chore: release v0.2.0

.github/workflows/ci.yml

@@ -195,27 +195,6 @@ 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
@@ -249,7 +228,6 @@ jobs:
       - semver
       - minimal-versions
       - cross-platform
-      - cross-targets
       - typos
     steps:
       - uses: actions/checkout@v6

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "optimizer"
-version = "0.3.0"
+version = "0.2.0"
 edition = "2024"
 rust-version = "1.88"
 license = "MIT"

src/error.rs

@@ -38,28 +38,7 @@ 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> = core::result::Result<T, TpeError>;
+pub type Result<T> = std::result::Result<T, TpeError>;

src/kde.rs

@@ -5,8 +5,6 @@
 
 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
@@ -30,7 +28,7 @@ use crate::error::{Result, TpeError};
 /// let sample = kde.sample(&mut rng);
 /// ```
 #[derive(Clone, Debug)]
-pub(crate) struct KernelDensityEstimator {
+pub struct KernelDensityEstimator {
     /// The sample points used to construct the KDE.
     samples: Vec<f64>,
     /// The bandwidth (standard deviation) of the Gaussian kernels.
@@ -40,45 +38,37 @@ pub(crate) 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.
     ///
-    /// # Errors
+    /// # Panics
     ///
-    /// Returns `TpeError::EmptySamples` if `samples` is empty.
+    /// Panics if `samples` is empty.
-    pub(crate) fn new(samples: Vec<f64>) -> Result<Self> {
+    pub fn new(samples: Vec<f64>) -> Self {
-        if samples.is_empty() {
+        assert!(!samples.is_empty(), "KDE requires at least one sample");
-            return Err(TpeError::EmptySamples);
-        }
 
         let bandwidth = Self::scotts_rule(&samples);
-        Ok(Self { samples, bandwidth })
+        Self { samples, bandwidth }
     }
 
     /// Creates a new KDE with a specified bandwidth.
     ///
     /// Use this when you want explicit control over the smoothing parameter.
     ///
-    /// # Errors
+    /// # Panics
     ///
-    /// Returns `TpeError::EmptySamples` if `samples` is empty.
+    /// Panics if `samples` is empty or `bandwidth` is not positive.
-    /// Returns `TpeError::InvalidBandwidth` if `bandwidth` is not positive.
+    pub(crate) fn with_bandwidth(samples: Vec<f64>, bandwidth: f64) -> Self {
-    pub(crate) fn with_bandwidth(samples: Vec<f64>, bandwidth: f64) -> Result<Self> {
+        assert!(!samples.is_empty(), "KDE requires at least one sample");
-        if samples.is_empty() {
+        assert!(bandwidth > 0.0, "Bandwidth must be positive");
-            return Err(TpeError::EmptySamples);
-        }
-        if bandwidth <= 0.0 {
-            return Err(TpeError::InvalidBandwidth(bandwidth));
-        }
 
-        Ok(Self { samples, bandwidth })
+        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);
@@ -93,7 +83,6 @@ 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;
@@ -106,14 +95,13 @@ 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.
-    #[allow(clippy::cast_precision_loss)]
+    pub fn pdf(&self, x: f64) -> f64 {
-    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 * core::f64::consts::PI).sqrt();
+        let normalization = inv_bandwidth / (2.0 * std::f64::consts::PI).sqrt();
 
         let density: f64 = self
             .samples
@@ -132,7 +120,7 @@ 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(crate) fn sample<R: Rng>(&self, rng: &mut R) -> f64 {
+    pub 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];
@@ -142,7 +130,7 @@ impl KernelDensityEstimator {
         let u1: f64 = rng.random();
         let u2: f64 = rng.random();
 
-        let z = (-2.0 * u1.ln()).sqrt() * (2.0 * core::f64::consts::PI * u2).cos();
+        let z = (-2.0 * u1.ln()).sqrt() * (2.0 * std::f64::consts::PI * u2).cos();
         center + z * self.bandwidth
     }
 
@@ -160,7 +148,7 @@ mod tests {
     #[test]
     fn test_kde_pdf_basic() {
         let samples = vec![0.0, 1.0, 2.0];
-        let kde = KernelDensityEstimator::new(samples).unwrap();
+        let kde = KernelDensityEstimator::new(samples);
 
         // Density should be positive everywhere
         assert!(kde.pdf(0.0) > 0.0);
@@ -176,17 +164,17 @@ 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).unwrap();
+        let kde = KernelDensityEstimator::new(samples);
 
         // Numerical integration over a wide range
         let n_points = 10000;
         let low = -10.0;
         let high = 15.0;
-        let dx = (high - low) / f64::from(n_points);
+        let dx = (high - low) / n_points as f64;
 
         let integral: f64 = (0..n_points)
             .map(|i| {
-                let x = low + (f64::from(i) + 0.5) * dx;
+                let x = low + (i as f64 + 0.5) * dx;
                 kde.pdf(x) * dx
             })
             .sum();
@@ -201,16 +189,16 @@ 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).unwrap();
+        let kde = KernelDensityEstimator::with_bandwidth(samples, 0.5);
 
-        assert!((kde.bandwidth() - 0.5).abs() < f64::EPSILON);
+        assert_eq!(kde.bandwidth(), 0.5);
         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).unwrap();
+        let kde = KernelDensityEstimator::new(samples);
         let mut rng = rand::rng();
 
         // Samples should generally be in a reasonable range around the data
@@ -225,7 +213,7 @@ mod tests {
     #[test]
     fn test_kde_single_sample() {
         let samples = vec![5.0];
-        let kde = KernelDensityEstimator::new(samples).unwrap();
+        let kde = KernelDensityEstimator::new(samples);
 
         // Should have positive density near the sample
         assert!(kde.pdf(5.0) > 0.0);
@@ -235,7 +223,7 @@ mod tests {
     #[test]
     fn test_kde_identical_samples() {
         let samples = vec![3.0, 3.0, 3.0, 3.0];
-        let kde = KernelDensityEstimator::new(samples).unwrap();
+        let kde = KernelDensityEstimator::new(samples);
 
         // Should handle degenerate case with identical samples
         assert!(kde.bandwidth() > 0.0);
@@ -245,7 +233,7 @@ 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).unwrap();
+        let kde = KernelDensityEstimator::new(samples);
 
         // n = 10, n^(-1/5) ≈ 0.631
         // std_dev ≈ 2.87
@@ -258,23 +246,23 @@ mod tests {
     }
 
     #[test]
+    #[should_panic(expected = "KDE requires at least one sample")]
     fn test_kde_empty_samples() {
         let samples: Vec<f64> = vec![];
-        let result = KernelDensityEstimator::new(samples);
+        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];
-        let result = KernelDensityEstimator::with_bandwidth(samples, 0.0);
+        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];
-        let result = KernelDensityEstimator::with_bandwidth(samples, -1.0);
+        KernelDensityEstimator::with_bandwidth(samples, -1.0);
-        assert!(matches!(result, Err(TpeError::InvalidBandwidth(_))));
     }
 }

src/lib.rs

@@ -1,14 +1,3 @@
-#![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
@@ -22,11 +11,10 @@
 //! # Quick Start
 //!
 //! ```
-//! use optimizer::sampler::tpe::TpeSampler;
+//! use optimizer::{Direction, Study, TpeSampler};
-//! use optimizer::{Direction, Study};
 //!
 //! // Create a study with TPE sampler
-//! let sampler = TpeSampler::builder().seed(42).build().unwrap();
+//! let sampler = TpeSampler::builder().seed(42).build();
 //! let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 //!
 //! // Optimize x^2 for 20 trials
@@ -47,9 +35,7 @@
 //! A [`Study`] manages optimization trials. Create one with an optimization direction:
 //!
 //! ```
-//! use optimizer::sampler::random::RandomSampler;
+//! use optimizer::{Direction, RandomSampler, Study, TpeSampler};
-//! use optimizer::sampler::tpe::TpeSampler;
-//! use optimizer::{Direction, Study};
 //!
 //! // Minimize with default random sampler
 //! let study: Study<f64> = Study::new(Direction::Minimize);
@@ -93,18 +79,17 @@
 //!
 //! # Configuring TPE
 //!
-//! The [`sampler::tpe::TpeSampler`] can be configured using the builder pattern:
+//! The [`TpeSampler`] can be configured using the builder pattern:
 //!
 //! ```
-//! use optimizer::sampler::tpe::TpeSampler;
+//! use optimizer::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
@@ -135,13 +120,13 @@ mod distribution;
 mod error;
 mod kde;
 mod param;
-pub mod sampler;
+mod sampler;
 mod study;
 mod trial;
 mod types;
 
 pub use error::{Result, TpeError};
-pub use param::ParamValue;
+pub use sampler::{CompletedTrial, RandomSampler, Sampler, TpeSampler, TpeSamplerBuilder};
 pub use study::Study;
 pub use trial::Trial;
 pub use types::{Direction, TrialState};

src/sampler/mod.rs

@@ -1,10 +1,13 @@
 //! Sampler trait and implementations for parameter sampling.
 
-pub mod random;
+mod random;
 pub mod tpe;
 
 use std::collections::HashMap;
 
+pub use random::RandomSampler;
+pub use tpe::{TpeSampler, TpeSamplerBuilder};
+
 use crate::distribution::Distribution;
 use crate::param::ParamValue;
 

src/sampler/random.rs

@@ -17,7 +17,7 @@ use crate::sampler::{CompletedTrial, Sampler};
 /// # Examples
 ///
 /// ```
-/// use optimizer::sampler::random::RandomSampler;
+/// use optimizer::RandomSampler;
 ///
 /// // Create with default RNG
 /// let sampler = RandomSampler::new();
@@ -31,7 +31,6 @@ 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()),
@@ -41,7 +40,6 @@ 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)),
@@ -56,7 +54,6 @@ impl Default for RandomSampler {
 }
 
 impl Sampler for RandomSampler {
-    #[allow(clippy::cast_possible_truncation, clippy::cast_precision_loss)]
     fn sample(
         &self,
         distribution: &Distribution,
@@ -113,7 +110,6 @@ 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

@@ -9,7 +9,6 @@ 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};
@@ -28,7 +27,7 @@ use crate::sampler::{CompletedTrial, Sampler};
 /// # Examples
 ///
 /// ```
-/// use optimizer::sampler::tpe::TpeSampler;
+/// use optimizer::TpeSampler;
 ///
 /// // Create with default settings
 /// let sampler = TpeSampler::new();
@@ -39,8 +38,7 @@ 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).
@@ -60,10 +58,9 @@ 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,
@@ -79,17 +76,15 @@ impl TpeSampler {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::sampler::tpe::TpeSampler;
+    /// use optimizer::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()
     }
@@ -104,24 +99,22 @@ impl TpeSampler {
     /// * `kde_bandwidth` - Optional fixed bandwidth for KDE. If None, uses Scott's rule.
     /// * `seed` - Optional seed for reproducibility.
     ///
-    /// # Errors
+    /// # Panics
     ///
-    /// Returns `TpeError::InvalidGamma` if gamma is not in (0.0, 1.0).
+    /// Panics if gamma is not in (0.0, 1.0) or if kde_bandwidth is Some but not positive.
-    /// 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>,
-    ) -> Result<Self> {
+    ) -> Self {
-        if gamma <= 0.0 || gamma >= 1.0 {
+        assert!(
-            return Err(TpeError::InvalidGamma(gamma));
+            gamma > 0.0 && gamma < 1.0,
-        }
+            "gamma must be in (0.0, 1.0), got {gamma}"
-        if let Some(bw) = kde_bandwidth
+        );
-            && bw <= 0.0
+        if let Some(bw) = kde_bandwidth {
-        {
+            assert!(bw > 0.0, "kde_bandwidth must be positive, got {bw}");
-            return Err(TpeError::InvalidBandwidth(bw));
         }
 
         let rng = match seed {
@@ -129,24 +122,19 @@ impl TpeSampler {
             None => StdRng::from_os_rng(),
         };
 
-        Ok(Self {
+        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],
@@ -161,7 +149,7 @@ impl TpeSampler {
             history[a]
                 .value
                 .partial_cmp(&history[b].value)
-                .unwrap_or(core::cmp::Ordering::Equal)
+                .unwrap_or(std::cmp::Ordering::Equal)
         });
 
         // Calculate the split point (gamma quantile)
@@ -183,11 +171,6 @@ 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) => {
@@ -258,11 +241,6 @@ 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;
@@ -311,19 +289,15 @@ impl TpeSampler {
     }
 
     /// Samples using TPE for integer distributions.
-    #[allow(
+    #[allow(clippy::too_many_arguments)]
-        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: &[i64],
+        good_values: Vec<i64>,
-        bad_values: &[i64],
+        bad_values: Vec<i64>,
         rng: &mut StdRng,
     ) -> i64 {
         // Convert to floats for KDE
@@ -357,24 +331,23 @@ 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: &[usize],
+        good_indices: Vec<usize>,
-        bad_indices: &[usize],
+        bad_indices: Vec<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;
             }
@@ -422,15 +395,14 @@ impl Default for TpeSampler {
 /// # Examples
 ///
 /// ```
-/// use optimizer::sampler::tpe::TpeSamplerBuilder;
+/// use optimizer::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 {
@@ -446,11 +418,10 @@ 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,
@@ -470,23 +441,24 @@ 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::sampler::tpe::TpeSamplerBuilder;
+    /// use optimizer::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
     }
@@ -504,14 +476,12 @@ impl TpeSamplerBuilder {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::sampler::tpe::TpeSamplerBuilder;
+    /// use optimizer::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
@@ -530,14 +500,12 @@ impl TpeSamplerBuilder {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::sampler::tpe::TpeSamplerBuilder;
+    /// use optimizer::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
@@ -555,23 +523,24 @@ impl TpeSamplerBuilder {
     ///
     /// * `bandwidth` - The fixed bandwidth (standard deviation) for Gaussian kernels.
     ///
+    /// # Panics
+    ///
+    /// Panics if bandwidth is not positive.
+    ///
     /// # Examples
     ///
     /// ```
-    /// use optimizer::sampler::tpe::TpeSamplerBuilder;
+    /// use optimizer::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
     }
@@ -585,14 +554,12 @@ impl TpeSamplerBuilder {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::sampler::tpe::TpeSamplerBuilder;
+    /// use optimizer::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
@@ -600,25 +567,19 @@ 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::sampler::tpe::TpeSamplerBuilder;
+    /// use optimizer::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) -> Result<TpeSampler> {
+    pub fn build(self) -> TpeSampler {
         TpeSampler::with_config(
             self.gamma,
             self.n_startup_trials,
@@ -636,7 +597,6 @@ impl Default for TpeSamplerBuilder {
 }
 
 impl Sampler for TpeSampler {
-    #[allow(clippy::too_many_lines)]
     fn sample(
         &self,
         distribution: &Distribution,
@@ -733,8 +693,8 @@ impl Sampler for TpeSampler {
                     d.high,
                     d.log_scale,
                     d.step,
-                    &good_values,
+                    good_values,
-                    &bad_values,
+                    bad_values,
                     &mut rng,
                 );
                 ParamValue::Int(value)
@@ -765,7 +725,7 @@ 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)
             }
         }
@@ -773,11 +733,6 @@ impl Sampler for TpeSampler {
 }
 
 #[cfg(test)]
-#[allow(
-    clippy::similar_names,
-    clippy::cast_sign_loss,
-    clippy::cast_precision_loss
-)]
 mod tests {
     use std::collections::HashMap;
 
@@ -801,34 +756,34 @@ mod tests {
     #[test]
     fn test_tpe_sampler_new() {
         let sampler = TpeSampler::new();
-        assert!((sampler.gamma - 0.25).abs() < f64::EPSILON);
+        assert_eq!(sampler.gamma, 0.25);
         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)).unwrap();
+        let sampler = TpeSampler::with_config(0.15, 20, 32, None, Some(42));
-        assert!((sampler.gamma - 0.15).abs() < f64::EPSILON);
+        assert_eq!(sampler.gamma, 0.15);
         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() {
-        let result = TpeSampler::with_config(0.0, 10, 24, None, None);
+        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() {
-        let result = TpeSampler::with_config(1.0, 10, 24, None, None);
+        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)).unwrap();
+        let sampler = TpeSampler::with_config(0.25, 10, 24, None, Some(42));
         let dist = Distribution::Float(FloatDistribution {
             low: 0.0,
             high: 1.0,
@@ -851,7 +806,7 @@ mod tests {
 
     #[test]
     fn test_tpe_split_trials() {
-        let sampler = TpeSampler::with_config(0.25, 10, 24, None, Some(42)).unwrap();
+        let sampler = TpeSampler::with_config(0.25, 10, 24, None, Some(42));
 
         let dist = Distribution::Float(FloatDistribution {
             low: 0.0,
@@ -865,8 +820,8 @@ mod tests {
             .map(|i| {
                 create_trial(
                     i as u64,
-                    f64::from(i),
+                    i as f64,
-                    vec![("x", ParamValue::Float(f64::from(i) / 20.0), dist.clone())],
+                    vec![("x", ParamValue::Float(i as f64 / 20.0), dist.clone())],
                 )
             })
             .collect();
@@ -885,7 +840,7 @@ mod tests {
 
     #[test]
     fn test_tpe_samples_float_with_history() {
-        let sampler = TpeSampler::with_config(0.25, 5, 24, None, Some(42)).unwrap();
+        let sampler = TpeSampler::with_config(0.25, 5, 24, None, Some(42));
 
         let dist = Distribution::Float(FloatDistribution {
             low: 0.0,
@@ -897,7 +852,7 @@ mod tests {
         // Create history where low values (near 0.2) are "good"
         let history: Vec<CompletedTrial> = (0..20)
             .map(|i| {
-                let x = f64::from(i) / 20.0;
+                let x = i as f64 / 20.0;
                 // Objective is (x - 0.2)^2, minimized at x=0.2
                 let value = (x - 0.2).powi(2);
                 create_trial(
@@ -927,7 +882,7 @@ mod tests {
 
     #[test]
     fn test_tpe_categorical_sampling() {
-        let sampler = TpeSampler::with_config(0.25, 5, 24, None, Some(42)).unwrap();
+        let sampler = TpeSampler::with_config(0.25, 5, 24, None, Some(42));
 
         let dist = Distribution::Categorical(CategoricalDistribution { n_choices: 4 });
 
@@ -967,7 +922,7 @@ mod tests {
 
     #[test]
     fn test_tpe_int_sampling() {
-        let sampler = TpeSampler::with_config(0.25, 5, 24, None, Some(42)).unwrap();
+        let sampler = TpeSampler::with_config(0.25, 5, 24, None, Some(42));
 
         let dist = Distribution::Int(IntDistribution {
             low: 0,
@@ -1013,14 +968,14 @@ mod tests {
             .map(|i| {
                 create_trial(
                     i as u64,
-                    f64::from(i),
+                    i as f64,
-                    vec![("x", ParamValue::Float(f64::from(i) / 20.0), dist.clone())],
+                    vec![("x", ParamValue::Float(i as f64 / 20.0), dist.clone())],
                 )
             })
             .collect();
 
-        let sampler1 = TpeSampler::with_config(0.25, 5, 24, None, Some(12345)).unwrap();
+        let sampler1 = TpeSampler::with_config(0.25, 5, 24, None, Some(12345));
-        let sampler2 = TpeSampler::with_config(0.25, 5, 24, None, Some(12345)).unwrap();
+        let sampler2 = TpeSampler::with_config(0.25, 5, 24, None, Some(12345));
 
         for i in 0..10 {
             let v1 = sampler1.sample(&dist, i, &history);
@@ -1032,8 +987,8 @@ mod tests {
     #[test]
     fn test_tpe_sampler_builder_default() {
         let builder = TpeSamplerBuilder::new();
-        let sampler = builder.build().unwrap();
+        let sampler = builder.build();
-        assert!((sampler.gamma - 0.25).abs() < f64::EPSILON);
+        assert_eq!(sampler.gamma, 0.25);
         assert_eq!(sampler.n_startup_trials, 10);
         assert_eq!(sampler.n_ei_candidates, 24);
     }
@@ -1045,9 +1000,8 @@ mod tests {
             .n_startup_trials(20)
             .n_ei_candidates(32)
             .seed(42)
-            .build()
+            .build();
-            .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);
     }
@@ -1058,9 +1012,8 @@ mod tests {
             .gamma(0.10)
             .n_startup_trials(15)
             .n_ei_candidates(48)
-            .build()
+            .build();
-            .unwrap();
+        assert_eq!(sampler.gamma, 0.10);
-        assert!((sampler.gamma - 0.10).abs() < f64::EPSILON);
         assert_eq!(sampler.n_startup_trials, 15);
         assert_eq!(sampler.n_ei_candidates, 48);
     }
@@ -1068,16 +1021,16 @@ mod tests {
     #[test]
     fn test_tpe_sampler_builder_partial() {
         // Test setting only some options
-        let sampler = TpeSamplerBuilder::new().gamma(0.20).build().unwrap();
+        let sampler = TpeSamplerBuilder::new().gamma(0.20).build();
-        assert!((sampler.gamma - 0.20).abs() < f64::EPSILON);
+        assert_eq!(sampler.gamma, 0.20);
         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() {
-        let result = TpeSamplerBuilder::new().gamma(1.5).build();
+        TpeSamplerBuilder::new().gamma(1.5).build();
-        assert!(matches!(result, Err(TpeError::InvalidGamma(_))));
     }
 
     #[test]
@@ -1089,12 +1042,12 @@ mod tests {
             step: None,
         });
 
-        let history: Vec<CompletedTrial> = (0..20u32)
+        let history: Vec<CompletedTrial> = (0..20)
             .map(|i| {
                 create_trial(
-                    u64::from(i),
+                    i as u64,
-                    f64::from(i),
+                    i as f64,
-                    vec![("x", ParamValue::Float(f64::from(i) / 20.0), dist.clone())],
+                    vec![("x", ParamValue::Float(i as f64 / 20.0), dist.clone())],
                 )
             })
             .collect();
@@ -1102,13 +1055,11 @@ 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

@@ -1,15 +1,14 @@
 //! Study implementation for managing optimization trials.
 
 #[cfg(feature = "async")]
-use core::future::Future;
+use std::future::Future;
-use core::ops::ControlFlow;
+use std::ops::ControlFlow;
-use core::sync::atomic::{AtomicU64, Ordering};
 use std::sync::Arc;
+use std::sync::atomic::{AtomicU64, Ordering};
 
 use parking_lot::RwLock;
 
-use crate::sampler::random::RandomSampler;
+use crate::sampler::{CompletedTrial, RandomSampler, Sampler};
-use crate::sampler::{CompletedTrial, Sampler};
 use crate::trial::Trial;
 use crate::types::Direction;
 
@@ -65,7 +64,6 @@ 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())
     }
@@ -80,8 +78,7 @@ where
     /// # Examples
     ///
     /// ```
-    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, RandomSampler, Study};
-    /// use optimizer::{Direction, Study};
     ///
     /// let sampler = RandomSampler::with_seed(42);
     /// let study: Study<f64> = Study::with_sampler(Direction::Maximize, sampler);
@@ -110,8 +107,7 @@ where
     /// # Examples
     ///
     /// ```
-    /// use optimizer::sampler::tpe::TpeSampler;
+    /// use optimizer::{Direction, Study, TpeSampler};
-    /// use optimizer::{Direction, Study};
     ///
     /// let mut study: Study<f64> = Study::new(Direction::Minimize);
     /// study.set_sampler(TpeSampler::new());
@@ -317,15 +313,17 @@ where
                 match self.direction {
                     Direction::Minimize => {
                         // Reverse ordering: smaller values are "greater" for max_by
-                        ordering.map_or(core::cmp::Ordering::Equal, core::cmp::Ordering::reverse)
+                        ordering
+                            .map(|o| o.reverse())
+                            .unwrap_or(std::cmp::Ordering::Equal)
                     }
                     Direction::Maximize => {
                         // Normal ordering: larger values are "greater" for max_by
-                        ordering.unwrap_or(core::cmp::Ordering::Equal)
+                        ordering.unwrap_or(std::cmp::Ordering::Equal)
                     }
                 }
             })
-            .ok_or(crate::TpeError::NoCompletedTrials)?;
+            .expect("trials is not empty");
 
         Ok(best.clone())
     }
@@ -392,8 +390,7 @@ where
     /// # Examples
     ///
     /// ```
-    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, RandomSampler, Study};
-    /// use optimizer::{Direction, Study};
     ///
     /// // Minimize x^2
     /// let sampler = RandomSampler::with_seed(42);
@@ -413,7 +410,7 @@ where
     /// ```
     pub fn optimize<F, E>(&self, n_trials: usize, mut objective: F) -> crate::Result<()>
     where
-        F: FnMut(&mut Trial) -> core::result::Result<V, E>,
+        F: FnMut(&mut Trial) -> std::result::Result<V, E>,
         E: ToString,
     {
         for _ in 0..n_trials {
@@ -460,8 +457,7 @@ where
     /// # Examples
     ///
     /// ```
-    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, RandomSampler, Study};
-    /// use optimizer::{Direction, Study};
     ///
     /// # #[cfg(feature = "async")]
     /// # async fn example() -> optimizer::Result<()> {
@@ -491,7 +487,7 @@ where
     ) -> crate::Result<()>
     where
         F: Fn(Trial) -> Fut,
-        Fut: Future<Output = core::result::Result<(Trial, V), E>>,
+        Fut: Future<Output = std::result::Result<(Trial, V), E>>,
         E: ToString,
     {
         for _ in 0..n_trials {
@@ -537,13 +533,11 @@ 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::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, RandomSampler, Study};
-    /// use optimizer::{Direction, Study};
     ///
     /// # #[cfg(feature = "async")]
     /// # async fn example() -> optimizer::Result<()> {
@@ -574,7 +568,7 @@ where
     ) -> crate::Result<()>
     where
         F: Fn(Trial) -> Fut + Send + Sync + 'static,
-        Fut: Future<Output = core::result::Result<(Trial, V), E>> + Send,
+        Fut: Future<Output = std::result::Result<(Trial, V), E>> + Send,
         E: ToString + Send + 'static,
         V: Send + 'static,
     {
@@ -586,11 +580,7 @@ where
         let mut handles = Vec::with_capacity(n_trials);
 
         for _ in 0..n_trials {
-            let permit = semaphore
+            let permit = semaphore.clone().acquire_owned().await.unwrap();
-                .clone()
-                .acquire_owned()
-                .await
-                .map_err(|e| crate::TpeError::TaskError(e.to_string()))?;
             let trial = self.create_trial();
             let objective = Arc::clone(&objective);
 
@@ -605,10 +595,7 @@ where
 
         // Wait for all tasks and record results
         for handle in handles {
-            match handle
+            match handle.await.unwrap() {
-                .await
-                .map_err(|e| crate::TpeError::TaskError(e.to_string()))?
-            {
                 Ok((trial, value)) => {
                     self.complete_trial(trial, value);
                 }
@@ -645,15 +632,13 @@ 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::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, RandomSampler, Study};
-    /// use optimizer::{Direction, Study};
     ///
     /// // Stop early when we find a good enough value
     /// let sampler = RandomSampler::with_seed(42);
@@ -688,7 +673,7 @@ where
     ) -> crate::Result<()>
     where
         V: Clone,
-        F: FnMut(&mut Trial) -> core::result::Result<V, E>,
+        F: FnMut(&mut Trial) -> std::result::Result<V, E>,
         C: FnMut(&Study<V>, &CompletedTrial<V>) -> ControlFlow<()>,
         E: ToString,
     {
@@ -701,11 +686,7 @@ where
 
                     // Get the just-completed trial for the callback
                     let trials = self.completed_trials.read();
-                    let Some(completed) = trials.last() else {
+                    let completed = trials.last().expect("just added a trial");
-                        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
@@ -746,8 +727,7 @@ impl Study<f64> {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, RandomSampler, Study};
-    /// use optimizer::{Direction, Study};
     ///
     /// // With a seeded sampler for reproducibility
     /// let sampler = RandomSampler::with_seed(42);
@@ -788,8 +768,7 @@ impl Study<f64> {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, RandomSampler, Study};
-    /// use optimizer::{Direction, Study};
     ///
     /// // Minimize x^2 with sampler integration
     /// let sampler = RandomSampler::with_seed(42);
@@ -811,7 +790,7 @@ impl Study<f64> {
         mut objective: F,
     ) -> crate::Result<()>
     where
-        F: FnMut(&mut Trial) -> core::result::Result<f64, E>,
+        F: FnMut(&mut Trial) -> std::result::Result<f64, E>,
         E: ToString,
     {
         for _ in 0..n_trials {
@@ -852,15 +831,13 @@ 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::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, RandomSampler, Study};
-    /// use optimizer::{Direction, Study};
     ///
     /// // Optimize with sampler integration and early stopping
     /// let sampler = RandomSampler::with_seed(42);
@@ -893,7 +870,7 @@ impl Study<f64> {
         mut callback: C,
     ) -> crate::Result<()>
     where
-        F: FnMut(&mut Trial) -> core::result::Result<f64, E>,
+        F: FnMut(&mut Trial) -> std::result::Result<f64, E>,
         C: FnMut(&Study<f64>, &CompletedTrial<f64>) -> ControlFlow<()>,
         E: ToString,
     {
@@ -906,11 +883,7 @@ impl Study<f64> {
 
                     // Get the just-completed trial for the callback
                     let trials = self.completed_trials.read();
-                    let Some(completed) = trials.last() else {
+                    let completed = trials.last().expect("just added a trial");
-                        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
@@ -958,8 +931,7 @@ impl Study<f64> {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, RandomSampler, Study};
-    /// use optimizer::{Direction, Study};
     ///
     /// # #[cfg(feature = "async")]
     /// # async fn example() -> optimizer::Result<()> {
@@ -989,7 +961,7 @@ impl Study<f64> {
     ) -> crate::Result<()>
     where
         F: Fn(Trial) -> Fut,
-        Fut: Future<Output = core::result::Result<(Trial, f64), E>>,
+        Fut: Future<Output = std::result::Result<(Trial, f64), E>>,
         E: ToString,
     {
         for _ in 0..n_trials {
@@ -1035,13 +1007,11 @@ 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::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, RandomSampler, Study};
-    /// use optimizer::{Direction, Study};
     ///
     /// # #[cfg(feature = "async")]
     /// # async fn example() -> optimizer::Result<()> {
@@ -1072,7 +1042,7 @@ impl Study<f64> {
     ) -> crate::Result<()>
     where
         F: Fn(Trial) -> Fut + Send + Sync + 'static,
-        Fut: Future<Output = core::result::Result<(Trial, f64), E>> + Send,
+        Fut: Future<Output = std::result::Result<(Trial, f64), E>> + Send,
         E: ToString + Send + 'static,
     {
         use tokio::sync::Semaphore;
@@ -1083,11 +1053,7 @@ impl Study<f64> {
         let mut handles = Vec::with_capacity(n_trials);
 
         for _ in 0..n_trials {
-            let permit = semaphore
+            let permit = semaphore.clone().acquire_owned().await.unwrap();
-                .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);
 
@@ -1102,10 +1068,7 @@ impl Study<f64> {
 
         // Wait for all tasks and record results
         for handle in handles {
-            match handle
+            match handle.await.unwrap() {
-                .await
-                .map_err(|e| crate::TpeError::TaskError(e.to_string()))?
-            {
                 Ok((trial, value)) => {
                     self.complete_trial(trial, value);
                 }

src/trial.rs

@@ -37,8 +37,8 @@ pub struct Trial {
     history: Option<Arc<RwLock<Vec<CompletedTrial<f64>>>>>,
 }
 
-impl core::fmt::Debug for Trial {
+impl std::fmt::Debug for Trial {
-    fn fmt(&self, f: &mut core::fmt::Formatter<'_>) -> core::fmt::Result {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
         f.debug_struct("Trial")
             .field("id", &self.id)
             .field("state", &self.state)
@@ -71,7 +71,6 @@ impl Trial {
     /// let trial = Trial::new(0);
     /// assert_eq!(trial.id(), 0);
     /// ```
-    #[must_use]
     pub fn new(id: u64) -> Self {
         Self {
             id,
@@ -111,7 +110,7 @@ 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) {
@@ -119,32 +118,28 @@ impl Trial {
             sampler.sample(distribution, self.id, &history_guard)
         } else {
             // Fallback to RandomSampler when no sampler is configured
-            use crate::sampler::random::RandomSampler;
+            use crate::sampler::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 +200,8 @@ 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).abs() < f64::EPSILON
+                && existing.low == low
-                && (existing.high - high).abs() < f64::EPSILON
+                && existing.high == high
                 && !existing.log_scale
                 && existing.step.is_none()
             {
@@ -225,10 +220,9 @@ impl Trial {
 
         // Sample using the sampler
         let dist = Distribution::Float(distribution);
-        let ParamValue::Float(value) = self.sample_value(&dist) else {
+        let value = match self.sample_value(&dist) {
-            return Err(TpeError::Internal(
+            ParamValue::Float(v) => v,
-                "Float distribution should return Float value",
+            _ => unreachable!("Float distribution should return Float value"),
-            ));
         };
 
         // Store distribution and value
@@ -243,7 +237,7 @@ 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.
     ///
@@ -302,8 +296,8 @@ 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).abs() < f64::EPSILON
+                && existing.low == low
-                && (existing.high - high).abs() < f64::EPSILON
+                && existing.high == high
                 && existing.log_scale
                 && existing.step.is_none()
             {
@@ -322,10 +316,9 @@ impl Trial {
 
         // Sample using the sampler (sampler handles log-scale transformation)
         let dist = Distribution::Float(distribution);
-        let ParamValue::Float(value) = self.sample_value(&dist) else {
+        let value = match self.sample_value(&dist) {
-            return Err(TpeError::Internal(
+            ParamValue::Float(v) => v,
-                "Float distribution should return Float value",
+            _ => unreachable!("Float distribution should return Float value"),
-            ));
         };
 
         // Store distribution and value
@@ -399,8 +392,8 @@ 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).abs() < f64::EPSILON
+                && existing.low == low
-                && (existing.high - high).abs() < f64::EPSILON
+                && existing.high == high
                 && !existing.log_scale
                 && existing.step == Some(step)
             {
@@ -419,10 +412,9 @@ impl Trial {
 
         // Sample using the sampler (sampler handles step-grid)
         let dist = Distribution::Float(distribution);
-        let ParamValue::Float(value) = self.sample_value(&dist) else {
+        let value = match self.sample_value(&dist) {
-            return Err(TpeError::Internal(
+            ParamValue::Float(v) => v,
-                "Float distribution should return Float value",
+            _ => unreachable!("Float distribution should return Float value"),
-            ));
         };
 
         // Store distribution and value
@@ -463,7 +455,6 @@ 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 {
@@ -504,10 +495,9 @@ impl Trial {
 
         // Sample using the sampler
         let dist = Distribution::Int(distribution);
-        let ParamValue::Int(value) = self.sample_value(&dist) else {
+        let value = match self.sample_value(&dist) {
-            return Err(TpeError::Internal(
+            ParamValue::Int(v) => v,
-                "Int distribution should return Int value",
+            _ => unreachable!("Int distribution should return Int value"),
-            ));
         };
 
         // Store distribution and value
@@ -522,7 +512,7 @@ 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.
     ///
@@ -551,7 +541,6 @@ 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);
@@ -596,10 +585,9 @@ impl Trial {
 
         // Sample using the sampler (sampler handles log-scale transformation)
         let dist = Distribution::Int(distribution);
-        let ParamValue::Int(value) = self.sample_value(&dist) else {
+        let value = match self.sample_value(&dist) {
-            return Err(TpeError::Internal(
+            ParamValue::Int(v) => v,
-                "Int distribution should return Int value",
+            _ => unreachable!("Int distribution should return Int value"),
-            ));
         };
 
         // Store distribution and value
@@ -650,7 +638,6 @@ impl Trial {
     ///     .unwrap();
     /// assert_eq!(n, n2);
     /// ```
-    #[allow(clippy::cast_precision_loss)]
     pub fn suggest_int_step(
         &mut self,
         name: impl Into<String>,
@@ -701,10 +688,9 @@ impl Trial {
 
         // Sample using the sampler (sampler handles step-grid)
         let dist = Distribution::Int(distribution);
-        let ParamValue::Int(value) = self.sample_value(&dist) else {
+        let value = match self.sample_value(&dist) {
-            return Err(TpeError::Internal(
+            ParamValue::Int(v) => v,
-                "Int distribution should return Int value",
+            _ => unreachable!("Int distribution should return Int value"),
-            ));
         };
 
         // Store distribution and value
@@ -788,10 +774,9 @@ impl Trial {
 
         // Sample using the sampler
         let dist = Distribution::Categorical(distribution);
-        let ParamValue::Categorical(index) = self.sample_value(&dist) else {
+        let index = match self.sample_value(&dist) {
-            return Err(TpeError::Internal(
+            ParamValue::Categorical(idx) => idx,
-                "Categorical distribution should return Categorical value",
+            _ => unreachable!("Categorical distribution should return Categorical value"),
-            ));
         };
 
         // Store distribution and value (store the index)

tests/async_tests.rs

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

tests/integration.rs

@@ -1,14 +1,6 @@
 //! Integration tests for the optimizer library.
 
-#![allow(
+use optimizer::{Direction, RandomSampler, Study, TpeError, TpeSampler, Trial};
-    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
@@ -22,8 +14,7 @@ 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);
 
@@ -49,11 +40,7 @@ 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()
+    let sampler = TpeSampler::builder().seed(123).n_startup_trials(10).build();
-        .seed(123)
-        .n_startup_trials(10)
-        .build()
-        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -79,11 +66,7 @@ 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()
+    let sampler = TpeSampler::builder().seed(456).n_startup_trials(5).build();
-        .seed(456)
-        .n_startup_trials(5)
-        .build()
-        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Maximize, sampler);
 
@@ -567,11 +550,7 @@ fn test_invalid_step_errors() {
 
 #[test]
 fn test_tpe_with_categorical_parameter() {
-    let sampler = TpeSampler::builder()
+    let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();
-        .seed(42)
-        .n_startup_trials(5)
-        .build()
-        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Maximize, sampler);
 
@@ -603,11 +582,7 @@ fn test_tpe_with_categorical_parameter() {
 
 #[test]
 fn test_tpe_with_integer_parameters() {
-    let sampler = TpeSampler::builder()
+    let sampler = TpeSampler::builder().seed(789).n_startup_trials(5).build();
-        .seed(789)
-        .n_startup_trials(5)
-        .build()
-        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -781,11 +756,7 @@ 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()
+    let tpe = TpeSampler::builder().seed(42).n_startup_trials(5).build();
-        .seed(42)
-        .n_startup_trials(5)
-        .build()
-        .unwrap();
     study.set_sampler(tpe);
 
     // Should work with the new sampler
@@ -892,10 +863,10 @@ fn test_trial_debug_format() {
 
 #[test]
 fn test_tpe_sampler_builder_default_trait() {
-    use optimizer::sampler::tpe::TpeSamplerBuilder;
+    use optimizer::TpeSamplerBuilder;
 
     let builder = TpeSamplerBuilder::default();
-    let sampler = builder.build().unwrap();
+    let sampler = builder.build();
 
     // Should have default values
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
@@ -930,8 +901,7 @@ 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);
 
@@ -947,9 +917,9 @@ fn test_tpe_with_fixed_kde_bandwidth() {
 }
 
 #[test]
+#[should_panic(expected = "kde_bandwidth must be positive")]
 fn test_tpe_sampler_invalid_kde_bandwidth() {
-    let result = TpeSampler::with_config(0.25, 10, 24, Some(-1.0), None);
+    TpeSampler::with_config(0.25, 10, 24, Some(-1.0), None);
-    assert!(matches!(result, Err(TpeError::InvalidBandwidth(_))));
 }
 
 #[test]
@@ -958,8 +928,7 @@ 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);
 
@@ -975,11 +944,7 @@ fn test_tpe_split_trials_with_two_trials() {
 
 #[test]
 fn test_tpe_with_log_scale_int() {
-    let sampler = TpeSampler::builder()
+    let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();
-        .seed(42)
-        .n_startup_trials(5)
-        .build()
-        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -997,11 +962,7 @@ fn test_tpe_with_log_scale_int() {
 
 #[test]
 fn test_tpe_with_step_distributions() {
-    let sampler = TpeSampler::builder()
+    let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();
-        .seed(42)
-        .n_startup_trials(5)
-        .build()
-        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -1079,8 +1040,7 @@ 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);