chore: release v0.3.0

Refactor
2026-01-30 19:21:42 +01:00 · 2026-01-30 19:21:35 +01:00
11 changed files with 434 additions and 234 deletions
@@ -1,6 +1,6 @@
 [package]
 name = "optimizer"
-version = "0.2.0"
+version = "0.3.0"
 edition = "2024"
 rust-version = "1.88"
 license = "MIT"
@@ -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>;
@@ -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.
-    pub fn new(samples: Vec<f64>) -> Self {
-        assert!(!samples.is_empty(), "KDE requires at least one sample");
+    /// Returns `TpeError::EmptySamples` if `samples` is empty.
+    pub(crate) fn new(samples: Vec<f64>) -> Result<Self> {
+        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.
-    pub(crate) fn with_bandwidth(samples: Vec<f64>, bandwidth: f64) -> Self {
-        assert!(!samples.is_empty(), "KDE requires at least one sample");
-        assert!(bandwidth > 0.0, "Bandwidth must be positive");
+    /// Returns `TpeError::EmptySamples` if `samples` is empty.
+    /// Returns `TpeError::InvalidBandwidth` if `bandwidth` is not positive.
+    pub(crate) fn with_bandwidth(samples: Vec<f64>, bandwidth: f64) -> Result<Self> {
+        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(_))));
    }
 }
@@ -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
@@ -11,10 +22,11 @@
 //! # 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
@@ -35,7 +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);
@@ -79,17 +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
@@ -120,13 +135,13 @@ 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};
@@ -1,13 +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;
-pub use tpe::{TpeSampler, TpeSamplerBuilder};
-
 use crate::distribution::Distribution;
 use crate::param::ParamValue;

@@ -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};
@@ -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_ei_candidates: 24 (evaluate 24 candidates per sample)
-    /// - kde_bandwidth: None (uses Scott's rule for automatic bandwidth)
+    /// - `n_startup_trials`: 10 (random sampling for first 10 trials)
+    /// - `n_ei_candidates`: 24 (evaluate 24 candidates per sample)
+    /// - `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 {
-        assert!(
-            gamma > 0.0 && gamma < 1.0,
-            "gamma must be in (0.0, 1.0), got {gamma}"
-        );
-        if let Some(bw) = kde_bandwidth {
-            assert!(bw > 0.0, "kde_bandwidth must be positive, got {bw}");
+    ) -> Result<Self> {
+        if gamma <= 0.0 || gamma >= 1.0 {
+            return Err(TpeError::InvalidGamma(gamma));
+        }
+        if let Some(bw) = kde_bandwidth
+            && bw <= 0.0
+        {
+            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>,
-        bad_values: Vec<i64>,
+        good_values: &[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>,
-        bad_indices: Vec<usize>,
+        good_indices: &[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_ei_candidates: 24 (evaluate 24 candidates per sample)
-    /// - kde_bandwidth: None (uses Scott's rule for automatic bandwidth)
+    /// - `n_startup_trials`: 10 (random sampling for first 10 trials)
+    /// - `n_ei_candidates`: 24 (evaluate 24 candidates per sample)
+    /// - `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,
-                    bad_values,
+                    &good_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));
-        assert_eq!(sampler.gamma, 0.15);
+        let sampler = TpeSampler::with_config(0.15, 20, 32, None, Some(42)).unwrap();
+        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,
-                    vec![("x", ParamValue::Float(i as f64 / 20.0), dist.clone())],
+                    f64::from(i),
+                    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,
-                    vec![("x", ParamValue::Float(i as f64 / 20.0), dist.clone())],
+                    f64::from(i),
+                    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 sampler2 = 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)).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();
-        assert_eq!(sampler.gamma, 0.25);
+        let sampler = builder.build().unwrap();
+        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();
-        assert_eq!(sampler.gamma, 0.15);
+            .build()
+            .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();
-        assert_eq!(sampler.gamma, 0.10);
+            .build()
+            .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();
-        assert_eq!(sampler.gamma, 0.20);
+        let sampler = TpeSamplerBuilder::new().gamma(0.20).build().unwrap();
+        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,
-                    i as f64,
-                    vec![("x", ParamValue::Float(i as f64 / 20.0), dist.clone())],
+                    u64::from(i),
+                    f64::from(i),
+                    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);
@@ -1,14 +1,15 @@
 //! Study implementation for managing optimization trials.

 #[cfg(feature = "async")]
-use std::future::Future;
-use std::ops::ControlFlow;
+use core::future::Future;
+use core::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::{CompletedTrial, RandomSampler, Sampler};
+use crate::sampler::random::RandomSampler;
+use crate::sampler::{CompletedTrial, Sampler};
 use crate::trial::Trial;
 use crate::types::Direction;

@@ -64,6 +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())
    }
@@ -78,7 +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);
@@ -107,7 +110,8 @@ where
    /// # Examples
    ///
    /// ```
-    /// use optimizer::{Direction, Study, TpeSampler};
+    /// use optimizer::sampler::tpe::TpeSampler;
+    /// use optimizer::{Direction, Study};
    ///
    /// let mut study: Study<f64> = Study::new(Direction::Minimize);
    /// study.set_sampler(TpeSampler::new());
@@ -313,17 +317,15 @@ where
                match self.direction {
                    Direction::Minimize => {
                        // Reverse ordering: smaller values are "greater" for max_by
-                        ordering
-                            .map(|o| o.reverse())
-                            .unwrap_or(std::cmp::Ordering::Equal)
+                        ordering.map_or(core::cmp::Ordering::Equal, core::cmp::Ordering::reverse)
                    }
                    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())
    }
@@ -390,7 +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);
@@ -410,7 +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 {
@@ -457,7 +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<()> {
@@ -487,7 +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 {
@@ -533,11 +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<()> {
@@ -568,7 +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,
    {
@@ -580,7 +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);

@@ -595,7 +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);
                }
@@ -632,13 +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);
@@ -673,7 +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,
    {
@@ -686,7 +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
@@ -727,7 +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);
@@ -768,7 +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);
@@ -790,7 +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 {
@@ -831,13 +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);
@@ -870,7 +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,
    {
@@ -883,7 +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
@@ -931,7 +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<()> {
@@ -961,7 +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 {
@@ -1007,11 +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<()> {
@@ -1042,7 +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;
@@ -1053,7 +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);

@@ -1068,7 +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);
                }
@@ -37,8 +37,8 @@ pub struct Trial {
    history: Option<Arc<RwLock<Vec<CompletedTrial<f64>>>>>,
 }

-impl std::fmt::Debug for Trial {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+impl core::fmt::Debug for Trial {
+    fn fmt(&self, f: &mut core::fmt::Formatter<'_>) -> core::fmt::Result {
        f.debug_struct("Trial")
            .field("id", &self.id)
            .field("state", &self.state)
@@ -71,6 +71,7 @@ impl Trial {
    /// let trial = Trial::new(0);
    /// assert_eq!(trial.id(), 0);
    /// ```
+    #[must_use]
    pub fn new(id: u64) -> Self {
        Self {
            id,
@@ -110,7 +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) {
@@ -118,28 +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
    }
@@ -200,8 +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.high == high
+                && (existing.low - low).abs() < f64::EPSILON
+                && (existing.high - high).abs() < f64::EPSILON
                && !existing.log_scale
                && existing.step.is_none()
            {
@@ -220,9 +225,10 @@ impl Trial {

        // Sample using the sampler
        let dist = Distribution::Float(distribution);
-        let value = match self.sample_value(&dist) {
-            ParamValue::Float(v) => v,
-            _ => unreachable!("Float distribution should return Float value"),
+        let ParamValue::Float(value) = self.sample_value(&dist) else {
+            return Err(TpeError::Internal(
+                "Float distribution should return Float value",
+            ));
        };

        // Store distribution and value
@@ -237,7 +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.
    ///
@@ -296,8 +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.high == high
+                && (existing.low - low).abs() < f64::EPSILON
+                && (existing.high - high).abs() < f64::EPSILON
                && existing.log_scale
                && existing.step.is_none()
            {
@@ -316,9 +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) {
-            ParamValue::Float(v) => v,
-            _ => unreachable!("Float distribution should return Float value"),
+        let ParamValue::Float(value) = self.sample_value(&dist) else {
+            return Err(TpeError::Internal(
+                "Float distribution should return Float value",
+            ));
        };

        // Store distribution and value
@@ -392,8 +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.high == high
+                && (existing.low - low).abs() < f64::EPSILON
+                && (existing.high - high).abs() < f64::EPSILON
                && !existing.log_scale
                && existing.step == Some(step)
            {
@@ -412,9 +419,10 @@ impl Trial {

        // Sample using the sampler (sampler handles step-grid)
        let dist = Distribution::Float(distribution);
-        let value = match self.sample_value(&dist) {
-            ParamValue::Float(v) => v,
-            _ => unreachable!("Float distribution should return Float value"),
+        let ParamValue::Float(value) = self.sample_value(&dist) else {
+            return Err(TpeError::Internal(
+                "Float distribution should return Float value",
+            ));
        };

        // Store distribution and value
@@ -455,6 +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 {
@@ -495,9 +504,10 @@ impl Trial {

        // Sample using the sampler
        let dist = Distribution::Int(distribution);
-        let value = match self.sample_value(&dist) {
-            ParamValue::Int(v) => v,
-            _ => unreachable!("Int distribution should return Int value"),
+        let ParamValue::Int(value) = self.sample_value(&dist) else {
+            return Err(TpeError::Internal(
+                "Int distribution should return Int value",
+            ));
        };

        // Store distribution and value
@@ -512,7 +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.
    ///
@@ -541,6 +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);
@@ -585,9 +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) {
-            ParamValue::Int(v) => v,
-            _ => unreachable!("Int distribution should return Int value"),
+        let ParamValue::Int(value) = self.sample_value(&dist) else {
+            return Err(TpeError::Internal(
+                "Int distribution should return Int value",
+            ));
        };

        // Store distribution and value
@@ -638,6 +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>,
@@ -688,9 +701,10 @@ impl Trial {

        // Sample using the sampler (sampler handles step-grid)
        let dist = Distribution::Int(distribution);
-        let value = match self.sample_value(&dist) {
-            ParamValue::Int(v) => v,
-            _ => unreachable!("Int distribution should return Int value"),
+        let ParamValue::Int(value) = self.sample_value(&dist) else {
+            return Err(TpeError::Internal(
+                "Int distribution should return Int value",
+            ));
        };

        // Store distribution and value
@@ -774,9 +788,10 @@ impl Trial {

        // Sample using the sampler
        let dist = Distribution::Categorical(distribution);
-        let index = match self.sample_value(&dist) {
-            ParamValue::Categorical(idx) => idx,
-            _ => unreachable!("Categorical distribution should return Categorical value"),
+        let ParamValue::Categorical(index) = self.sample_value(&dist) else {
+            return Err(TpeError::Internal(
+                "Categorical distribution should return Categorical value",
+            ));
        };

        // Store distribution and value (store the index)
@@ -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);

@@ -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);
Author	SHA1	Message	Date
Manuel Raimann	e44ab3669a	chore: release v0.3.0	2026-01-30 19:21:42 +01:00
Manuel Raimann	7a3f89360e	Refactor	2026-01-30 19:21:35 +01:00