b].value)
-                .unwrap_or(std::cmp::Ordering::Equal)
+                .unwrap_or(core::cmp::Ordering::Equal)
         });
 
         // Calculate the split point (gamma quantile)
@@ -171,6 +183,11 @@ impl TpeSampler {
     }
 
     /// Samples uniformly from a distribution (used during startup phase).
+    #[allow(
+        clippy::cast_possible_truncation,
+        clippy::cast_precision_loss,
+        clippy::unused_self
+    )]
     fn sample_uniform(&self, distribution: &Distribution, rng: &mut StdRng) -> ParamValue {
         match distribution {
             Distribution::Float(d) => {
@@ -241,6 +258,11 @@ impl TpeSampler {
             None => KernelDensityEstimator::new(bad_internal),
         };
 
+        // If KDE construction fails, fall back to uniform sampling
+        let (Ok(l_kde), Ok(g_kde)) = (l_kde, g_kde) else {
+            return rng.random_range(low..=high);
+        };
+
         // Generate candidates from l(x) and select the one with best l(x)/g(x) ratio
         let mut best_candidate = internal_low;
         let mut best_ratio = f64::NEG_INFINITY;
@@ -289,15 +311,19 @@ impl TpeSampler {
     }
 
     /// Samples using TPE for integer distributions.
-    #[allow(clippy::too_many_arguments)]
+    #[allow(
+        clippy::too_many_arguments,
+        clippy::cast_precision_loss,
+        clippy::cast_possible_truncation
+    )]
     fn sample_tpe_int(
         &self,
         low: i64,
         high: i64,
         log_scale: bool,
         step: Option<i64>,
-        good_values: Vec<i64>,
+        good_values: &[i64],
-        bad_values: Vec<i64>,
+        bad_values: &[i64],
         rng: &mut StdRng,
     ) -> i64 {
         // Convert to floats for KDE
@@ -331,23 +357,24 @@ impl TpeSampler {
     }
 
     /// Samples using TPE for categorical distributions.
+    #[allow(clippy::cast_precision_loss, clippy::unused_self)]
     fn sample_tpe_categorical(
         &self,
         n_choices: usize,
-        good_indices: Vec<usize>,
+        good_indices: &[usize],
-        bad_indices: Vec<usize>,
+        bad_indices: &[usize],
         rng: &mut StdRng,
     ) -> usize {
         // Count occurrences in good and bad groups
         let mut good_counts = vec![0usize; n_choices];
         let mut bad_counts = vec![0usize; n_choices];
 
-        for &idx in &good_indices {
+        for &idx in good_indices {
             if idx < n_choices {
                 good_counts[idx] += 1;
             }
         }
-        for &idx in &bad_indices {
+        for &idx in bad_indices {
             if idx < n_choices {
                 bad_counts[idx] += 1;
             }
@@ -395,14 +422,15 @@ impl Default for TpeSampler {
 /// # Examples
 ///
 /// ```
-/// use optimizer::TpeSamplerBuilder;
+/// use optimizer::sampler::tpe::TpeSamplerBuilder;
 ///
 /// let sampler = TpeSamplerBuilder::new()
 ///     .gamma(0.15)
 ///     .n_startup_trials(20)
 ///     .n_ei_candidates(32)
 ///     .seed(42)
-///     .build();
+///     .build()
+///     .unwrap();
 /// ```
 #[derive(Debug, Clone)]
 pub struct TpeSamplerBuilder {
@@ -418,10 +446,11 @@ impl TpeSamplerBuilder {
     ///
     /// Default settings:
     /// - gamma: 0.25 (top 25% of trials are considered "good")
-    /// - n_startup_trials: 10 (random sampling for first 10 trials)
+    /// - `n_startup_trials`: 10 (random sampling for first 10 trials)
-    /// - n_ei_candidates: 24 (evaluate 24 candidates per sample)
+    /// - `n_ei_candidates`: 24 (evaluate 24 candidates per sample)
-    /// - kde_bandwidth: None (uses Scott's rule for automatic bandwidth)
+    /// - `kde_bandwidth`: None (uses Scott's rule for automatic bandwidth)
     /// - seed: None (use OS-provided entropy)
+    #[must_use]
     pub fn new() -> Self {
         Self {
             gamma: 0.25,
@@ -441,24 +470,23 @@ impl TpeSamplerBuilder {
     ///
     /// * `gamma` - Quantile value, must be in (0.0, 1.0).
     ///
-    /// # Panics
-    ///
-    /// Panics if gamma is not in (0.0, 1.0).
-    ///
     /// # Examples
     ///
     /// ```
-    /// use optimizer::TpeSamplerBuilder;
+    /// use optimizer::sampler::tpe::TpeSamplerBuilder;
     ///
     /// let sampler = TpeSamplerBuilder::new()
     ///     .gamma(0.10)  // Use top 10% as "good" trials
-    ///     .build();
+    ///     .build()
+    ///     .unwrap();
     /// ```
+    ///
+    /// # Note
+    ///
+    /// Validation happens at `build()` time. If gamma is not in (0.0, 1.0),
+    /// `build()` will return `Err(TpeError::InvalidGamma)`.
+    #[must_use]
     pub fn gamma(mut self, gamma: f64) -> Self {
-        assert!(
-            gamma > 0.0 && gamma < 1.0,
-            "gamma must be in (0.0, 1.0), got {gamma}"
-        );
         self.gamma = gamma;
         self
     }
@@ -476,12 +504,14 @@ impl TpeSamplerBuilder {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::TpeSamplerBuilder;
+    /// use optimizer::sampler::tpe::TpeSamplerBuilder;
     ///
     /// let sampler = TpeSamplerBuilder::new()
     ///     .n_startup_trials(20)  // Random sample first 20 trials
-    ///     .build();
+    ///     .build()
+    ///     .unwrap();
     /// ```
+    #[must_use]
     pub fn n_startup_trials(mut self, n: usize) -> Self {
         self.n_startup_trials = n;
         self
@@ -500,12 +530,14 @@ impl TpeSamplerBuilder {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::TpeSamplerBuilder;
+    /// use optimizer::sampler::tpe::TpeSamplerBuilder;
     ///
     /// let sampler = TpeSamplerBuilder::new()
     ///     .n_ei_candidates(48)  // Evaluate more candidates
-    ///     .build();
+    ///     .build()
+    ///     .unwrap();
     /// ```
+    #[must_use]
     pub fn n_ei_candidates(mut self, n: usize) -> Self {
         self.n_ei_candidates = n;
         self
@@ -523,24 +555,23 @@ impl TpeSamplerBuilder {
     ///
     /// * `bandwidth` - The fixed bandwidth (standard deviation) for Gaussian kernels.
     ///
-    /// # Panics
-    ///
-    /// Panics if bandwidth is not positive.
-    ///
     /// # Examples
     ///
     /// ```
-    /// use optimizer::TpeSamplerBuilder;
+    /// use optimizer::sampler::tpe::TpeSamplerBuilder;
     ///
     /// let sampler = TpeSamplerBuilder::new()
     ///     .kde_bandwidth(0.5)  // Fixed bandwidth of 0.5
-    ///     .build();
+    ///     .build()
+    ///     .unwrap();
     /// ```
+    ///
+    /// # Note
+    ///
+    /// Validation happens at `build()` time. If bandwidth is not positive,
+    /// `build()` will return `Err(TpeError::InvalidBandwidth)`.
+    #[must_use]
     pub fn kde_bandwidth(mut self, bandwidth: f64) -> Self {
-        assert!(
-            bandwidth > 0.0,
-            "kde_bandwidth must be positive, got {bandwidth}"
-        );
         self.kde_bandwidth = Some(bandwidth);
         self
     }
@@ -554,12 +585,14 @@ impl TpeSamplerBuilder {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::TpeSamplerBuilder;
+    /// use optimizer::sampler::tpe::TpeSamplerBuilder;
     ///
     /// let sampler = TpeSamplerBuilder::new()
     ///     .seed(42)  // Reproducible results
-    ///     .build();
+    ///     .build()
+    ///     .unwrap();
     /// ```
+    #[must_use]
     pub fn seed(mut self, seed: u64) -> Self {
         self.seed = Some(seed);
         self
@@ -567,19 +600,25 @@ impl TpeSamplerBuilder {
 
     /// Builds the configured [`TpeSampler`].
     ///
+    /// # Errors
+    ///
+    /// Returns `TpeError::InvalidGamma` if gamma is not in (0.0, 1.0).
+    /// Returns `TpeError::InvalidBandwidth` if `kde_bandwidth` is Some but not positive.
+    ///
     /// # Examples
     ///
     /// ```
-    /// use optimizer::TpeSamplerBuilder;
+    /// use optimizer::sampler::tpe::TpeSamplerBuilder;
     ///
     /// let sampler = TpeSamplerBuilder::new()
     ///     .gamma(0.15)
     ///     .n_startup_trials(20)
     ///     .n_ei_candidates(32)
     ///     .seed(42)
-    ///     .build();
+    ///     .build()
+    ///     .unwrap();
     /// ```
-    pub fn build(self) -> TpeSampler {
+    pub fn build(self) -> Result<TpeSampler> {
         TpeSampler::with_config(
             self.gamma,
             self.n_startup_trials,
@@ -597,6 +636,7 @@ impl Default for TpeSamplerBuilder {
 }
 
 impl Sampler for TpeSampler {
+    #[allow(clippy::too_many_lines)]
     fn sample(
         &self,
         distribution: &Distribution,
@@ -693,8 +733,8 @@ impl Sampler for TpeSampler {
                     d.high,
                     d.log_scale,
                     d.step,
-                    good_values,
+                    &good_values,
-                    bad_values,
+                    &bad_values,
                     &mut rng,
                 );
                 ParamValue::Int(value)
@@ -725,7 +765,7 @@ impl Sampler for TpeSampler {
                 }
 
                 let index =
-                    self.sample_tpe_categorical(d.n_choices, good_indices, bad_indices, &mut rng);
+                    self.sample_tpe_categorical(d.n_choices, &good_indices, &bad_indices, &mut rng);
                 ParamValue::Categorical(index)
             }
         }
@@ -733,6 +773,11 @@ impl Sampler for TpeSampler {
 }
 
 #[cfg(test)]
+#[allow(
+    clippy::similar_names,
+    clippy::cast_sign_loss,
+    clippy::cast_precision_loss
+)]
 mod tests {
     use std::collections::HashMap;
 
@@ -756,34 +801,34 @@ mod tests {
     #[test]
     fn test_tpe_sampler_new() {
         let sampler = TpeSampler::new();
-        assert_eq!(sampler.gamma, 0.25);
+        assert!((sampler.gamma - 0.25).abs() < f64::EPSILON);
         assert_eq!(sampler.n_startup_trials, 10);
         assert_eq!(sampler.n_ei_candidates, 24);
     }
 
     #[test]
     fn test_tpe_sampler_with_config() {
-        let sampler = TpeSampler::with_config(0.15, 20, 32, None, Some(42));
+        let sampler = TpeSampler::with_config(0.15, 20, 32, None, Some(42)).unwrap();
-        assert_eq!(sampler.gamma, 0.15);
+        assert!((sampler.gamma - 0.15).abs() < f64::EPSILON);
         assert_eq!(sampler.n_startup_trials, 20);
         assert_eq!(sampler.n_ei_candidates, 32);
     }
 
     #[test]
-    #[should_panic(expected = "gamma must be in (0.0, 1.0)")]
     fn test_tpe_sampler_invalid_gamma_zero() {
-        TpeSampler::with_config(0.0, 10, 24, None, None);
+        let result = TpeSampler::with_config(0.0, 10, 24, None, None);
+        assert!(matches!(result, Err(TpeError::InvalidGamma(_))));
     }
 
     #[test]
-    #[should_panic(expected = "gamma must be in (0.0, 1.0)")]
     fn test_tpe_sampler_invalid_gamma_one() {
-        TpeSampler::with_config(1.0, 10, 24, None, None);
+        let result = TpeSampler::with_config(1.0, 10, 24, None, None);
+        assert!(matches!(result, Err(TpeError::InvalidGamma(_))));
     }
 
     #[test]
     fn test_tpe_startup_random_sampling() {
-        let sampler = TpeSampler::with_config(0.25, 10, 24, None, Some(42));
+        let sampler = TpeSampler::with_config(0.25, 10, 24, None, Some(42)).unwrap();
         let dist = Distribution::Float(FloatDistribution {
             low: 0.0,
             high: 1.0,
@@ -806,7 +851,7 @@ mod tests {
 
     #[test]
     fn test_tpe_split_trials() {
-        let sampler = TpeSampler::with_config(0.25, 10, 24, None, Some(42));
+        let sampler = TpeSampler::with_config(0.25, 10, 24, None, Some(42)).unwrap();
 
         let dist = Distribution::Float(FloatDistribution {
             low: 0.0,
@@ -820,8 +865,8 @@ mod tests {
             .map(|i| {
                 create_trial(
                     i as u64,
-                    i as f64,
+                    f64::from(i),
-                    vec![("x", ParamValue::Float(i as f64 / 20.0), dist.clone())],
+                    vec![("x", ParamValue::Float(f64::from(i) / 20.0), dist.clone())],
                 )
             })
             .collect();
@@ -840,7 +885,7 @@ mod tests {
 
     #[test]
     fn test_tpe_samples_float_with_history() {
-        let sampler = TpeSampler::with_config(0.25, 5, 24, None, Some(42));
+        let sampler = TpeSampler::with_config(0.25, 5, 24, None, Some(42)).unwrap();
 
         let dist = Distribution::Float(FloatDistribution {
             low: 0.0,
@@ -852,7 +897,7 @@ mod tests {
         // Create history where low values (near 0.2) are "good"
         let history: Vec<CompletedTrial> = (0..20)
             .map(|i| {
-                let x = i as f64 / 20.0;
+                let x = f64::from(i) / 20.0;
                 // Objective is (x - 0.2)^2, minimized at x=0.2
                 let value = (x - 0.2).powi(2);
                 create_trial(
@@ -882,7 +927,7 @@ mod tests {
 
     #[test]
     fn test_tpe_categorical_sampling() {
-        let sampler = TpeSampler::with_config(0.25, 5, 24, None, Some(42));
+        let sampler = TpeSampler::with_config(0.25, 5, 24, None, Some(42)).unwrap();
 
         let dist = Distribution::Categorical(CategoricalDistribution { n_choices: 4 });
 
@@ -922,7 +967,7 @@ mod tests {
 
     #[test]
     fn test_tpe_int_sampling() {
-        let sampler = TpeSampler::with_config(0.25, 5, 24, None, Some(42));
+        let sampler = TpeSampler::with_config(0.25, 5, 24, None, Some(42)).unwrap();
 
         let dist = Distribution::Int(IntDistribution {
             low: 0,
@@ -968,14 +1013,14 @@ mod tests {
             .map(|i| {
                 create_trial(
                     i as u64,
-                    i as f64,
+                    f64::from(i),
-                    vec![("x", ParamValue::Float(i as f64 / 20.0), dist.clone())],
+                    vec![("x", ParamValue::Float(f64::from(i) / 20.0), dist.clone())],
                 )
             })
             .collect();
 
-        let sampler1 = TpeSampler::with_config(0.25, 5, 24, None, Some(12345));
+        let sampler1 = TpeSampler::with_config(0.25, 5, 24, None, Some(12345)).unwrap();
-        let sampler2 = TpeSampler::with_config(0.25, 5, 24, None, Some(12345));
+        let sampler2 = TpeSampler::with_config(0.25, 5, 24, None, Some(12345)).unwrap();
 
         for i in 0..10 {
             let v1 = sampler1.sample(&dist, i, &history);
@@ -987,8 +1032,8 @@ mod tests {
     #[test]
     fn test_tpe_sampler_builder_default() {
         let builder = TpeSamplerBuilder::new();
-        let sampler = builder.build();
+        let sampler = builder.build().unwrap();
-        assert_eq!(sampler.gamma, 0.25);
+        assert!((sampler.gamma - 0.25).abs() < f64::EPSILON);
         assert_eq!(sampler.n_startup_trials, 10);
         assert_eq!(sampler.n_ei_candidates, 24);
     }
@@ -1000,8 +1045,9 @@ mod tests {
             .n_startup_trials(20)
             .n_ei_candidates(32)
             .seed(42)
-            .build();
+            .build()
-        assert_eq!(sampler.gamma, 0.15);
+            .unwrap();
+        assert!((sampler.gamma - 0.15).abs() < f64::EPSILON);
         assert_eq!(sampler.n_startup_trials, 20);
         assert_eq!(sampler.n_ei_candidates, 32);
     }
@@ -1012,8 +1058,9 @@ mod tests {
             .gamma(0.10)
             .n_startup_trials(15)
             .n_ei_candidates(48)
-            .build();
+            .build()
-        assert_eq!(sampler.gamma, 0.10);
+            .unwrap();
+        assert!((sampler.gamma - 0.10).abs() < f64::EPSILON);
         assert_eq!(sampler.n_startup_trials, 15);
         assert_eq!(sampler.n_ei_candidates, 48);
     }
@@ -1021,16 +1068,16 @@ mod tests {
     #[test]
     fn test_tpe_sampler_builder_partial() {
         // Test setting only some options
-        let sampler = TpeSamplerBuilder::new().gamma(0.20).build();
+        let sampler = TpeSamplerBuilder::new().gamma(0.20).build().unwrap();
-        assert_eq!(sampler.gamma, 0.20);
+        assert!((sampler.gamma - 0.20).abs() < f64::EPSILON);
         assert_eq!(sampler.n_startup_trials, 10); // default
         assert_eq!(sampler.n_ei_candidates, 24); // default
     }
 
     #[test]
-    #[should_panic(expected = "gamma must be in (0.0, 1.0)")]
     fn test_tpe_sampler_builder_invalid_gamma() {
-        TpeSamplerBuilder::new().gamma(1.5).build();
+        let result = TpeSamplerBuilder::new().gamma(1.5).build();
+        assert!(matches!(result, Err(TpeError::InvalidGamma(_))));
     }
 
     #[test]
@@ -1042,12 +1089,12 @@ mod tests {
             step: None,
         });
 
-        let history: Vec<CompletedTrial> = (0..20)
+        let history: Vec<CompletedTrial> = (0..20u32)
             .map(|i| {
                 create_trial(
-                    i as u64,
+                    u64::from(i),
-                    i as f64,
+                    f64::from(i),
-                    vec![("x", ParamValue::Float(i as f64 / 20.0), dist.clone())],
+                    vec![("x", ParamValue::Float(f64::from(i) / 20.0), dist.clone())],
                 )
             })
             .collect();
@@ -1055,11 +1102,13 @@ mod tests {
         let sampler1 = TpeSampler::builder()
             .seed(99999)
             .n_startup_trials(5)
-            .build();
+            .build()
+            .unwrap();
         let sampler2 = TpeSampler::builder()
             .seed(99999)
             .n_startup_trials(5)
-            .build();
+            .build()
+            .unwrap();
 
         for i in 0..10 {
             let v1 = sampler1.sample(&dist, i, &history);

src/study.rs

@@ -1,25 +1,18 @@
 //! Study implementation for managing optimization trials.
 
 #[cfg(feature = "async")]
-use std::future::Future;
+use core::future::Future;
-use std::ops::ControlFlow;
+use core::ops::ControlFlow;
+use core::sync::atomic::{AtomicU64, Ordering};
 use std::sync::Arc;
-use std::sync::atomic::{AtomicU64, Ordering};
 
 use parking_lot::RwLock;
-#[cfg(feature = "serde")]
-use serde::{Deserialize, Serialize};
 
-use crate::sampler::{CompletedTrial, RandomSampler, Sampler};
+use crate::sampler::random::RandomSampler;
+use crate::sampler::{CompletedTrial, Sampler};
 use crate::trial::Trial;
 use crate::types::Direction;
 
-/// Helper function to create default sampler for serde deserialization.
-#[cfg(feature = "serde")]
-fn default_sampler() -> Arc<dyn Sampler> {
-    Arc::new(RandomSampler::new())
-}
-
 /// A study manages the optimization process, tracking trials and their results.
 ///
 /// The study is parameterized by the objective value type `V`, which defaults to `f64`.
@@ -29,13 +22,6 @@ fn default_sampler() -> Arc<dyn Sampler> {
 /// When `V = f64`, the study passes trial history to the sampler for informed
 /// parameter suggestions (e.g., TPE sampler uses history to guide sampling).
 ///
-/// # Serialization
-///
-/// When the `serde` feature is enabled, the study can be serialized and deserialized.
-/// The completed trials and trial ID counter are preserved, allowing optimization to
-/// continue after deserialization. The sampler is not serialized; upon deserialization,
-/// a default `RandomSampler` is used. Use `Study::set_sampler()` to restore a custom sampler.
-///
 /// # Examples
 ///
 /// ```
@@ -79,6 +65,7 @@ where
     /// let study: Study<f64> = Study::new(Direction::Minimize);
     /// assert_eq!(study.direction(), Direction::Minimize);
     /// ```
+    #[must_use]
     pub fn new(direction: Direction) -> Self {
         Self::with_sampler(direction, RandomSampler::new())
     }
@@ -93,7 +80,8 @@ where
     /// # Examples
     ///
     /// ```
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// let sampler = RandomSampler::with_seed(42);
     /// let study: Study<f64> = Study::with_sampler(Direction::Maximize, sampler);
@@ -115,9 +103,6 @@ where
 
     /// Sets a new sampler for the study.
     ///
-    /// This method is useful after deserializing a study when you want to use
-    /// a custom sampler (e.g., TPE) instead of the default `RandomSampler`.
-    ///
     /// # Arguments
     ///
     /// * `sampler` - The sampler to use for parameter sampling.
@@ -125,9 +110,9 @@ where
     /// # Examples
     ///
     /// ```
-    /// use optimizer::{Direction, Study, TpeSampler};
+    /// use optimizer::sampler::tpe::TpeSampler;
+    /// use optimizer::{Direction, Study};
     ///
-    /// // After deserializing a study, restore the TPE sampler
     /// let mut study: Study<f64> = Study::new(Direction::Minimize);
     /// study.set_sampler(TpeSampler::new());
     /// ```
@@ -332,17 +317,15 @@ where
                 match self.direction {
                     Direction::Minimize => {
                         // Reverse ordering: smaller values are "greater" for max_by
-                        ordering
+                        ordering.map_or(core::cmp::Ordering::Equal, core::cmp::Ordering::reverse)
-                            .map(|o| o.reverse())
-                            .unwrap_or(std::cmp::Ordering::Equal)
                     }
                     Direction::Maximize => {
                         // Normal ordering: larger values are "greater" for max_by
-                        ordering.unwrap_or(std::cmp::Ordering::Equal)
+                        ordering.unwrap_or(core::cmp::Ordering::Equal)
                     }
                 }
             })
-            .expect("trials is not empty");
+            .ok_or(crate::TpeError::NoCompletedTrials)?;
 
         Ok(best.clone())
     }
@@ -409,7 +392,8 @@ where
     /// # Examples
     ///
     /// ```
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// // Minimize x^2
     /// let sampler = RandomSampler::with_seed(42);
@@ -429,7 +413,7 @@ where
     /// ```
     pub fn optimize<F, E>(&self, n_trials: usize, mut objective: F) -> crate::Result<()>
     where
-        F: FnMut(&mut Trial) -> std::result::Result<V, E>,
+        F: FnMut(&mut Trial) -> core::result::Result<V, E>,
         E: ToString,
     {
         for _ in 0..n_trials {
@@ -476,7 +460,8 @@ where
     /// # Examples
     ///
     /// ```
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// # #[cfg(feature = "async")]
     /// # async fn example() -> optimizer::Result<()> {
@@ -506,7 +491,7 @@ where
     ) -> crate::Result<()>
     where
         F: Fn(Trial) -> Fut,
-        Fut: Future<Output = std::result::Result<(Trial, V), E>>,
+        Fut: Future<Output = core::result::Result<(Trial, V), E>>,
         E: ToString,
     {
         for _ in 0..n_trials {
@@ -552,11 +537,13 @@ where
     /// # Errors
     ///
     /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `TpeError::TaskError` if the semaphore is closed or a spawned task panics.
     ///
     /// # Examples
     ///
     /// ```
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// # #[cfg(feature = "async")]
     /// # async fn example() -> optimizer::Result<()> {
@@ -587,7 +574,7 @@ where
     ) -> crate::Result<()>
     where
         F: Fn(Trial) -> Fut + Send + Sync + 'static,
-        Fut: Future<Output = std::result::Result<(Trial, V), E>> + Send,
+        Fut: Future<Output = core::result::Result<(Trial, V), E>> + Send,
         E: ToString + Send + 'static,
         V: Send + 'static,
     {
@@ -599,7 +586,11 @@ where
         let mut handles = Vec::with_capacity(n_trials);
 
         for _ in 0..n_trials {
-            let permit = semaphore.clone().acquire_owned().await.unwrap();
+            let permit = semaphore
+                .clone()
+                .acquire_owned()
+                .await
+                .map_err(|e| crate::TpeError::TaskError(e.to_string()))?;
             let trial = self.create_trial();
             let objective = Arc::clone(&objective);
 
@@ -614,7 +605,10 @@ where
 
         // Wait for all tasks and record results
         for handle in handles {
-            match handle.await.unwrap() {
+            match handle
+                .await
+                .map_err(|e| crate::TpeError::TaskError(e.to_string()))?
+            {
                 Ok((trial, value)) => {
                     self.complete_trial(trial, value);
                 }
@@ -651,13 +645,15 @@ where
     ///
     /// Returns `TpeError::NoCompletedTrials` if no trials completed successfully
     /// before optimization stopped (either by completing all trials or early stopping).
+    /// Returns `TpeError::Internal` if a completed trial is not found after adding (internal invariant violation).
     ///
     /// # Examples
     ///
     /// ```
     /// use std::ops::ControlFlow;
     ///
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// // Stop early when we find a good enough value
     /// let sampler = RandomSampler::with_seed(42);
@@ -692,7 +688,7 @@ where
     ) -> crate::Result<()>
     where
         V: Clone,
-        F: FnMut(&mut Trial) -> std::result::Result<V, E>,
+        F: FnMut(&mut Trial) -> core::result::Result<V, E>,
         C: FnMut(&Study<V>, &CompletedTrial<V>) -> ControlFlow<()>,
         E: ToString,
     {
@@ -705,7 +701,11 @@ where
 
                     // Get the just-completed trial for the callback
                     let trials = self.completed_trials.read();
-                    let completed = trials.last().expect("just added a trial");
+                    let Some(completed) = trials.last() else {
+                        return Err(crate::TpeError::Internal(
+                            "completed trial not found after adding",
+                        ));
+                    };
 
                     // Call the callback and check if we should stop
                     // Note: We need to drop the read lock before calling callback
@@ -746,7 +746,8 @@ impl Study<f64> {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// // With a seeded sampler for reproducibility
     /// let sampler = RandomSampler::with_seed(42);
@@ -787,7 +788,8 @@ impl Study<f64> {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// // Minimize x^2 with sampler integration
     /// let sampler = RandomSampler::with_seed(42);
@@ -809,7 +811,7 @@ impl Study<f64> {
         mut objective: F,
     ) -> crate::Result<()>
     where
-        F: FnMut(&mut Trial) -> std::result::Result<f64, E>,
+        F: FnMut(&mut Trial) -> core::result::Result<f64, E>,
         E: ToString,
     {
         for _ in 0..n_trials {
@@ -850,13 +852,15 @@ impl Study<f64> {
     /// # Errors
     ///
     /// Returns `TpeError::NoCompletedTrials` if no trials completed successfully.
+    /// Returns `TpeError::Internal` if a completed trial is not found after adding (internal invariant violation).
     ///
     /// # Examples
     ///
     /// ```
     /// use std::ops::ControlFlow;
     ///
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// // Optimize with sampler integration and early stopping
     /// let sampler = RandomSampler::with_seed(42);
@@ -889,7 +893,7 @@ impl Study<f64> {
         mut callback: C,
     ) -> crate::Result<()>
     where
-        F: FnMut(&mut Trial) -> std::result::Result<f64, E>,
+        F: FnMut(&mut Trial) -> core::result::Result<f64, E>,
         C: FnMut(&Study<f64>, &CompletedTrial<f64>) -> ControlFlow<()>,
         E: ToString,
     {
@@ -902,7 +906,11 @@ impl Study<f64> {
 
                     // Get the just-completed trial for the callback
                     let trials = self.completed_trials.read();
-                    let completed = trials.last().expect("just added a trial");
+                    let Some(completed) = trials.last() else {
+                        return Err(crate::TpeError::Internal(
+                            "completed trial not found after adding",
+                        ));
+                    };
 
                     // Call the callback and check if we should stop
                     // Note: We need to drop the read lock before calling callback
@@ -950,7 +958,8 @@ impl Study<f64> {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// # #[cfg(feature = "async")]
     /// # async fn example() -> optimizer::Result<()> {
@@ -980,7 +989,7 @@ impl Study<f64> {
     ) -> crate::Result<()>
     where
         F: Fn(Trial) -> Fut,
-        Fut: Future<Output = std::result::Result<(Trial, f64), E>>,
+        Fut: Future<Output = core::result::Result<(Trial, f64), E>>,
         E: ToString,
     {
         for _ in 0..n_trials {
@@ -1026,11 +1035,13 @@ impl Study<f64> {
     /// # Errors
     ///
     /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `TpeError::TaskError` if the semaphore is closed or a spawned task panics.
     ///
     /// # Examples
     ///
     /// ```
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// # #[cfg(feature = "async")]
     /// # async fn example() -> optimizer::Result<()> {
@@ -1061,7 +1072,7 @@ impl Study<f64> {
     ) -> crate::Result<()>
     where
         F: Fn(Trial) -> Fut + Send + Sync + 'static,
-        Fut: Future<Output = std::result::Result<(Trial, f64), E>> + Send,
+        Fut: Future<Output = core::result::Result<(Trial, f64), E>> + Send,
         E: ToString + Send + 'static,
     {
         use tokio::sync::Semaphore;
@@ -1072,7 +1083,11 @@ impl Study<f64> {
         let mut handles = Vec::with_capacity(n_trials);
 
         for _ in 0..n_trials {
-            let permit = semaphore.clone().acquire_owned().await.unwrap();
+            let permit = semaphore
+                .clone()
+                .acquire_owned()
+                .await
+                .map_err(|e| crate::TpeError::TaskError(e.to_string()))?;
             let trial = self.create_trial_with_sampler();
             let objective = Arc::clone(&objective);
 
@@ -1087,7 +1102,10 @@ impl Study<f64> {
 
         // Wait for all tasks and record results
         for handle in handles {
-            match handle.await.unwrap() {
+            match handle
+                .await
+                .map_err(|e| crate::TpeError::TaskError(e.to_string()))?
+            {
                 Ok((trial, value)) => {
                     self.complete_trial(trial, value);
                 }
@@ -1105,236 +1123,3 @@ impl Study<f64> {
         Ok(())
     }
 }
-
-// Manual Serialize implementation for Study<V> when serde feature is enabled.
-#[cfg(feature = "serde")]
-impl<V> Serialize for Study<V>
-where
-    V: PartialOrd + Serialize,
-{
-    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
-    where
-        S: serde::Serializer,
-    {
-        use serde::ser::SerializeStruct;
-
-        let mut state = serializer.serialize_struct("Study", 3)?;
-        state.serialize_field("direction", &self.direction)?;
-        // Serialize the Vec inside the Arc<RwLock<>>
-        let trials = self.completed_trials.read();
-        state.serialize_field("completed_trials", &*trials)?;
-        state.serialize_field("next_trial_id", &self.next_trial_id.load(Ordering::SeqCst))?;
-        state.end()
-    }
-}
-
-// Manual Deserialize implementation for Study<V> when serde feature is enabled.
-#[cfg(feature = "serde")]
-impl<'de, V> Deserialize<'de> for Study<V>
-where
-    V: PartialOrd + Deserialize<'de>,
-{
-    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
-    where
-        D: serde::Deserializer<'de>,
-    {
-        use std::fmt;
-        use std::marker::PhantomData;
-
-        use serde::de::{self, MapAccess, Visitor};
-
-        #[derive(serde::Deserialize)]
-        #[serde(field_identifier, rename_all = "snake_case")]
-        enum Field {
-            Direction,
-            CompletedTrials,
-            NextTrialId,
-        }
-
-        struct StudyVisitor<V>(PhantomData<V>);
-
-        impl<'de, V> Visitor<'de> for StudyVisitor<V>
-        where
-            V: PartialOrd + Deserialize<'de>,
-        {
-            type Value = Study<V>;
-
-            fn expecting(&self, formatter: &mut fmt::Formatter) -> fmt::Result {
-                formatter.write_str("struct Study")
-            }
-
-            fn visit_map<A>(self, mut map: A) -> Result<Self::Value, A::Error>
-            where
-                A: MapAccess<'de>,
-            {
-                let mut direction = None;
-                let mut completed_trials: Option<Vec<CompletedTrial<V>>> = None;
-                let mut next_trial_id = None;
-
-                while let Some(key) = map.next_key()? {
-                    match key {
-                        Field::Direction => {
-                            if direction.is_some() {
-                                return Err(de::Error::duplicate_field("direction"));
-                            }
-                            direction = Some(map.next_value()?);
-                        }
-                        Field::CompletedTrials => {
-                            if completed_trials.is_some() {
-                                return Err(de::Error::duplicate_field("completed_trials"));
-                            }
-                            completed_trials = Some(map.next_value()?);
-                        }
-                        Field::NextTrialId => {
-                            if next_trial_id.is_some() {
-                                return Err(de::Error::duplicate_field("next_trial_id"));
-                            }
-                            next_trial_id = Some(map.next_value()?);
-                        }
-                    }
-                }
-
-                let direction = direction.ok_or_else(|| de::Error::missing_field("direction"))?;
-                let completed_trials =
-                    completed_trials.ok_or_else(|| de::Error::missing_field("completed_trials"))?;
-                let next_trial_id: u64 =
-                    next_trial_id.ok_or_else(|| de::Error::missing_field("next_trial_id"))?;
-
-                Ok(Study {
-                    direction,
-                    sampler: default_sampler(),
-                    completed_trials: Arc::new(RwLock::new(completed_trials)),
-                    next_trial_id: AtomicU64::new(next_trial_id),
-                })
-            }
-        }
-
-        const FIELDS: &[&str] = &["direction", "completed_trials", "next_trial_id"];
-        deserializer.deserialize_struct("Study", FIELDS, StudyVisitor(PhantomData))
-    }
-}
-
-#[cfg(all(test, feature = "serde"))]
-mod serde_tests {
-    use super::*;
-
-    #[test]
-    fn test_study_serde_round_trip() {
-        // Create a study and add some trials
-        let study: Study<f64> = Study::new(Direction::Minimize);
-
-        // Run some optimization
-        study
-            .optimize(5, |trial| {
-                let x = trial.suggest_float("x", 0.0, 10.0)?;
-                let y = trial.suggest_int("y", 1, 5)?;
-                Ok::<_, crate::TpeError>(x + y as f64)
-            })
-            .unwrap();
-
-        // Serialize to JSON
-        let serialized = serde_json::to_string(&study).unwrap();
-
-        // Deserialize from JSON
-        let deserialized: Study<f64> = serde_json::from_str(&serialized).unwrap();
-
-        // Verify the data is preserved
-        assert_eq!(deserialized.direction(), study.direction());
-        assert_eq!(deserialized.n_trials(), study.n_trials());
-
-        // Verify the best trial is the same
-        let original_best = study.best_trial().unwrap();
-        let deserialized_best = deserialized.best_trial().unwrap();
-        assert_eq!(original_best.id, deserialized_best.id);
-        // Use approximate comparison for floats due to JSON serialization precision
-        assert!((original_best.value - deserialized_best.value).abs() < 1e-10);
-        // Check that all param keys match
-        assert_eq!(original_best.params.len(), deserialized_best.params.len());
-        for (key, original_val) in &original_best.params {
-            let deserialized_val = deserialized_best.params.get(key).unwrap();
-            match (original_val, deserialized_val) {
-                (crate::param::ParamValue::Float(a), crate::param::ParamValue::Float(b)) => {
-                    assert!((a - b).abs() < 1e-10, "Float param {key} differs");
-                }
-                _ => assert_eq!(original_val, deserialized_val),
-            }
-        }
-
-        // Verify we can continue optimization on the deserialized study
-        let initial_count = deserialized.n_trials();
-        deserialized
-            .optimize(3, |trial| {
-                let x = trial.suggest_float("x", 0.0, 10.0)?;
-                let y = trial.suggest_int("y", 1, 5)?;
-                Ok::<_, crate::TpeError>(x + y as f64)
-            })
-            .unwrap();
-
-        // Verify new trials were added
-        assert_eq!(deserialized.n_trials(), initial_count + 3);
-    }
-
-    #[test]
-    fn test_study_serde_preserves_trial_ids() {
-        let study: Study<f64> = Study::new(Direction::Maximize);
-
-        // Add 5 trials
-        study
-            .optimize(5, |trial| {
-                let x = trial.suggest_float("x", -1.0, 1.0)?;
-                Ok::<_, crate::TpeError>(x * x)
-            })
-            .unwrap();
-
-        // Serialize and deserialize
-        let serialized = serde_json::to_string(&study).unwrap();
-        let deserialized: Study<f64> = serde_json::from_str(&serialized).unwrap();
-
-        // Create a new trial - its ID should continue from where we left off
-        let new_trial = deserialized.create_trial();
-        assert_eq!(new_trial.id(), 5); // Next trial should be ID 5
-    }
-
-    #[test]
-    fn test_completed_trial_serde() {
-        use std::collections::HashMap;
-
-        use crate::distribution::{Distribution, FloatDistribution, IntDistribution};
-        use crate::param::ParamValue;
-
-        let mut params = HashMap::new();
-        params.insert("x".to_string(), ParamValue::Float(0.5));
-        params.insert("n".to_string(), ParamValue::Int(42));
-
-        let mut distributions = HashMap::new();
-        distributions.insert(
-            "x".to_string(),
-            Distribution::Float(FloatDistribution {
-                low: 0.0,
-                high: 1.0,
-                log_scale: false,
-                step: None,
-            }),
-        );
-        distributions.insert(
-            "n".to_string(),
-            Distribution::Int(IntDistribution {
-                low: 1,
-                high: 100,
-                log_scale: false,
-                step: None,
-            }),
-        );
-
-        let completed = CompletedTrial::new(42, params.clone(), distributions.clone(), 0.75);
-
-        // Serialize and deserialize
-        let serialized = serde_json::to_string(&completed).unwrap();
-        let deserialized: CompletedTrial<f64> = serde_json::from_str(&serialized).unwrap();
-
-        assert_eq!(deserialized.id, 42);
-        assert_eq!(deserialized.value, 0.75);
-        assert_eq!(deserialized.params, params);
-        assert_eq!(deserialized.distributions, distributions);
-    }
-}

src/trial.rs

@@ -4,8 +4,6 @@ use std::collections::HashMap;
 use std::sync::Arc;
 
 use parking_lot::RwLock;
-#[cfg(feature = "serde")]
-use serde::{Deserialize, Serialize};
 
 use crate::distribution::{
     CategoricalDistribution, Distribution, FloatDistribution, IntDistribution,
@@ -24,7 +22,6 @@ use crate::types::TrialState;
 /// `Study::create_trial()`, the trial receives the study's sampler and access
 /// to the history of completed trials for informed sampling.
 #[derive(Clone)]
-#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub struct Trial {
     /// Unique identifier for this trial.
     id: u64,
@@ -35,15 +32,13 @@ pub struct Trial {
     /// Parameter distributions, keyed by parameter name.
     distributions: HashMap<String, Distribution>,
     /// The sampler to use for generating parameter values.
-    #[cfg_attr(feature = "serde", serde(skip))]
     sampler: Option<Arc<dyn Sampler>>,
     /// Access to the history of completed trials (shared with Study).
-    #[cfg_attr(feature = "serde", serde(skip))]
     history: Option<Arc<RwLock<Vec<CompletedTrial<f64>>>>>,
 }
 
-impl std::fmt::Debug for Trial {
+impl core::fmt::Debug for Trial {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+    fn fmt(&self, f: &mut core::fmt::Formatter<'_>) -> core::fmt::Result {
         f.debug_struct("Trial")
             .field("id", &self.id)
             .field("state", &self.state)
@@ -76,6 +71,7 @@ impl Trial {
     /// let trial = Trial::new(0);
     /// assert_eq!(trial.id(), 0);
     /// ```
+    #[must_use]
     pub fn new(id: u64) -> Self {
         Self {
             id,
@@ -115,7 +111,7 @@ impl Trial {
     /// Samples a value from the given distribution using the sampler.
     ///
     /// If the trial has a sampler, it delegates to the sampler's sample method
-    /// with the history of completed trials. Otherwise, it uses the RandomSampler
+    /// with the history of completed trials. Otherwise, it uses the `RandomSampler`
     /// as a fallback.
     fn sample_value(&self, distribution: &Distribution) -> ParamValue {
         if let (Some(sampler), Some(history)) = (&self.sampler, &self.history) {
@@ -123,28 +119,32 @@ impl Trial {
             sampler.sample(distribution, self.id, &history_guard)
         } else {
             // Fallback to RandomSampler when no sampler is configured
-            use crate::sampler::RandomSampler;
+            use crate::sampler::random::RandomSampler;
             let fallback = RandomSampler::new();
             fallback.sample(distribution, self.id, &[])
         }
     }
 
     /// Returns the unique ID of this trial.
+    #[must_use]
     pub fn id(&self) -> u64 {
         self.id
     }
 
     /// Returns the current state of this trial.
+    #[must_use]
     pub fn state(&self) -> TrialState {
         self.state
     }
 
     /// Returns a reference to the sampled parameters.
+    #[must_use]
     pub fn params(&self) -> &HashMap<String, ParamValue> {
         &self.params
     }
 
     /// Returns a reference to the parameter distributions.
+    #[must_use]
     pub fn distributions(&self) -> &HashMap<String, Distribution> {
         &self.distributions
     }
@@ -205,8 +205,8 @@ impl Trial {
         if let Some(existing_dist) = self.distributions.get(&name) {
             // Verify the distribution matches
             if let Distribution::Float(existing) = existing_dist
-                && existing.low == low
+                && (existing.low - low).abs() < f64::EPSILON
-                && existing.high == high
+                && (existing.high - high).abs() < f64::EPSILON
                 && !existing.log_scale
                 && existing.step.is_none()
             {
@@ -225,9 +225,10 @@ impl Trial {
 
         // Sample using the sampler
         let dist = Distribution::Float(distribution);
-        let value = match self.sample_value(&dist) {
+        let ParamValue::Float(value) = self.sample_value(&dist) else {
-            ParamValue::Float(v) => v,
+            return Err(TpeError::Internal(
-            _ => unreachable!("Float distribution should return Float value"),
+                "Float distribution should return Float value",
+            ));
         };
 
         // Store distribution and value
@@ -242,7 +243,7 @@ impl Trial {
     /// The value is sampled uniformly in log space, which is useful for parameters
     /// that span multiple orders of magnitude (e.g., learning rates).
     ///
-    /// If the parameter has already been sampled with the same bounds and log_scale=true,
+    /// If the parameter has already been sampled with the same bounds and `log_scale=true`,
     /// the cached value is returned. If the parameter was sampled with different configuration,
     /// a `ParameterConflict` error is returned.
     ///
@@ -301,8 +302,8 @@ impl Trial {
         if let Some(existing_dist) = self.distributions.get(&name) {
             // Verify the distribution matches
             if let Distribution::Float(existing) = existing_dist
-                && existing.low == low
+                && (existing.low - low).abs() < f64::EPSILON
-                && existing.high == high
+                && (existing.high - high).abs() < f64::EPSILON
                 && existing.log_scale
                 && existing.step.is_none()
             {
@@ -321,9 +322,10 @@ impl Trial {
 
         // Sample using the sampler (sampler handles log-scale transformation)
         let dist = Distribution::Float(distribution);
-        let value = match self.sample_value(&dist) {
+        let ParamValue::Float(value) = self.sample_value(&dist) else {
-            ParamValue::Float(v) => v,
+            return Err(TpeError::Internal(
-            _ => unreachable!("Float distribution should return Float value"),
+                "Float distribution should return Float value",
+            ));
         };
 
         // Store distribution and value
@@ -397,8 +399,8 @@ impl Trial {
         if let Some(existing_dist) = self.distributions.get(&name) {
             // Verify the distribution matches
             if let Distribution::Float(existing) = existing_dist
-                && existing.low == low
+                && (existing.low - low).abs() < f64::EPSILON
-                && existing.high == high
+                && (existing.high - high).abs() < f64::EPSILON
                 && !existing.log_scale
                 && existing.step == Some(step)
             {
@@ -417,9 +419,10 @@ impl Trial {
 
         // Sample using the sampler (sampler handles step-grid)
         let dist = Distribution::Float(distribution);
-        let value = match self.sample_value(&dist) {
+        let ParamValue::Float(value) = self.sample_value(&dist) else {
-            ParamValue::Float(v) => v,
+            return Err(TpeError::Internal(
-            _ => unreachable!("Float distribution should return Float value"),
+                "Float distribution should return Float value",
+            ));
         };
 
         // Store distribution and value
@@ -460,6 +463,7 @@ impl Trial {
     /// let n2 = trial.suggest_int("n_layers", 1, 10).unwrap();
     /// assert_eq!(n, n2);
     /// ```
+    #[allow(clippy::cast_precision_loss)]
     pub fn suggest_int(&mut self, name: impl Into<String>, low: i64, high: i64) -> Result<i64> {
         if low > high {
             return Err(TpeError::InvalidBounds {
@@ -500,9 +504,10 @@ impl Trial {
 
         // Sample using the sampler
         let dist = Distribution::Int(distribution);
-        let value = match self.sample_value(&dist) {
+        let ParamValue::Int(value) = self.sample_value(&dist) else {
-            ParamValue::Int(v) => v,
+            return Err(TpeError::Internal(
-            _ => unreachable!("Int distribution should return Int value"),
+                "Int distribution should return Int value",
+            ));
         };
 
         // Store distribution and value
@@ -517,7 +522,7 @@ impl Trial {
     /// The value is sampled uniformly in log space, which is useful for parameters
     /// that span multiple orders of magnitude (e.g., batch sizes).
     ///
-    /// If the parameter has already been sampled with the same bounds and log_scale=true,
+    /// If the parameter has already been sampled with the same bounds and `log_scale=true`,
     /// the cached value is returned. If the parameter was sampled with different configuration,
     /// a `ParameterConflict` error is returned.
     ///
@@ -546,6 +551,7 @@ impl Trial {
     /// let batch_size2 = trial.suggest_int_log("batch_size", 1, 1024).unwrap();
     /// assert_eq!(batch_size, batch_size2);
     /// ```
+    #[allow(clippy::cast_precision_loss)]
     pub fn suggest_int_log(&mut self, name: impl Into<String>, low: i64, high: i64) -> Result<i64> {
         if low < 1 {
             return Err(TpeError::InvalidLogBounds);
@@ -590,9 +596,10 @@ impl Trial {
 
         // Sample using the sampler (sampler handles log-scale transformation)
         let dist = Distribution::Int(distribution);
-        let value = match self.sample_value(&dist) {
+        let ParamValue::Int(value) = self.sample_value(&dist) else {
-            ParamValue::Int(v) => v,
+            return Err(TpeError::Internal(
-            _ => unreachable!("Int distribution should return Int value"),
+                "Int distribution should return Int value",
+            ));
         };
 
         // Store distribution and value
@@ -643,6 +650,7 @@ impl Trial {
     ///     .unwrap();
     /// assert_eq!(n, n2);
     /// ```
+    #[allow(clippy::cast_precision_loss)]
     pub fn suggest_int_step(
         &mut self,
         name: impl Into<String>,
@@ -693,9 +701,10 @@ impl Trial {
 
         // Sample using the sampler (sampler handles step-grid)
         let dist = Distribution::Int(distribution);
-        let value = match self.sample_value(&dist) {
+        let ParamValue::Int(value) = self.sample_value(&dist) else {
-            ParamValue::Int(v) => v,
+            return Err(TpeError::Internal(
-            _ => unreachable!("Int distribution should return Int value"),
+                "Int distribution should return Int value",
+            ));
         };
 
         // Store distribution and value
@@ -779,9 +788,10 @@ impl Trial {
 
         // Sample using the sampler
         let dist = Distribution::Categorical(distribution);
-        let index = match self.sample_value(&dist) {
+        let ParamValue::Categorical(index) = self.sample_value(&dist) else {
-            ParamValue::Categorical(idx) => idx,
+            return Err(TpeError::Internal(
-            _ => unreachable!("Categorical distribution should return Categorical value"),
+                "Categorical distribution should return Categorical value",
+            ));
         };
 
         // Store distribution and value (store the index)

src/types.rs

@@ -1,11 +1,7 @@
 //! Core types for the optimizer library.
 
-#[cfg(feature = "serde")]
-use serde::{Deserialize, Serialize};
-
 /// The direction of optimization.
 #[derive(Clone, Copy, Debug, PartialEq, Eq)]
-#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub enum Direction {
     /// Minimize the objective value.
     Minimize,
@@ -15,7 +11,6 @@ pub enum Direction {
 
 /// The state of a trial in its lifecycle.
 #[derive(Clone, Copy, Debug, PartialEq, Eq)]
-#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub enum TrialState {
     /// The trial is currently running.
     Running,

tests/async_tests.rs

@@ -4,7 +4,9 @@
 
 #![cfg(feature = "async")]
 
-use optimizer::{Direction, RandomSampler, Study, TpeError, TpeSampler};
+use optimizer::sampler::random::RandomSampler;
+use optimizer::sampler::tpe::TpeSampler;
+use optimizer::{Direction, Study, TpeError};
 
 #[tokio::test]
 async fn test_optimize_async_basic() {
@@ -26,7 +28,11 @@ async fn test_optimize_async_basic() {
 
 #[tokio::test]
 async fn test_optimize_async_with_sampler() {
-    let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();
+    let sampler = TpeSampler::builder()
+        .seed(42)
+        .n_startup_trials(5)
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -61,7 +67,11 @@ async fn test_optimize_parallel() {
 
 #[tokio::test]
 async fn test_optimize_parallel_with_sampler() {
-    let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();
+    let sampler = TpeSampler::builder()
+        .seed(42)
+        .n_startup_trials(5)
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 

tests/integration.rs

@@ -1,6 +1,14 @@
 //! Integration tests for the optimizer library.
 
-use optimizer::{Direction, RandomSampler, Study, TpeError, TpeSampler, Trial};
+#![allow(
+    clippy::cast_sign_loss,
+    clippy::cast_precision_loss,
+    clippy::cast_possible_truncation
+)]
+
+use optimizer::sampler::random::RandomSampler;
+use optimizer::sampler::tpe::TpeSampler;
+use optimizer::{Direction, Study, TpeError, Trial};
 
 // =============================================================================
 // Test: optimize simple quadratic function with TPE, finds near-optimal
@@ -14,7 +22,8 @@ fn test_tpe_optimizes_quadratic_function() {
         .seed(42)
         .n_startup_trials(5) // Quick startup for test
         .n_ei_candidates(24)
-        .build();
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -40,7 +49,11 @@ fn test_tpe_optimizes_quadratic_function() {
 fn test_tpe_optimizes_multivariate_function() {
     // Minimize f(x, y) = x^2 + y^2 where x, y ∈ [-5, 5]
     // Optimal: (0, 0), f(0, 0) = 0
-    let sampler = TpeSampler::builder().seed(123).n_startup_trials(10).build();
+    let sampler = TpeSampler::builder()
+        .seed(123)
+        .n_startup_trials(10)
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -66,7 +79,11 @@ fn test_tpe_optimizes_multivariate_function() {
 fn test_tpe_maximization() {
     // Maximize f(x) = -(x - 2)^2 + 10 where x ∈ [-10, 10]
     // Optimal: x = 2, f(2) = 10
-    let sampler = TpeSampler::builder().seed(456).n_startup_trials(5).build();
+    let sampler = TpeSampler::builder()
+        .seed(456)
+        .n_startup_trials(5)
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Maximize, sampler);
 
@@ -550,7 +567,11 @@ fn test_invalid_step_errors() {
 
 #[test]
 fn test_tpe_with_categorical_parameter() {
-    let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();
+    let sampler = TpeSampler::builder()
+        .seed(42)
+        .n_startup_trials(5)
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Maximize, sampler);
 
@@ -582,7 +603,11 @@ fn test_tpe_with_categorical_parameter() {
 
 #[test]
 fn test_tpe_with_integer_parameters() {
-    let sampler = TpeSampler::builder().seed(789).n_startup_trials(5).build();
+    let sampler = TpeSampler::builder()
+        .seed(789)
+        .n_startup_trials(5)
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -756,7 +781,11 @@ fn test_study_set_sampler() {
     let mut study: Study<f64> = Study::new(Direction::Minimize);
 
     // Initially uses RandomSampler, now switch to TPE
-    let tpe = TpeSampler::builder().seed(42).n_startup_trials(5).build();
+    let tpe = TpeSampler::builder()
+        .seed(42)
+        .n_startup_trials(5)
+        .build()
+        .unwrap();
     study.set_sampler(tpe);
 
     // Should work with the new sampler
@@ -863,10 +892,10 @@ fn test_trial_debug_format() {
 
 #[test]
 fn test_tpe_sampler_builder_default_trait() {
-    use optimizer::TpeSamplerBuilder;
+    use optimizer::sampler::tpe::TpeSamplerBuilder;
 
     let builder = TpeSamplerBuilder::default();
-    let sampler = builder.build();
+    let sampler = builder.build().unwrap();
 
     // Should have default values
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
@@ -901,7 +930,8 @@ fn test_tpe_with_fixed_kde_bandwidth() {
         .seed(42)
         .n_startup_trials(5)
         .kde_bandwidth(0.5)
-        .build();
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -917,9 +947,9 @@ fn test_tpe_with_fixed_kde_bandwidth() {
 }
 
 #[test]
-#[should_panic(expected = "kde_bandwidth must be positive")]
 fn test_tpe_sampler_invalid_kde_bandwidth() {
-    TpeSampler::with_config(0.25, 10, 24, Some(-1.0), None);
+    let result = TpeSampler::with_config(0.25, 10, 24, Some(-1.0), None);
+    assert!(matches!(result, Err(TpeError::InvalidBandwidth(_))));
 }
 
 #[test]
@@ -928,7 +958,8 @@ fn test_tpe_split_trials_with_two_trials() {
     let sampler = TpeSampler::builder()
         .seed(42)
         .n_startup_trials(2) // TPE kicks in after 2 trials
-        .build();
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -944,7 +975,11 @@ fn test_tpe_split_trials_with_two_trials() {
 
 #[test]
 fn test_tpe_with_log_scale_int() {
-    let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();
+    let sampler = TpeSampler::builder()
+        .seed(42)
+        .n_startup_trials(5)
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -962,7 +997,11 @@ fn test_tpe_with_log_scale_int() {
 
 #[test]
 fn test_tpe_with_step_distributions() {
-    let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();
+    let sampler = TpeSampler::builder()
+        .seed(42)
+        .n_startup_trials(5)
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -1040,7 +1079,8 @@ fn test_tpe_empty_good_or_bad_values_fallback() {
         .seed(42)
         .n_startup_trials(5)
         .gamma(0.1) // Very small gamma means few "good" trials
-        .build();
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -1142,144 +1182,3 @@ fn test_best_trial_with_nan_values() {
     let best = study.best_trial();
     assert!(best.is_ok());
 }
-
-// =============================================================================
-// Serde tests (only run when serde feature is enabled)
-// =============================================================================
-
-#[cfg(feature = "serde")]
-mod serde_tests {
-    use super::*;
-
-    #[test]
-    fn test_direction_serde() {
-        // Test Direction serialization
-        let min = Direction::Minimize;
-        let max = Direction::Maximize;
-
-        let min_json = serde_json::to_string(&min).unwrap();
-        let max_json = serde_json::to_string(&max).unwrap();
-
-        let min_deser: Direction = serde_json::from_str(&min_json).unwrap();
-        let max_deser: Direction = serde_json::from_str(&max_json).unwrap();
-
-        assert_eq!(min, min_deser);
-        assert_eq!(max, max_deser);
-    }
-
-    #[test]
-    fn test_study_serde_with_categorical() {
-        let study: Study<f64> = Study::new(Direction::Minimize);
-
-        study
-            .optimize(5, |trial| {
-                let x = trial.suggest_float("x", 0.0, 10.0)?;
-                let opt = trial.suggest_categorical("opt", &["a", "b", "c"])?;
-                let _ = opt;
-                Ok::<_, TpeError>(x)
-            })
-            .unwrap();
-
-        // Serialize
-        let json = serde_json::to_string(&study).unwrap();
-
-        // Deserialize
-        let loaded: Study<f64> = serde_json::from_str(&json).unwrap();
-
-        assert_eq!(loaded.n_trials(), 5);
-        assert_eq!(loaded.direction(), Direction::Minimize);
-    }
-
-    #[test]
-    fn test_study_serde_with_all_param_types() {
-        let study: Study<f64> = Study::new(Direction::Maximize);
-
-        study
-            .optimize(3, |trial| {
-                let x = trial.suggest_float("x", 0.0, 10.0)?;
-                let y = trial.suggest_float_log("y", 0.001, 1.0)?;
-                let z = trial.suggest_float_step("z", 0.0, 1.0, 0.1)?;
-                let a = trial.suggest_int("a", 1, 10)?;
-                let b = trial.suggest_int_log("b", 1, 100)?;
-                let c = trial.suggest_int_step("c", 0, 100, 10)?;
-                let d = trial.suggest_categorical("d", &["p", "q"])?;
-                let _ = (y, z, b, c, d);
-                Ok::<_, TpeError>(x + a as f64)
-            })
-            .unwrap();
-
-        let json = serde_json::to_string(&study).unwrap();
-        let loaded: Study<f64> = serde_json::from_str(&json).unwrap();
-
-        assert_eq!(loaded.n_trials(), 3);
-        assert_eq!(loaded.direction(), Direction::Maximize);
-
-        // Verify we can continue optimization
-        loaded
-            .optimize(2, |trial| {
-                let x = trial.suggest_float("x", 0.0, 10.0)?;
-                Ok::<_, TpeError>(x)
-            })
-            .unwrap();
-
-        assert_eq!(loaded.n_trials(), 5);
-    }
-
-    #[test]
-    fn test_study_serde_empty() {
-        // Test serializing a study with no trials
-        let study: Study<f64> = Study::new(Direction::Minimize);
-
-        let json = serde_json::to_string(&study).unwrap();
-        let loaded: Study<f64> = serde_json::from_str(&json).unwrap();
-
-        assert_eq!(loaded.n_trials(), 0);
-        assert_eq!(loaded.direction(), Direction::Minimize);
-        assert!(loaded.best_trial().is_err());
-    }
-
-    #[test]
-    fn test_study_serde_with_custom_value_type() {
-        // Test Study with i32 value type
-        let study: Study<i32> = Study::new(Direction::Minimize);
-
-        study
-            .optimize(5, |trial| {
-                let n = trial.suggest_int("n", 1, 100)?;
-                Ok::<_, TpeError>(n as i32)
-            })
-            .unwrap();
-
-        let json = serde_json::to_string(&study).unwrap();
-        let loaded: Study<i32> = serde_json::from_str(&json).unwrap();
-
-        assert_eq!(loaded.n_trials(), 5);
-        let best = loaded.best_trial().unwrap();
-        assert!(best.value >= 1 && best.value <= 100);
-    }
-
-    #[test]
-    fn test_completed_trial_access_after_serde() {
-        let study: Study<f64> = Study::new(Direction::Minimize);
-
-        study
-            .optimize(3, |trial| {
-                let x = trial.suggest_float("x", 0.0, 10.0)?;
-                Ok::<_, TpeError>(x * x)
-            })
-            .unwrap();
-
-        let json = serde_json::to_string(&study).unwrap();
-        let loaded: Study<f64> = serde_json::from_str(&json).unwrap();
-
-        // Access all trials
-        let trials = loaded.trials();
-        assert_eq!(trials.len(), 3);
-
-        for trial in &trials {
-            assert!(trial.params.contains_key("x"));
-            assert!(trial.distributions.contains_key("x"));
-            assert!(trial.value >= 0.0); // x^2 is non-negative
-        }
-    }
-}