chore: release v0.2.0

Remove Serde
fix: remove unused dependency 'ordered-float' from Cargo.toml
2026-01-30 18:40:03 +01:00 · 2026-01-30 18:38:43 +01:00 · 2026-01-30 18:32:14 +01:00
15 changed files with 367 additions and 2213 deletions
@@ -6,9 +6,6 @@ on:
  pull_request:
    branches: [main, master]

-permissions:
-  contents: read
-
 env:
  CARGO_TERM_COLOR: always

@@ -198,27 +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
@@ -252,7 +228,6 @@ jobs:
      - semver
      - minimal-versions
      - cross-platform
-      - cross-targets
      - typos
    steps:
      - uses: actions/checkout@v6
@@ -278,14 +253,7 @@ jobs:
          fi

      - name: Publish
-        if: steps.check.outputs.skip == 'false'
-        run: |
-          cargo publish --allow-dirty 2>&1 | tee publish_output.txt || {
-            if grep -q "already uploaded" publish_output.txt; then
-              echo "Version already published, skipping"
-              exit 0
-            fi
-            exit 1
-          }
+        if: steps.check.outputs.skip == 'false' && github.ref == 'refs/heads/master'
+        run: cargo publish
        env:
          CARGO_REGISTRY_TOKEN: ${{ secrets.CARGO_REGISTRY_TOKEN }}
@@ -5,9 +5,6 @@ on:
    - cron: "0 6 * * *" # Daily at 6:00 UTC
  workflow_dispatch: # Allow manual trigger

-permissions:
-  contents: read
-
 env:
  CARGO_TERM_COLOR: always

@@ -1,16 +1,13 @@
 [package]
 name = "optimizer"
-version = "0.3.1"
+version = "0.2.0"
 edition = "2024"
 rust-version = "1.88"
 license = "MIT"
 authors = ["Manuel Raimann <raimannma@outlook.de"]
 description = "A Rust library for optimization algorithms."
 repository = "https://github.com/raimannma/rust-optimizer"
-documentation = "https://docs.rs/optimizer"
-keywords = ["optimization", "hyperparameter", "tpe", "grid-search", "bayesian"]
-categories = ["algorithm", "science", "data-structures"]
-readme = "README.md"
+

 [dependencies]
 rand = "0.9"
@@ -1,6 +1,6 @@
 # optimizer

-A Rust library for black-box optimization with multiple sampling strategies.
+A Rust library for black-box optimization using Tree-Parzen Estimator (TPE).

 [![Docs](https://docs.rs/optimizer/badge.svg)](https://docs.rs/optimizer)
 [![Crates.io](https://img.shields.io/crates/v/optimizer.svg)](https://crates.io/crates/optimizer)
@@ -9,10 +9,6 @@ A Rust library for black-box optimization with multiple sampling strategies.
 ## Features

 - Optuna-like API for hyperparameter optimization
- Multiple sampling strategies:
-  - **Random Search** - Simple random sampling for baseline comparisons
-  - **TPE (Tree-Parzen Estimator)** - Bayesian optimization for efficient search
-  - **Grid Search** - Exhaustive search over a specified parameter grid
 - Float, integer, and categorical parameter types
 - Log-scale and stepped parameter sampling
 - Sync and async optimization with parallel trial evaluation
@@ -20,16 +16,15 @@ A Rust library for black-box optimization with multiple sampling strategies.
 ## Quick Start

 ```rust
-use optimizer::{Direction, Study};
-use optimizer::sampler::tpe::TpeSampler;
+use optimizer::{Direction, Study, TpeSampler};

-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);

 study
    .optimize_with_sampler(20, |trial| {
        let x = trial.suggest_float("x", -10.0, 10.0)?;
-        Ok::<_, optimizer::Error>(x * x)
+        Ok::<_, optimizer::TpeError>(x * x)
    })
    .unwrap();

@@ -37,50 +32,6 @@ let best = study.best_trial().unwrap();
 println!("Best value: {} at x={:?}", best.value, best.params);
 ```

-## Samplers
-
-### Random Search
-
-```rust
-use optimizer::{Direction, Study};
-use optimizer::sampler::random::RandomSampler;
-
-let study: Study<f64> = Study::with_sampler(
-    Direction::Minimize,
-    RandomSampler::with_seed(42),
-);
-```
-
-### TPE (Tree-Parzen Estimator)
-
-```rust
-use optimizer::{Direction, Study};
-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 kicks in
-    .n_ei_candidates(32)   // Candidates to evaluate
-    .seed(42)
-    .build()
-    .unwrap();
-
-let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
-```
-
-### Grid Search
-
-```rust
-use optimizer::{Direction, Study};
-use optimizer::sampler::grid::GridSearchSampler;
-
-let sampler = GridSearchSampler::builder()
-    .n_points_per_param(10)  // Number of points per parameter dimension
-    .build();
-
-let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
-```
-
 ## Feature Flags

 - `async` - Enable async optimization methods (requires tokio)
@@ -1,5 +1,10 @@
-#[derive(Debug, thiserror::Error)]
-pub enum Error {
+//! Error types for the optimizer library.
+
+use thiserror::Error;
+
+/// The error type for TPE operations.
+#[derive(Debug, Error)]
+pub enum TpeError {
    /// Returned when the lower bound is greater than the upper bound.
    #[error("invalid bounds: low ({low}) must be less than or equal to high ({high})")]
    InvalidBounds {
@@ -33,27 +38,7 @@ pub enum Error {
    /// 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),
 }

-pub type Result<T> = core::result::Result<T, Error>;
+/// A specialized Result type for TPE operations.
+pub type Result<T> = std::result::Result<T, TpeError>;
@@ -5,8 +5,6 @@

 use rand::Rng;

-use crate::error::{Error, Result};
-
 /// 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::{Error, Result};
 /// 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 `Error::EmptySamples` if `samples` is empty.
-    pub(crate) fn new(samples: Vec<f64>) -> Result<Self> {
-        if samples.is_empty() {
-            return Err(Error::EmptySamples);
-        }
+    /// Panics if `samples` is empty.
+    pub fn new(samples: Vec<f64>) -> Self {
+        assert!(!samples.is_empty(), "KDE requires at least one sample");

        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 `Error::EmptySamples` if `samples` is empty.
-    /// Returns `Error::InvalidBandwidth` if `bandwidth` is not positive.
-    pub(crate) fn with_bandwidth(samples: Vec<f64>, bandwidth: f64) -> Result<Self> {
-        if samples.is_empty() {
-            return Err(Error::EmptySamples);
-        }
-        if bandwidth <= 0.0 {
-            return Err(Error::InvalidBandwidth(bandwidth));
-        }
+    /// 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");

-        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(crate) fn pdf(&self, x: f64) -> f64 {
+    pub 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);
-        assert!(matches!(result, Err(Error::EmptySamples)));
+        KernelDensityEstimator::new(samples);
    }

    #[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);
-        assert!(matches!(result, Err(Error::InvalidBandwidth(_))));
+        KernelDensityEstimator::with_bandwidth(samples, 0.0);
    }

    #[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);
-        assert!(matches!(result, Err(Error::InvalidBandwidth(_))));
+        KernelDensityEstimator::with_bandwidth(samples, -1.0);
    }
 }
@@ -1,24 +1,7 @@
-#![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 black-box optimization library with multiple sampling strategies.
+//! A Tree-Parzen Estimator (TPE) library for black-box optimization.
 //!
 //! This library provides an Optuna-like API for hyperparameter optimization
-//! with support for multiple sampling algorithms:
-//!
-//! - **Random Search** - Simple random sampling for baseline comparisons
-//! - **TPE (Tree-Parzen Estimator)** - Bayesian optimization for efficient search
-//! - **Grid Search** - Exhaustive search over a specified parameter grid
-//!
-//! Additional features include:
+//! using the Tree-Parzen Estimator algorithm. It supports:
 //!
 //! - Float, integer, and categorical parameter types
 //! - Log-scale and stepped parameter sampling
@@ -28,18 +11,17 @@
 //! # Quick Start
 //!
 //! ```
-//! use optimizer::sampler::tpe::TpeSampler;
-//! use optimizer::{Direction, Study};
+//! use optimizer::{Direction, Study, TpeSampler};
 //!
 //! // 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
 //! study
 //!     .optimize_with_sampler(20, |trial| {
 //!         let x = trial.suggest_float("x", -10.0, 10.0)?;
-//!         Ok::<_, optimizer::Error>(x * x)
+//!         Ok::<_, optimizer::TpeError>(x * x)
 //!     })
 //!     .unwrap();
 //!
@@ -53,9 +35,7 @@
 //! A [`Study`] manages optimization trials. Create one with an optimization direction:
 //!
 //! ```
-//! use optimizer::sampler::random::RandomSampler;
-//! use optimizer::sampler::tpe::TpeSampler;
-//! use optimizer::{Direction, Study};
+//! use optimizer::{Direction, RandomSampler, Study, TpeSampler};
 //!
 //! // Minimize with default random sampler
 //! let study: Study<f64> = Study::new(Direction::Minimize);
@@ -92,53 +72,24 @@
 //!         let optimizer = trial.suggest_categorical("optimizer", &["sgd", "adam", "rmsprop"])?;
 //!
 //!         // Return objective value
-//!         Ok::<_, optimizer::Error>(x * n as f64)
+//!         Ok::<_, optimizer::TpeError>(x * n as f64)
 //!     })
 //!     .unwrap();
 //! ```
 //!
-//! # Available Samplers
+//! # Configuring TPE
 //!
-//! ## Random Search
-//!
-//! The simplest sampling strategy, useful for baselines:
+//! The [`TpeSampler`] can be configured using the builder pattern:
 //!
 //! ```
-//! use optimizer::sampler::random::RandomSampler;
-//! use optimizer::{Direction, Study};
-//!
-//! let study: Study<f64> = Study::with_sampler(Direction::Minimize, RandomSampler::with_seed(42));
-//! ```
-//!
-//! ## TPE (Tree-Parzen Estimator)
-//!
-//! Bayesian optimization that learns from previous trials:
-//!
-//! ```
-//! 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()
-//!     .unwrap();
-//! ```
-//!
-//! ## Grid Search
-//!
-//! Exhaustive search over a discretized parameter space:
-//!
-//! ```
-//! use optimizer::sampler::grid::GridSearchSampler;
-//! use optimizer::{Direction, Study};
-//!
-//! let sampler = GridSearchSampler::builder()
-//!     .n_points_per_param(10)  // Points per parameter dimension
 //!     .build();
-//!
-//! let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 //! ```
 //!
 //! # Async and Parallel Optimization
@@ -169,13 +120,13 @@ mod distribution;
 mod error;
 mod kde;
 mod param;
-pub mod sampler;
+mod sampler;
 mod study;
 mod trial;
 mod types;

-pub use error::{Error, Result};
-pub use param::ParamValue;
+pub use error::{Result, TpeError};
+pub use sampler::{CompletedTrial, RandomSampler, Sampler, TpeSampler, TpeSamplerBuilder};
 pub use study::Study;
-pub use trial::{SuggestableRange, Trial};
+pub use trial::Trial;
 pub use types::{Direction, TrialState};
@@ -1,11 +1,13 @@
 //! Sampler trait and implementations for parameter sampling.

-pub mod grid;
-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;

@@ -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};
@@ -9,7 +9,6 @@ use rand::rngs::StdRng;
 use rand::{Rng, SeedableRng};

 use crate::distribution::Distribution;
-use crate::error::{Error, Result};
 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()
-///     .unwrap();
+///     .build();
 /// ```
 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_ei_candidates`: 24 (evaluate 24 candidates per sample)
-    /// - `kde_bandwidth`: None (uses Scott's rule for automatic bandwidth)
-    #[must_use]
+    /// - 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)
    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()
-    ///     .unwrap();
+    ///     .build();
    /// ```
-    #[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 `Error::InvalidGamma` if gamma is not in (0.0, 1.0).
-    /// Returns `Error::InvalidBandwidth` if `kde_bandwidth` is Some but not positive.
+    /// Panics if gamma is not in (0.0, 1.0) or 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> {
-        if gamma <= 0.0 || gamma >= 1.0 {
-            return Err(Error::InvalidGamma(gamma));
-        }
-        if let Some(bw) = kde_bandwidth
-            && bw <= 0.0
-        {
-            return Err(Error::InvalidBandwidth(bw));
+    ) -> 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}");
        }

        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(
-        clippy::too_many_arguments,
-        clippy::cast_precision_loss,
-        clippy::cast_possible_truncation
-    )]
+    #[allow(clippy::too_many_arguments)]
    fn sample_tpe_int(
        &self,
        low: i64,
        high: i64,
        log_scale: bool,
        step: Option<i64>,
-        good_values: &[i64],
-        bad_values: &[i64],
+        good_values: Vec<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],
-        bad_indices: &[usize],
+        good_indices: Vec<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()
-///     .unwrap();
+///     .build();
 /// ```
 #[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_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,
@@ -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()
-    ///     .unwrap();
+    ///     .build();
    /// ```
-    ///
-    /// # Note
-    ///
-    /// Validation happens at `build()` time. If gamma is not in (0.0, 1.0),
-    /// `build()` will return `Err(Error::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()
-    ///     .unwrap();
+    ///     .build();
    /// ```
-    #[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()
-    ///     .unwrap();
+    ///     .build();
    /// ```
-    #[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()
-    ///     .unwrap();
+    ///     .build();
    /// ```
-    ///
-    /// # Note
-    ///
-    /// Validation happens at `build()` time. If bandwidth is not positive,
-    /// `build()` will return `Err(Error::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()
-    ///     .unwrap();
+    ///     .build();
    /// ```
-    #[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 `Error::InvalidGamma` if gamma is not in (0.0, 1.0).
-    /// Returns `Error::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()
-    ///     .unwrap();
+    ///     .build();
    /// ```
-    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,
-                    &bad_values,
+                    good_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();
-        assert!((sampler.gamma - 0.15).abs() < f64::EPSILON);
+        let sampler = TpeSampler::with_config(0.15, 20, 32, None, Some(42));
+        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);
-        assert!(matches!(result, Err(Error::InvalidGamma(_))));
+        TpeSampler::with_config(0.0, 10, 24, None, None);
    }

    #[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);
-        assert!(matches!(result, Err(Error::InvalidGamma(_))));
+        TpeSampler::with_config(1.0, 10, 24, None, None);
    }

    #[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),
-                    vec![("x", ParamValue::Float(f64::from(i) / 20.0), dist.clone())],
+                    i as f64,
+                    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),
-                    vec![("x", ParamValue::Float(f64::from(i) / 20.0), dist.clone())],
+                    i as f64,
+                    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 sampler2 = 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));

        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();
-        assert!((sampler.gamma - 0.25).abs() < f64::EPSILON);
+        let sampler = builder.build();
+        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()
-            .unwrap();
-        assert!((sampler.gamma - 0.15).abs() < f64::EPSILON);
+            .build();
+        assert_eq!(sampler.gamma, 0.15);
        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()
-            .unwrap();
-        assert!((sampler.gamma - 0.10).abs() < f64::EPSILON);
+            .build();
+        assert_eq!(sampler.gamma, 0.10);
        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();
-        assert!((sampler.gamma - 0.20).abs() < f64::EPSILON);
+        let sampler = TpeSamplerBuilder::new().gamma(0.20).build();
+        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();
-        assert!(matches!(result, Err(Error::InvalidGamma(_))));
+        TpeSamplerBuilder::new().gamma(1.5).build();
    }

    #[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),
-                    f64::from(i),
-                    vec![("x", ParamValue::Float(f64::from(i) / 20.0), dist.clone())],
+                    i as u64,
+                    i as f64,
+                    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()
-            .unwrap();
+            .build();
        let sampler2 = TpeSampler::builder()
            .seed(99999)
            .n_startup_trials(5)
-            .build()
-            .unwrap();
+            .build();

        for i in 0..10 {
            let v1 = sampler1.sample(&dist, i, &history);
@@ -1,15 +1,14 @@
 //! Study implementation for managing optimization trials.

 #[cfg(feature = "async")]
-use core::future::Future;
-use core::ops::ControlFlow;
-use core::sync::atomic::{AtomicU64, Ordering};
+use std::future::Future;
+use std::ops::ControlFlow;
 use std::sync::Arc;
+use std::sync::atomic::{AtomicU64, Ordering};

 use parking_lot::RwLock;

-use crate::sampler::random::RandomSampler;
-use crate::sampler::{CompletedTrial, Sampler};
+use crate::sampler::{CompletedTrial, RandomSampler, 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, Study};
+    /// use optimizer::{Direction, RandomSampler, 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};
+    /// use optimizer::{Direction, Study, TpeSampler};
    ///
    /// let mut study: Study<f64> = Study::new(Direction::Minimize);
    /// study.set_sampler(TpeSampler::new());
@@ -275,7 +271,7 @@ where
    ///
    /// # Errors
    ///
-    /// Returns `Error::NoCompletedTrials` if no trials have been completed.
+    /// Returns `TpeError::NoCompletedTrials` if no trials have been completed.
    ///
    /// # Examples
    ///
@@ -305,7 +301,7 @@ where
        let trials = self.completed_trials.read();

        if trials.is_empty() {
-            return Err(crate::Error::NoCompletedTrials);
+            return Err(crate::TpeError::NoCompletedTrials);
        }

        let best = trials
@@ -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::Error::NoCompletedTrials)?;
+            .expect("trials is not empty");

        Ok(best.clone())
    }
@@ -338,7 +336,7 @@ where
    ///
    /// # Errors
    ///
-    /// Returns `Error::NoCompletedTrials` if no trials have been completed.
+    /// Returns `TpeError::NoCompletedTrials` if no trials have been completed.
    ///
    /// # Examples
    ///
@@ -387,13 +385,12 @@ where
    ///
    /// # Errors
    ///
-    /// Returns `Error::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
    ///
    /// # Examples
    ///
    /// ```
-    /// use optimizer::sampler::random::RandomSampler;
-    /// use optimizer::{Direction, Study};
+    /// use optimizer::{Direction, RandomSampler, Study};
    ///
    /// // Minimize x^2
    /// let sampler = RandomSampler::with_seed(42);
@@ -402,7 +399,7 @@ where
    /// study
    ///     .optimize(10, |trial| {
    ///         let x = trial.suggest_float("x", -10.0, 10.0)?;
-    ///         Ok::<_, optimizer::Error>(x * x)
+    ///         Ok::<_, optimizer::TpeError>(x * x)
    ///     })
    ///     .unwrap();
    ///
@@ -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 {
@@ -431,7 +428,7 @@ where

        // Return error if no trials succeeded
        if self.n_trials() == 0 {
-            return Err(crate::Error::NoCompletedTrials);
+            return Err(crate::TpeError::NoCompletedTrials);
        }

        Ok(())
@@ -455,13 +452,12 @@ where
    ///
    /// # Errors
    ///
-    /// Returns `Error::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
    ///
    /// # Examples
    ///
    /// ```
-    /// use optimizer::sampler::random::RandomSampler;
-    /// use optimizer::{Direction, Study};
+    /// use optimizer::{Direction, RandomSampler, Study};
    ///
    /// # #[cfg(feature = "async")]
    /// # async fn example() -> optimizer::Result<()> {
@@ -474,7 +470,7 @@ where
    ///         let x = trial.suggest_float("x", -10.0, 10.0)?;
    ///         // Simulate async work (e.g., network request)
    ///         let value = x * x;
-    ///         Ok::<_, optimizer::Error>((trial, value))
+    ///         Ok::<_, optimizer::TpeError>((trial, value))
    ///     })
    ///     .await?;
    ///
@@ -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 {
@@ -511,7 +507,7 @@ where

        // Return error if no trials succeeded
        if self.n_trials() == 0 {
-            return Err(crate::Error::NoCompletedTrials);
+            return Err(crate::TpeError::NoCompletedTrials);
        }

        Ok(())
@@ -536,14 +532,12 @@ where
    ///
    /// # Errors
    ///
-    /// Returns `Error::NoCompletedTrials` if all trials failed (no successful trials).
-    /// Returns `Error::TaskError` if the semaphore is closed or a spawned task panics.
+    /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
    ///
    /// # Examples
    ///
    /// ```
-    /// use optimizer::sampler::random::RandomSampler;
-    /// use optimizer::{Direction, Study};
+    /// use optimizer::{Direction, RandomSampler, Study};
    ///
    /// # #[cfg(feature = "async")]
    /// # async fn example() -> optimizer::Result<()> {
@@ -556,7 +550,7 @@ where
    ///         let x = trial.suggest_float("x", -10.0, 10.0)?;
    ///         // Async objective function (e.g., network request)
    ///         let value = x * x;
-    ///         Ok::<_, optimizer::Error>((trial, value))
+    ///         Ok::<_, optimizer::TpeError>((trial, value))
    ///     })
    ///     .await?;
    ///
@@ -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
-                .clone()
-                .acquire_owned()
-                .await
-                .map_err(|e| crate::Error::TaskError(e.to_string()))?;
+            let permit = semaphore.clone().acquire_owned().await.unwrap();
            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
-                .await
-                .map_err(|e| crate::Error::TaskError(e.to_string()))?
-            {
+            match handle.await.unwrap() {
                Ok((trial, value)) => {
                    self.complete_trial(trial, value);
                }
@@ -620,7 +607,7 @@ where

        // Return error if no trials succeeded
        if self.n_trials() == 0 {
-            return Err(crate::Error::NoCompletedTrials);
+            return Err(crate::TpeError::NoCompletedTrials);
        }

        Ok(())
@@ -643,17 +630,15 @@ where
    ///
    /// # Errors
    ///
-    /// Returns `Error::NoCompletedTrials` if no trials completed successfully
+    /// Returns `TpeError::NoCompletedTrials` if no trials completed successfully
    /// before optimization stopped (either by completing all trials or early stopping).
-    /// Returns `Error::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, Study};
+    /// use optimizer::{Direction, RandomSampler, Study};
    ///
    /// // Stop early when we find a good enough value
    /// let sampler = RandomSampler::with_seed(42);
@@ -664,7 +649,7 @@ where
    ///         100,
    ///         |trial| {
    ///             let x = trial.suggest_float("x", -10.0, 10.0)?;
-    ///             Ok::<_, optimizer::Error>(x * x)
+    ///             Ok::<_, optimizer::TpeError>(x * x)
    ///         },
    ///         |_study, completed_trial| {
    ///             // Stop early if we find a value less than 1.0
@@ -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 {
-                        return Err(crate::Error::Internal(
-                            "completed trial not found after adding",
-                        ));
-                    };
+                    let completed = trials.last().expect("just added a trial");

                    // Call the callback and check if we should stop
                    // Note: We need to drop the read lock before calling callback
@@ -725,7 +706,7 @@ where

        // Return error if no trials succeeded
        if self.n_trials() == 0 {
-            return Err(crate::Error::NoCompletedTrials);
+            return Err(crate::TpeError::NoCompletedTrials);
        }

        Ok(())
@@ -746,8 +727,7 @@ impl Study<f64> {
    /// # Examples
    ///
    /// ```
-    /// use optimizer::sampler::random::RandomSampler;
-    /// use optimizer::{Direction, Study};
+    /// use optimizer::{Direction, RandomSampler, Study};
    ///
    /// // With a seeded sampler for reproducibility
    /// let sampler = RandomSampler::with_seed(42);
@@ -783,13 +763,12 @@ impl Study<f64> {
    ///
    /// # Errors
    ///
-    /// Returns `Error::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
    ///
    /// # Examples
    ///
    /// ```
-    /// use optimizer::sampler::random::RandomSampler;
-    /// use optimizer::{Direction, Study};
+    /// use optimizer::{Direction, RandomSampler, Study};
    ///
    /// // Minimize x^2 with sampler integration
    /// let sampler = RandomSampler::with_seed(42);
@@ -798,7 +777,7 @@ impl Study<f64> {
    /// study
    ///     .optimize_with_sampler(10, |trial| {
    ///         let x = trial.suggest_float("x", -10.0, 10.0)?;
-    ///         Ok::<_, optimizer::Error>(x * x)
+    ///         Ok::<_, optimizer::TpeError>(x * x)
    ///     })
    ///     .unwrap();
    ///
@@ -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 {
@@ -829,7 +808,7 @@ impl Study<f64> {

        // Return error if no trials succeeded
        if self.n_trials() == 0 {
-            return Err(crate::Error::NoCompletedTrials);
+            return Err(crate::TpeError::NoCompletedTrials);
        }

        Ok(())
@@ -851,16 +830,14 @@ impl Study<f64> {
    ///
    /// # Errors
    ///
-    /// Returns `Error::NoCompletedTrials` if no trials completed successfully.
-    /// Returns `Error::Internal` if a completed trial is not found after adding (internal invariant violation).
+    /// Returns `TpeError::NoCompletedTrials` if no trials completed successfully.
    ///
    /// # Examples
    ///
    /// ```
    /// use std::ops::ControlFlow;
    ///
-    /// use optimizer::sampler::random::RandomSampler;
-    /// use optimizer::{Direction, Study};
+    /// use optimizer::{Direction, RandomSampler, Study};
    ///
    /// // Optimize with sampler integration and early stopping
    /// let sampler = RandomSampler::with_seed(42);
@@ -871,7 +848,7 @@ impl Study<f64> {
    ///         100,
    ///         |trial| {
    ///             let x = trial.suggest_float("x", -10.0, 10.0)?;
-    ///             Ok::<_, optimizer::Error>(x * x)
+    ///             Ok::<_, optimizer::TpeError>(x * x)
    ///         },
    ///         |study, _completed_trial| {
    ///             // Stop after finding 5 good trials
@@ -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 {
-                        return Err(crate::Error::Internal(
-                            "completed trial not found after adding",
-                        ));
-                    };
+                    let completed = trials.last().expect("just added a trial");

                    // Call the callback and check if we should stop
                    // Note: We need to drop the read lock before calling callback
@@ -930,7 +903,7 @@ impl Study<f64> {

        // Return error if no trials succeeded
        if self.n_trials() == 0 {
-            return Err(crate::Error::NoCompletedTrials);
+            return Err(crate::TpeError::NoCompletedTrials);
        }

        Ok(())
@@ -953,13 +926,12 @@ impl Study<f64> {
    ///
    /// # Errors
    ///
-    /// Returns `Error::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
    ///
    /// # Examples
    ///
    /// ```
-    /// use optimizer::sampler::random::RandomSampler;
-    /// use optimizer::{Direction, Study};
+    /// use optimizer::{Direction, RandomSampler, Study};
    ///
    /// # #[cfg(feature = "async")]
    /// # async fn example() -> optimizer::Result<()> {
@@ -972,7 +944,7 @@ impl Study<f64> {
    ///         let x = trial.suggest_float("x", -10.0, 10.0)?;
    ///         // Simulate async work (e.g., network request)
    ///         let value = x * x;
-    ///         Ok::<_, optimizer::Error>((trial, value))
+    ///         Ok::<_, optimizer::TpeError>((trial, value))
    ///     })
    ///     .await?;
    ///
@@ -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 {
@@ -1009,7 +981,7 @@ impl Study<f64> {

        // Return error if no trials succeeded
        if self.n_trials() == 0 {
-            return Err(crate::Error::NoCompletedTrials);
+            return Err(crate::TpeError::NoCompletedTrials);
        }

        Ok(())
@@ -1034,14 +1006,12 @@ impl Study<f64> {
    ///
    /// # Errors
    ///
-    /// Returns `Error::NoCompletedTrials` if all trials failed (no successful trials).
-    /// Returns `Error::TaskError` if the semaphore is closed or a spawned task panics.
+    /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
    ///
    /// # Examples
    ///
    /// ```
-    /// use optimizer::sampler::random::RandomSampler;
-    /// use optimizer::{Direction, Study};
+    /// use optimizer::{Direction, RandomSampler, Study};
    ///
    /// # #[cfg(feature = "async")]
    /// # async fn example() -> optimizer::Result<()> {
@@ -1054,7 +1024,7 @@ impl Study<f64> {
    ///         let x = trial.suggest_float("x", -10.0, 10.0)?;
    ///         // Async objective function (e.g., network request)
    ///         let value = x * x;
-    ///         Ok::<_, optimizer::Error>((trial, value))
+    ///         Ok::<_, optimizer::TpeError>((trial, value))
    ///     })
    ///     .await?;
    ///
@@ -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
-                .clone()
-                .acquire_owned()
-                .await
-                .map_err(|e| crate::Error::TaskError(e.to_string()))?;
+            let permit = semaphore.clone().acquire_owned().await.unwrap();
            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
-                .await
-                .map_err(|e| crate::Error::TaskError(e.to_string()))?
-            {
+            match handle.await.unwrap() {
                Ok((trial, value)) => {
                    self.complete_trial(trial, value);
                }
@@ -1117,7 +1080,7 @@ impl Study<f64> {

        // Return error if no trials succeeded
        if self.n_trials() == 0 {
-            return Err(crate::Error::NoCompletedTrials);
+            return Err(crate::TpeError::NoCompletedTrials);
        }

        Ok(())
@@ -1,6 +1,5 @@
 //! Trial implementation for tracking sampled parameters and trial state.

-use core::ops::{Range, RangeInclusive};
 use std::collections::HashMap;
 use std::sync::Arc;

@@ -9,69 +8,11 @@ use parking_lot::RwLock;
 use crate::distribution::{
    CategoricalDistribution, Distribution, FloatDistribution, IntDistribution,
 };
-use crate::error::{Error, Result};
+use crate::error::{Result, TpeError};
 use crate::param::ParamValue;
 use crate::sampler::{CompletedTrial, Sampler};
 use crate::types::TrialState;

-/// A trait for types that can be used with [`Trial::suggest_range`].
-///
-/// This trait is implemented for [`Range`] and [`RangeInclusive`] over `f64` and `i64`.
-/// It allows using Rust's range syntax directly with the optimizer.
-///
-/// # Supported Range Types
-///
-/// | Range Type | Example | Description |
-/// |------------|---------|-------------|
-/// | `Range<f64>` | `0.0..1.0` | Float range (end-exclusive, treated as inclusive for continuous sampling) |
-/// | `RangeInclusive<f64>` | `0.0..=1.0` | Float range (end-inclusive) |
-/// | `Range<i64>` | `1..10` | Integer range from 1 to 9 (end-exclusive) |
-/// | `RangeInclusive<i64>` | `1..=10` | Integer range from 1 to 10 (end-inclusive) |
-pub trait SuggestableRange {
-    /// The output type when suggesting from this range.
-    type Output;
-
-    /// Suggests a value from this range using the given trial.
-    ///
-    /// # Errors
-    ///
-    /// Returns an error if the range is invalid (e.g., empty or low > high).
-    fn suggest(self, trial: &mut Trial, name: String) -> Result<Self::Output>;
-}
-
-impl SuggestableRange for Range<f64> {
-    type Output = f64;
-
-    fn suggest(self, trial: &mut Trial, name: String) -> Result<f64> {
-        trial.suggest_float(name, self.start, self.end)
-    }
-}
-
-impl SuggestableRange for RangeInclusive<f64> {
-    type Output = f64;
-
-    fn suggest(self, trial: &mut Trial, name: String) -> Result<f64> {
-        trial.suggest_float(name, *self.start(), *self.end())
-    }
-}
-
-impl SuggestableRange for Range<i64> {
-    type Output = i64;
-
-    fn suggest(self, trial: &mut Trial, name: String) -> Result<i64> {
-        // Range is exclusive on the end, so subtract 1
-        trial.suggest_int(name, self.start, self.end.saturating_sub(1))
-    }
-}
-
-impl SuggestableRange for RangeInclusive<i64> {
-    type Output = i64;
-
-    fn suggest(self, trial: &mut Trial, name: String) -> Result<i64> {
-        trial.suggest_int(name, *self.start(), *self.end())
-    }
-}
-
 /// A trial represents a single evaluation of the objective function.
 ///
 /// Each trial has a unique ID and stores the sampled parameters along with
@@ -96,8 +37,8 @@ pub struct Trial {
    history: Option<Arc<RwLock<Vec<CompletedTrial<f64>>>>>,
 }

-impl core::fmt::Debug for Trial {
-    fn fmt(&self, f: &mut core::fmt::Formatter<'_>) -> core::fmt::Result {
+impl std::fmt::Debug for Trial {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("Trial")
            .field("id", &self.id)
            .field("state", &self.state)
@@ -130,7 +71,6 @@ impl Trial {
    /// let trial = Trial::new(0);
    /// assert_eq!(trial.id(), 0);
    /// ```
-    #[must_use]
    pub fn new(id: u64) -> Self {
        Self {
            id,
@@ -170,7 +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) {
@@ -178,32 +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
    }
@@ -249,7 +185,7 @@ impl Trial {
    /// ```
    pub fn suggest_float(&mut self, name: impl Into<String>, low: f64, high: f64) -> Result<f64> {
        if low > high {
-            return Err(Error::InvalidBounds { low, high });
+            return Err(TpeError::InvalidBounds { low, high });
        }

        let name = name.into();
@@ -264,8 +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.high - high).abs() < f64::EPSILON
+                && existing.low == low
+                && existing.high == high
                && !existing.log_scale
                && existing.step.is_none()
            {
@@ -275,7 +211,7 @@ impl Trial {
                }
            }
            // Distribution exists but doesn't match
-            return Err(Error::ParameterConflict {
+            return Err(TpeError::ParameterConflict {
                name,
                reason: "parameter was previously sampled with different bounds or type"
                    .to_string(),
@@ -284,10 +220,9 @@ impl Trial {

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

        // Store distribution and value
@@ -302,7 +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.
    ///
@@ -342,11 +277,11 @@ impl Trial {
        high: f64,
    ) -> Result<f64> {
        if low <= 0.0 {
-            return Err(Error::InvalidLogBounds);
+            return Err(TpeError::InvalidLogBounds);
        }

        if low > high {
-            return Err(Error::InvalidBounds { low, high });
+            return Err(TpeError::InvalidBounds { low, high });
        }

        let name = name.into();
@@ -361,8 +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.high - high).abs() < f64::EPSILON
+                && existing.low == low
+                && existing.high == high
                && existing.log_scale
                && existing.step.is_none()
            {
@@ -372,7 +307,7 @@ impl Trial {
                }
            }
            // Distribution exists but doesn't match
-            return Err(Error::ParameterConflict {
+            return Err(TpeError::ParameterConflict {
                name,
                reason: "parameter was previously sampled with different bounds or type"
                    .to_string(),
@@ -381,10 +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 {
-            return Err(Error::Internal(
-                "Float distribution should return Float value",
-            ));
+        let value = match self.sample_value(&dist) {
+            ParamValue::Float(v) => v,
+            _ => unreachable!("Float distribution should return Float value"),
        };

        // Store distribution and value
@@ -439,11 +373,11 @@ impl Trial {
        step: f64,
    ) -> Result<f64> {
        if step <= 0.0 {
-            return Err(Error::InvalidStep);
+            return Err(TpeError::InvalidStep);
        }

        if low > high {
-            return Err(Error::InvalidBounds { low, high });
+            return Err(TpeError::InvalidBounds { low, high });
        }

        let name = name.into();
@@ -458,8 +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.high - high).abs() < f64::EPSILON
+                && existing.low == low
+                && existing.high == high
                && !existing.log_scale
                && existing.step == Some(step)
            {
@@ -469,7 +403,7 @@ impl Trial {
                }
            }
            // Distribution exists but doesn't match
-            return Err(Error::ParameterConflict {
+            return Err(TpeError::ParameterConflict {
                name,
                reason: "parameter was previously sampled with different bounds or type"
                    .to_string(),
@@ -478,10 +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 {
-            return Err(Error::Internal(
-                "Float distribution should return Float value",
-            ));
+        let value = match self.sample_value(&dist) {
+            ParamValue::Float(v) => v,
+            _ => unreachable!("Float distribution should return Float value"),
        };

        // Store distribution and value
@@ -522,10 +455,9 @@ 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(Error::InvalidBounds {
+            return Err(TpeError::InvalidBounds {
                low: low as f64,
                high: high as f64,
            });
@@ -554,7 +486,7 @@ impl Trial {
                }
            }
            // Distribution exists but doesn't match
-            return Err(Error::ParameterConflict {
+            return Err(TpeError::ParameterConflict {
                name,
                reason: "parameter was previously sampled with different bounds or type"
                    .to_string(),
@@ -563,8 +495,9 @@ impl Trial {

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

        // Store distribution and value
@@ -579,7 +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.
    ///
@@ -608,14 +541,13 @@ 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(Error::InvalidLogBounds);
+            return Err(TpeError::InvalidLogBounds);
        }

        if low > high {
-            return Err(Error::InvalidBounds {
+            return Err(TpeError::InvalidBounds {
                low: low as f64,
                high: high as f64,
            });
@@ -644,7 +576,7 @@ impl Trial {
                }
            }
            // Distribution exists but doesn't match
-            return Err(Error::ParameterConflict {
+            return Err(TpeError::ParameterConflict {
                name,
                reason: "parameter was previously sampled with different bounds or type"
                    .to_string(),
@@ -653,8 +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 {
-            return Err(Error::Internal("Int distribution should return Int value"));
+        let value = match self.sample_value(&dist) {
+            ParamValue::Int(v) => v,
+            _ => unreachable!("Int distribution should return Int value"),
        };

        // Store distribution and value
@@ -705,7 +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>,
@@ -714,11 +646,11 @@ impl Trial {
        step: i64,
    ) -> Result<i64> {
        if step <= 0 {
-            return Err(Error::InvalidStep);
+            return Err(TpeError::InvalidStep);
        }

        if low > high {
-            return Err(Error::InvalidBounds {
+            return Err(TpeError::InvalidBounds {
                low: low as f64,
                high: high as f64,
            });
@@ -747,7 +679,7 @@ impl Trial {
                }
            }
            // Distribution exists but doesn't match
-            return Err(Error::ParameterConflict {
+            return Err(TpeError::ParameterConflict {
                name,
                reason: "parameter was previously sampled with different bounds or type"
                    .to_string(),
@@ -756,8 +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 {
-            return Err(Error::Internal("Int distribution should return Int value"));
+        let value = match self.sample_value(&dist) {
+            ParamValue::Int(v) => v,
+            _ => unreachable!("Int distribution should return Int value"),
        };

        // Store distribution and value
@@ -813,7 +746,7 @@ impl Trial {
        choices: &[T],
    ) -> Result<T> {
        if choices.is_empty() {
-            return Err(Error::EmptyChoices);
+            return Err(TpeError::EmptyChoices);
        }

        let name = name.into();
@@ -832,7 +765,7 @@ impl Trial {
                }
            }
            // Distribution exists but doesn't match
-            return Err(Error::ParameterConflict {
+            return Err(TpeError::ParameterConflict {
                name,
                reason: "parameter was previously sampled with different number of choices or type"
                    .to_string(),
@@ -841,10 +774,9 @@ impl Trial {

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

        // Store distribution and value (store the index)
@@ -853,95 +785,4 @@ impl Trial {

        Ok(choices[index].clone())
    }
-
-    /// Suggests a boolean parameter.
-    ///
-    /// The value is selected uniformly at random from `{false, true}`.
-    /// This is equivalent to calling `suggest_categorical(name, &[false, true])`.
-    ///
-    /// If the parameter has already been sampled, the cached value is returned.
-    /// If the parameter was sampled with a different type, a `ParameterConflict` error is returned.
-    ///
-    /// # Arguments
-    ///
-    /// * `name` - The name of the parameter.
-    ///
-    /// # Errors
-    ///
-    /// Returns `ParameterConflict` if the parameter was previously sampled with a different type.
-    ///
-    /// # Examples
-    ///
-    /// ```
-    /// use optimizer::Trial;
-    ///
-    /// let mut trial = Trial::new(0);
-    /// let use_dropout = trial.suggest_bool("use_dropout").unwrap();
-    /// assert!(use_dropout == true || use_dropout == false);
-    ///
-    /// // Calling again returns cached value
-    /// let use_dropout2 = trial.suggest_bool("use_dropout").unwrap();
-    /// assert_eq!(use_dropout, use_dropout2);
-    /// ```
-    pub fn suggest_bool(&mut self, name: impl Into<String>) -> Result<bool> {
-        self.suggest_categorical(name, &[false, true])
-    }
-
-    /// Suggests a parameter value from a range.
-    ///
-    /// This method accepts both [`Range`] (`..`) and [`RangeInclusive`] (`..=`)
-    /// for both `f64` and `i64` types, allowing natural Rust range syntax.
-    ///
-    /// For integer ranges, note that `Range` (`..`) is end-exclusive while
-    /// `RangeInclusive` (`..=`) is end-inclusive, matching Rust's semantics.
-    ///
-    /// If the parameter has already been sampled with the same bounds, the cached value is returned.
-    /// If the parameter was sampled with different bounds, a `ParameterConflict` error is returned.
-    ///
-    /// # Arguments
-    ///
-    /// * `name` - The name of the parameter.
-    /// * `range` - The range to sample from.
-    ///
-    /// # Type Parameters
-    ///
-    /// * `R` - A range type implementing [`SuggestableRange`].
-    ///
-    /// # Errors
-    ///
-    /// Returns `InvalidBounds` if the range is invalid (e.g., low > high or empty integer range).
-    /// Returns `ParameterConflict` if the parameter was previously sampled with different bounds.
-    ///
-    /// # Examples
-    ///
-    /// ```
-    /// use optimizer::Trial;
-    ///
-    /// let mut trial = Trial::new(0);
-    ///
-    /// // Float ranges
-    /// let x = trial.suggest_range("x", 0.0..1.0).unwrap();
-    /// assert!(x >= 0.0 && x <= 1.0);
-    ///
-    /// let y = trial.suggest_range("y", 0.0..=1.0).unwrap();
-    /// assert!(y >= 0.0 && y <= 1.0);
-    ///
-    /// // Integer ranges
-    /// let n = trial.suggest_range("n", 1_i64..10).unwrap(); // 1 to 9 inclusive
-    /// assert!(n >= 1 && n <= 9);
-    ///
-    /// let m = trial.suggest_range("m", 1_i64..=10).unwrap(); // 1 to 10 inclusive
-    /// assert!(m >= 1 && m <= 10);
-    ///
-    /// // Calling again with same range returns cached value
-    /// let x2 = trial.suggest_range("x", 0.0..1.0).unwrap();
-    /// assert_eq!(x, x2);
-    /// ```
-    pub fn suggest_range<R: SuggestableRange>(
-        &mut self,
-        name: impl Into<String>,
-        range: R,
-    ) -> Result<R::Output> {
-        range.suggest(self, name.into())
-    }
 }
@@ -4,9 +4,7 @@

 #![cfg(feature = "async")]

-use optimizer::sampler::random::RandomSampler;
-use optimizer::sampler::tpe::TpeSampler;
-use optimizer::{Direction, Error, Study};
+use optimizer::{Direction, RandomSampler, Study, TpeError, TpeSampler};

 #[tokio::test]
 async fn test_optimize_async_basic() {
@@ -16,7 +14,7 @@ async fn test_optimize_async_basic() {
    study
        .optimize_async(10, |mut trial| async move {
            let x = trial.suggest_float("x", -10.0, 10.0)?;
-            Ok::<_, Error>((trial, x * x))
+            Ok::<_, TpeError>((trial, x * x))
        })
        .await
        .expect("async optimization should succeed");
@@ -28,18 +26,14 @@ 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()
-        .unwrap();
+    let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();

    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);

    study
        .optimize_async_with_sampler(15, |mut trial| async move {
            let x = trial.suggest_float("x", -5.0, 5.0)?;
-            Ok::<_, Error>((trial, x * x))
+            Ok::<_, TpeError>((trial, x * x))
        })
        .await
        .expect("async optimization with sampler should succeed");
@@ -57,7 +51,7 @@ async fn test_optimize_parallel() {
    study
        .optimize_parallel(20, 4, |mut trial| async move {
            let x = trial.suggest_float("x", -10.0, 10.0)?;
-            Ok::<_, Error>((trial, x * x))
+            Ok::<_, TpeError>((trial, x * x))
        })
        .await
        .expect("parallel optimization should succeed");
@@ -67,11 +61,7 @@ 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()
-        .unwrap();
+    let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();

    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);

@@ -79,7 +69,7 @@ async fn test_optimize_parallel_with_sampler() {
        .optimize_parallel_with_sampler(15, 3, |mut trial| async move {
            let x = trial.suggest_float("x", -5.0, 5.0)?;
            let y = trial.suggest_float("y", -5.0, 5.0)?;
-            Ok::<_, Error>((trial, x * x + y * y))
+            Ok::<_, TpeError>((trial, x * x + y * y))
        })
        .await
        .expect("parallel optimization with sampler should succeed");
@@ -99,7 +89,7 @@ async fn test_optimize_async_all_failures() {
        .await;

    assert!(
-        matches!(result, Err(Error::NoCompletedTrials)),
+        matches!(result, Err(TpeError::NoCompletedTrials)),
        "should return NoCompletedTrials when all trials fail"
    );
 }
@@ -116,7 +106,7 @@ async fn test_optimize_async_with_sampler_all_failures() {
        .await;

    assert!(
-        matches!(result, Err(Error::NoCompletedTrials)),
+        matches!(result, Err(TpeError::NoCompletedTrials)),
        "should return NoCompletedTrials when all trials fail"
    );
 }
@@ -133,7 +123,7 @@ async fn test_optimize_parallel_all_failures() {
        .await;

    assert!(
-        matches!(result, Err(Error::NoCompletedTrials)),
+        matches!(result, Err(TpeError::NoCompletedTrials)),
        "should return NoCompletedTrials when all trials fail"
    );
 }
@@ -150,7 +140,7 @@ async fn test_optimize_parallel_with_sampler_all_failures() {
        .await;

    assert!(
-        matches!(result, Err(Error::NoCompletedTrials)),
+        matches!(result, Err(TpeError::NoCompletedTrials)),
        "should return NoCompletedTrials when all trials fail"
    );
 }
@@ -168,9 +158,9 @@ async fn test_optimize_async_partial_failures() {
            async move {
                if count.is_multiple_of(2) {
                    let x = trial.suggest_float("x", 0.0, 10.0)?;
-                    Ok::<_, Error>((trial, x))
+                    Ok::<_, TpeError>((trial, x))
                } else {
-                    Err(Error::NoCompletedTrials) // Use as error type
+                    Err(TpeError::NoCompletedTrials) // Use as error type
                }
            }
        })
@@ -190,7 +180,7 @@ async fn test_optimize_parallel_high_concurrency() {
    study
        .optimize_parallel(5, 10, |mut trial| async move {
            let x = trial.suggest_float("x", 0.0, 10.0)?;
-            Ok::<_, Error>((trial, x))
+            Ok::<_, TpeError>((trial, x))
        })
        .await
        .expect("should handle high concurrency");
@@ -207,7 +197,7 @@ async fn test_optimize_parallel_single_concurrency() {
    study
        .optimize_parallel(10, 1, |mut trial| async move {
            let x = trial.suggest_float("x", 0.0, 10.0)?;
-            Ok::<_, Error>((trial, x))
+            Ok::<_, TpeError>((trial, x))
        })
        .await
        .expect("should work with single concurrency");
@@ -1,14 +1,6 @@
 //! Integration tests for the optimizer library.

-#![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, Error, Study, Trial};
+use optimizer::{Direction, RandomSampler, Study, TpeError, TpeSampler, Trial};

 // =============================================================================
 // Test: optimize simple quadratic function with TPE, finds near-optimal
@@ -22,15 +14,14 @@ fn test_tpe_optimizes_quadratic_function() {
        .seed(42)
        .n_startup_trials(5) // Quick startup for test
        .n_ei_candidates(24)
-        .build()
-        .unwrap();
+        .build();

    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);

    study
        .optimize_with_sampler(50, |trial| {
            let x = trial.suggest_float("x", -10.0, 10.0)?;
-            Ok::<_, Error>((x - 3.0).powi(2))
+            Ok::<_, TpeError>((x - 3.0).powi(2))
        })
        .expect("optimization should succeed");

@@ -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()
-        .seed(123)
-        .n_startup_trials(10)
-        .build()
-        .unwrap();
+    let sampler = TpeSampler::builder().seed(123).n_startup_trials(10).build();

    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);

@@ -61,7 +48,7 @@ fn test_tpe_optimizes_multivariate_function() {
        .optimize_with_sampler(100, |trial| {
            let x = trial.suggest_float("x", -5.0, 5.0)?;
            let y = trial.suggest_float("y", -5.0, 5.0)?;
-            Ok::<_, Error>(x * x + y * y)
+            Ok::<_, TpeError>(x * x + y * y)
        })
        .expect("optimization should succeed");

@@ -79,18 +66,14 @@ 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()
-        .unwrap();
+    let sampler = TpeSampler::builder().seed(456).n_startup_trials(5).build();

    let study: Study<f64> = Study::with_sampler(Direction::Maximize, sampler);

    study
        .optimize_with_sampler(50, |trial| {
            let x = trial.suggest_float("x", -10.0, 10.0)?;
-            Ok::<_, Error>(-(x - 2.0).powi(2) + 10.0)
+            Ok::<_, TpeError>(-(x - 2.0).powi(2) + 10.0)
        })
        .expect("optimization should succeed");

@@ -123,7 +106,7 @@ fn test_random_sampler_uniform_float_distribution() {
        .optimize(n_samples, |trial| {
            let x = trial.suggest_float("x", 0.0, 1.0)?;
            samples.push(x);
-            Ok::<_, Error>(x)
+            Ok::<_, TpeError>(x)
        })
        .unwrap();

@@ -152,7 +135,7 @@ fn test_random_sampler_uniform_float_distribution() {
 fn test_random_sampler_uniform_int_distribution() {
    let study: Study<f64> = Study::with_sampler(Direction::Minimize, RandomSampler::with_seed(123));

-    let n_samples = 5000;
+    let n_samples = 1000;
    let mut counts = [0u32; 10]; // counts for values 1-10

    study
@@ -160,18 +143,16 @@ fn test_random_sampler_uniform_int_distribution() {
            let n = trial.suggest_int("n", 1, 10)?;
            assert!((1..=10).contains(&n), "sample {n} out of range [1, 10]");
            counts[(n - 1) as usize] += 1;
-            Ok::<_, Error>(n as f64)
+            Ok::<_, TpeError>(n as f64)
        })
        .unwrap();

    // Each value should appear roughly n_samples / 10 times
-    // With 5000 samples, expected ~500 per bucket, std dev ~21
-    // 20% tolerance allows for ~4.5 std devs which is very safe
    let expected = n_samples as f64 / 10.0;
    for (i, &count) in counts.iter().enumerate() {
        let diff = (count as f64 - expected).abs() / expected;
        assert!(
-            diff < 0.2,
+            diff < 0.3,
            "value {} appeared {} times, expected ~{}, diff = {:.1}%",
            i + 1,
            count,
@@ -185,7 +166,7 @@ fn test_random_sampler_uniform_int_distribution() {
 fn test_random_sampler_uniform_categorical_distribution() {
    let study: Study<f64> = Study::with_sampler(Direction::Minimize, RandomSampler::with_seed(456));

-    let n_samples = 2000;
+    let n_samples = 1000;
    let mut counts = [0u32; 4];
    let choices = ["a", "b", "c", "d"];

@@ -194,18 +175,16 @@ fn test_random_sampler_uniform_categorical_distribution() {
            let choice = trial.suggest_categorical("cat", &choices)?;
            let idx = choices.iter().position(|&c| c == choice).unwrap();
            counts[idx] += 1;
-            Ok::<_, Error>(idx as f64)
+            Ok::<_, TpeError>(idx as f64)
        })
        .unwrap();

    // Each category should appear roughly n_samples / 4 times
-    // With 2000 samples, expected ~500 per bucket, std dev ~19
-    // 15% tolerance allows for ~4 std devs which is very safe
    let expected = n_samples as f64 / 4.0;
    for (i, &count) in counts.iter().enumerate() {
        let diff = (count as f64 - expected).abs() / expected;
        assert!(
-            diff < 0.15,
+            diff < 0.25,
            "category {} appeared {} times, expected ~{}, diff = {:.1}%",
            i,
            count,
@@ -231,7 +210,7 @@ fn test_random_sampler_reproducibility() {
        .optimize_with_sampler(100, |trial| {
            let x = trial.suggest_float("x", 0.0, 100.0)?;
            values1.push(x);
-            Ok::<_, Error>(x)
+            Ok::<_, TpeError>(x)
        })
        .unwrap();

@@ -239,7 +218,7 @@ fn test_random_sampler_reproducibility() {
        .optimize_with_sampler(100, |trial| {
            let x = trial.suggest_float("x", 0.0, 100.0)?;
            values2.push(x);
-            Ok::<_, Error>(x)
+            Ok::<_, TpeError>(x)
        })
        .unwrap();

@@ -371,7 +350,7 @@ fn test_parameter_conflict_float_different_bounds() {
    trial.suggest_float("x", 0.0, 1.0).unwrap();
    let result = trial.suggest_float("x", 0.0, 2.0); // Different upper bound

-    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
+    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
 }

 #[test]
@@ -381,7 +360,7 @@ fn test_parameter_conflict_float_vs_log() {
    trial.suggest_float("x", 0.1, 1.0).unwrap();
    let result = trial.suggest_float_log("x", 0.1, 1.0); // Same bounds but log scale

-    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
+    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
 }

 #[test]
@@ -391,7 +370,7 @@ fn test_parameter_conflict_float_vs_step() {
    trial.suggest_float("x", 0.0, 1.0).unwrap();
    let result = trial.suggest_float_step("x", 0.0, 1.0, 0.1); // Same bounds but with step

-    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
+    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
 }

 #[test]
@@ -401,7 +380,7 @@ fn test_parameter_conflict_int_different_bounds() {
    trial.suggest_int("n", 1, 10).unwrap();
    let result = trial.suggest_int("n", 1, 20); // Different upper bound

-    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
+    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
 }

 #[test]
@@ -411,7 +390,7 @@ fn test_parameter_conflict_int_vs_log() {
    trial.suggest_int("n", 1, 100).unwrap();
    let result = trial.suggest_int_log("n", 1, 100); // Same bounds but log scale

-    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
+    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
 }

 #[test]
@@ -421,7 +400,7 @@ fn test_parameter_conflict_categorical_different_n_choices() {
    trial.suggest_categorical("opt", &["a", "b", "c"]).unwrap();
    let result = trial.suggest_categorical("opt", &["x", "y"]); // Different number of choices

-    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
+    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
 }

 #[test]
@@ -431,7 +410,7 @@ fn test_parameter_conflict_float_vs_int() {
    trial.suggest_float("x", 0.0, 10.0).unwrap();
    let result = trial.suggest_int("x", 0, 10); // Different type

-    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
+    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
 }

 #[test]
@@ -442,7 +421,7 @@ fn test_parameter_conflict_returns_name() {
    let result = trial.suggest_float("my_param", 0.0, 2.0);

    match result {
-        Err(Error::ParameterConflict { name, .. }) => {
+        Err(TpeError::ParameterConflict { name, .. }) => {
            assert_eq!(name, "my_param");
        }
        _ => panic!("expected ParameterConflict error"),
@@ -460,7 +439,7 @@ fn test_empty_categorical_returns_error() {

    let result = trial.suggest_categorical("opt", empty);

-    assert!(matches!(result, Err(Error::EmptyChoices)));
+    assert!(matches!(result, Err(TpeError::EmptyChoices)));
 }

 #[test]
@@ -470,7 +449,7 @@ fn test_empty_categorical_vec_returns_error() {

    let result = trial.suggest_categorical("numbers", &empty);

-    assert!(matches!(result, Err(Error::EmptyChoices)));
+    assert!(matches!(result, Err(TpeError::EmptyChoices)));
 }

 // =============================================================================
@@ -484,7 +463,7 @@ fn test_study_basic_workflow() {
    study
        .optimize(10, |trial| {
            let x = trial.suggest_float("x", -5.0, 5.0)?;
-            Ok::<_, Error>(x * x)
+            Ok::<_, TpeError>(x * x)
        })
        .expect("optimization should succeed");

@@ -521,7 +500,7 @@ fn test_no_completed_trials_error() {
    let study: Study<f64> = Study::new(Direction::Minimize);

    let result = study.best_trial();
-    assert!(matches!(result, Err(Error::NoCompletedTrials)));
+    assert!(matches!(result, Err(TpeError::NoCompletedTrials)));
 }

 #[test]
@@ -530,11 +509,11 @@ fn test_invalid_bounds_errors() {

    // low > high for float
    let result = trial.suggest_float("x", 10.0, 5.0);
-    assert!(matches!(result, Err(Error::InvalidBounds { .. })));
+    assert!(matches!(result, Err(TpeError::InvalidBounds { .. })));

    // low > high for int
    let result = trial.suggest_int("n", 100, 50);
-    assert!(matches!(result, Err(Error::InvalidBounds { .. })));
+    assert!(matches!(result, Err(TpeError::InvalidBounds { .. })));
 }

 #[test]
@@ -543,14 +522,14 @@ fn test_invalid_log_bounds_errors() {

    // low <= 0 for log float
    let result = trial.suggest_float_log("x", 0.0, 1.0);
-    assert!(matches!(result, Err(Error::InvalidLogBounds)));
+    assert!(matches!(result, Err(TpeError::InvalidLogBounds)));

    let result = trial.suggest_float_log("y", -1.0, 1.0);
-    assert!(matches!(result, Err(Error::InvalidLogBounds)));
+    assert!(matches!(result, Err(TpeError::InvalidLogBounds)));

    // low < 1 for log int
    let result = trial.suggest_int_log("n", 0, 100);
-    assert!(matches!(result, Err(Error::InvalidLogBounds)));
+    assert!(matches!(result, Err(TpeError::InvalidLogBounds)));
 }

 #[test]
@@ -559,23 +538,19 @@ fn test_invalid_step_errors() {

    // step <= 0 for float
    let result = trial.suggest_float_step("x", 0.0, 1.0, 0.0);
-    assert!(matches!(result, Err(Error::InvalidStep)));
+    assert!(matches!(result, Err(TpeError::InvalidStep)));

    let result = trial.suggest_float_step("y", 0.0, 1.0, -0.1);
-    assert!(matches!(result, Err(Error::InvalidStep)));
+    assert!(matches!(result, Err(TpeError::InvalidStep)));

    // step <= 0 for int
    let result = trial.suggest_int_step("n", 0, 100, 0);
-    assert!(matches!(result, Err(Error::InvalidStep)));
+    assert!(matches!(result, Err(TpeError::InvalidStep)));
 }

 #[test]
 fn test_tpe_with_categorical_parameter() {
-    let sampler = TpeSampler::builder()
-        .seed(42)
-        .n_startup_trials(5)
-        .build()
-        .unwrap();
+    let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();

    let study: Study<f64> = Study::with_sampler(Direction::Maximize, sampler);

@@ -592,7 +567,7 @@ fn test_tpe_with_categorical_parameter() {
                "cubic" => -((x - 1.0).powi(2)) + 10.0, // peak at x=1, max value 10
                _ => unreachable!(),
            };
-            Ok::<_, Error>(value)
+            Ok::<_, TpeError>(value)
        })
        .expect("optimization should succeed");

@@ -607,11 +582,7 @@ fn test_tpe_with_categorical_parameter() {

 #[test]
 fn test_tpe_with_integer_parameters() {
-    let sampler = TpeSampler::builder()
-        .seed(789)
-        .n_startup_trials(5)
-        .build()
-        .unwrap();
+    let sampler = TpeSampler::builder().seed(789).n_startup_trials(5).build();

    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);

@@ -619,7 +590,7 @@ fn test_tpe_with_integer_parameters() {
    study
        .optimize_with_sampler(30, |trial| {
            let n = trial.suggest_int("n", 1, 10)?;
-            Ok::<_, Error>(((n - 7) as f64).powi(2))
+            Ok::<_, TpeError>(((n - 7) as f64).powi(2))
        })
        .expect("optimization should succeed");

@@ -647,7 +618,7 @@ fn test_callback_early_stopping() {
            |trial| {
                trials_run.set(trials_run.get() + 1);
                let x = trial.suggest_float("x", 0.0, 10.0)?;
-                Ok::<_, Error>(x)
+                Ok::<_, TpeError>(x)
            },
            |_study, _trial| {
                // Stop after 5 trials
@@ -670,7 +641,7 @@ fn test_study_trials_iteration() {
    study
        .optimize(5, |trial| {
            let x = trial.suggest_float("x", 0.0, 1.0)?;
-            Ok::<_, Error>(x)
+            Ok::<_, TpeError>(x)
        })
        .unwrap();

@@ -762,7 +733,7 @@ fn test_best_value() {
    study
        .optimize(10, |trial| {
            let x = trial.suggest_float("x", 0.0, 10.0)?;
-            Ok::<_, Error>(x)
+            Ok::<_, TpeError>(x)
        })
        .unwrap();

@@ -785,18 +756,14 @@ 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()
-        .unwrap();
+    let tpe = TpeSampler::builder().seed(42).n_startup_trials(5).build();
    study.set_sampler(tpe);

    // Should work with the new sampler
    study
        .optimize_with_sampler(10, |trial| {
            let x = trial.suggest_float("x", -5.0, 5.0)?;
-            Ok::<_, Error>(x * x)
+            Ok::<_, TpeError>(x * x)
        })
        .expect("optimization should succeed with new sampler");

@@ -811,7 +778,7 @@ fn test_study_with_i32_value_type() {
    study
        .optimize(10, |trial| {
            let x = trial.suggest_int("x", -10, 10)?;
-            Ok::<_, Error>(x.abs() as i32)
+            Ok::<_, TpeError>(x.abs() as i32)
        })
        .expect("optimization should succeed");

@@ -828,7 +795,7 @@ fn test_optimize_all_trials_fail() {
    let result = study.optimize(5, |_trial| Err::<f64, &str>("always fails"));

    assert!(
-        matches!(result, Err(Error::NoCompletedTrials)),
+        matches!(result, Err(TpeError::NoCompletedTrials)),
        "should return NoCompletedTrials when all trials fail"
    );
 }
@@ -846,7 +813,7 @@ fn test_optimize_with_callback_all_trials_fail() {
    );

    assert!(
-        matches!(result, Err(Error::NoCompletedTrials)),
+        matches!(result, Err(TpeError::NoCompletedTrials)),
        "should return NoCompletedTrials when all trials fail"
    );
 }
@@ -858,7 +825,7 @@ fn test_optimize_with_sampler_all_trials_fail() {
    let result = study.optimize_with_sampler(5, |_trial| Err::<f64, &str>("always fails"));

    assert!(
-        matches!(result, Err(Error::NoCompletedTrials)),
+        matches!(result, Err(TpeError::NoCompletedTrials)),
        "should return NoCompletedTrials when all trials fail"
    );
 }
@@ -876,7 +843,7 @@ fn test_optimize_with_callback_sampler_all_trials_fail() {
    );

    assert!(
-        matches!(result, Err(Error::NoCompletedTrials)),
+        matches!(result, Err(TpeError::NoCompletedTrials)),
        "should return NoCompletedTrials when all trials fail"
    );
 }
@@ -896,17 +863,17 @@ 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);
    study
        .optimize_with_sampler(5, |trial| {
            let x = trial.suggest_float("x", 0.0, 1.0)?;
-            Ok::<_, Error>(x)
+            Ok::<_, TpeError>(x)
        })
        .unwrap();

@@ -921,7 +888,7 @@ fn test_tpe_sampler_default_trait() {
    study
        .optimize_with_sampler(5, |trial| {
            let x = trial.suggest_float("x", 0.0, 1.0)?;
-            Ok::<_, Error>(x)
+            Ok::<_, TpeError>(x)
        })
        .unwrap();

@@ -934,15 +901,14 @@ fn test_tpe_with_fixed_kde_bandwidth() {
        .seed(42)
        .n_startup_trials(5)
        .kde_bandwidth(0.5)
-        .build()
-        .unwrap();
+        .build();

    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);

    study
        .optimize_with_sampler(20, |trial| {
            let x = trial.suggest_float("x", -5.0, 5.0)?;
-            Ok::<_, Error>(x * x)
+            Ok::<_, TpeError>(x * x)
        })
        .expect("optimization should succeed");

@@ -951,9 +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);
-    assert!(matches!(result, Err(Error::InvalidBandwidth(_))));
+    TpeSampler::with_config(0.25, 10, 24, Some(-1.0), None);
 }

 #[test]
@@ -962,15 +928,14 @@ 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()
-        .unwrap();
+        .build();

    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);

    study
        .optimize_with_sampler(5, |trial| {
            let x = trial.suggest_float("x", 0.0, 10.0)?;
-            Ok::<_, Error>(x)
+            Ok::<_, TpeError>(x)
        })
        .expect("optimization should succeed with small history");

@@ -979,11 +944,7 @@ 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()
-        .unwrap();
+    let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();

    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);

@@ -991,7 +952,7 @@ fn test_tpe_with_log_scale_int() {
        .optimize_with_sampler(20, |trial| {
            let batch_size = trial.suggest_int_log("batch_size", 1, 1024)?;
            // Optimal around batch_size = 32
-            Ok::<_, Error>(((batch_size as f64).log2() - 5.0).powi(2))
+            Ok::<_, TpeError>(((batch_size as f64).log2() - 5.0).powi(2))
        })
        .expect("optimization should succeed");

@@ -1001,11 +962,7 @@ 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()
-        .unwrap();
+    let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();

    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);

@@ -1013,7 +970,7 @@ fn test_tpe_with_step_distributions() {
        .optimize_with_sampler(20, |trial| {
            let x = trial.suggest_float_step("x", 0.0, 10.0, 0.5)?;
            let n = trial.suggest_int_step("n", 0, 100, 10)?;
-            Ok::<_, Error>((x - 5.0).powi(2) + ((n - 50) as f64).powi(2))
+            Ok::<_, TpeError>((x - 5.0).powi(2) + ((n - 50) as f64).powi(2))
        })
        .expect("optimization should succeed");

@@ -1083,8 +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()
-        .unwrap();
+        .build();

    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);

@@ -1092,7 +1048,7 @@ fn test_tpe_empty_good_or_bad_values_fallback() {
    study
        .optimize_with_sampler(10, |trial| {
            let x = trial.suggest_float("x", 0.0, 10.0)?;
-            Ok::<_, Error>(x)
+            Ok::<_, TpeError>(x)
        })
        .unwrap();

@@ -1100,7 +1056,7 @@ fn test_tpe_empty_good_or_bad_values_fallback() {
    study
        .optimize_with_sampler(5, |trial| {
            let y = trial.suggest_float("y", 0.0, 10.0)?;
-            Ok::<_, Error>(y)
+            Ok::<_, TpeError>(y)
        })
        .unwrap();

@@ -1118,7 +1074,7 @@ fn test_callback_early_stopping_on_first_trial() {
            100,
            |trial| {
                let x = trial.suggest_float("x", 0.0, 10.0)?;
-                Ok::<_, Error>(x)
+                Ok::<_, TpeError>(x)
            },
            |_study, _trial| {
                // Stop immediately after first trial
@@ -1142,7 +1098,7 @@ fn test_callback_sampler_early_stopping() {
            100,
            |trial| {
                let x = trial.suggest_float("x", 0.0, 10.0)?;
-                Ok::<_, Error>(x)
+                Ok::<_, TpeError>(x)
            },
            |study, _trial| {
                if study.n_trials() >= 3 {
@@ -1178,7 +1134,7 @@ fn test_best_trial_with_nan_values() {
    study
        .optimize(5, |trial| {
            let x = trial.suggest_float("x", 0.0, 10.0)?;
-            Ok::<_, Error>(x)
+            Ok::<_, TpeError>(x)
        })
        .unwrap();

@@ -1186,228 +1142,3 @@ fn test_best_trial_with_nan_values() {
    let best = study.best_trial();
    assert!(best.is_ok());
 }
-
-// =============================================================================
-// Tests for suggest_bool
-// =============================================================================
-
-#[test]
-fn test_suggest_bool_returns_boolean() {
-    let mut trial = Trial::new(0);
-
-    let flag = trial.suggest_bool("use_feature").unwrap();
-    assert!(flag == true || flag == false);
-}
-
-#[test]
-fn test_suggest_bool_caching() {
-    let mut trial = Trial::new(0);
-
-    let b1 = trial.suggest_bool("flag").unwrap();
-    let b2 = trial.suggest_bool("flag").unwrap();
-
-    assert_eq!(b1, b2, "repeated suggest_bool should return cached value");
-}
-
-#[test]
-fn test_suggest_bool_multiple_parameters() {
-    let mut trial = Trial::new(0);
-
-    let a = trial.suggest_bool("use_dropout").unwrap();
-    let b = trial.suggest_bool("use_batchnorm").unwrap();
-    let c = trial.suggest_bool("use_skip_connections").unwrap();
-
-    // All should be cached independently
-    assert_eq!(a, trial.suggest_bool("use_dropout").unwrap());
-    assert_eq!(b, trial.suggest_bool("use_batchnorm").unwrap());
-    assert_eq!(c, trial.suggest_bool("use_skip_connections").unwrap());
-}
-
-#[test]
-fn test_suggest_bool_in_optimization() {
-    let study: Study<f64> = Study::new(Direction::Minimize);
-
-    study
-        .optimize(10, |trial| {
-            let use_feature = trial.suggest_bool("use_feature")?;
-            let x = trial.suggest_float("x", 0.0, 10.0)?;
-
-            // Objective depends on boolean flag
-            let value = if use_feature { x } else { x * 2.0 };
-            Ok::<_, Error>(value)
-        })
-        .unwrap();
-
-    assert_eq!(study.n_trials(), 10);
-}
-
-#[test]
-fn test_suggest_bool_with_tpe() {
-    let sampler = TpeSampler::builder()
-        .seed(42)
-        .n_startup_trials(5)
-        .build()
-        .unwrap();
-
-    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
-
-    study
-        .optimize_with_sampler(20, |trial| {
-            let use_large = trial.suggest_bool("use_large")?;
-            let base = if use_large { 10.0 } else { 1.0 };
-            let x = trial.suggest_float("x", 0.0, base)?;
-            Ok::<_, Error>(x)
-        })
-        .unwrap();
-
-    let best = study.best_trial().unwrap();
-    // Best should prefer use_large=false for smaller range
-    assert!(best.value < 5.0);
-}
-
-// =============================================================================
-// Tests for suggest_range
-// =============================================================================
-
-#[test]
-fn test_suggest_range_float_exclusive() {
-    let mut trial = Trial::new(0);
-
-    let x = trial.suggest_range("x", 0.0..1.0).unwrap();
-    assert!(x >= 0.0 && x <= 1.0, "value {x} out of range 0.0..1.0");
-}
-
-#[test]
-fn test_suggest_range_float_inclusive() {
-    let mut trial = Trial::new(0);
-
-    let x = trial.suggest_range("x", 0.0..=1.0).unwrap();
-    assert!(x >= 0.0 && x <= 1.0, "value {x} out of range 0.0..=1.0");
-}
-
-#[test]
-fn test_suggest_range_int_exclusive() {
-    let mut trial = Trial::new(0);
-
-    // 1..10 means 1 to 9 inclusive
-    let n = trial.suggest_range("n", 1_i64..10).unwrap();
-    assert!(n >= 1 && n <= 9, "value {n} out of range 1..10 (should be 1-9)");
-}
-
-#[test]
-fn test_suggest_range_int_inclusive() {
-    let mut trial = Trial::new(0);
-
-    // 1..=10 means 1 to 10 inclusive
-    let n = trial.suggest_range("n", 1_i64..=10).unwrap();
-    assert!(
-        n >= 1 && n <= 10,
-        "value {n} out of range 1..=10 (should be 1-10)"
-    );
-}
-
-#[test]
-fn test_suggest_range_caching_float() {
-    let mut trial = Trial::new(0);
-
-    let x1 = trial.suggest_range("x", 0.0..1.0).unwrap();
-    let x2 = trial.suggest_range("x", 0.0..1.0).unwrap();
-
-    assert_eq!(x1, x2, "repeated suggest_range should return cached value");
-}
-
-#[test]
-fn test_suggest_range_caching_int() {
-    let mut trial = Trial::new(0);
-
-    let n1 = trial.suggest_range("n", 1_i64..=100).unwrap();
-    let n2 = trial.suggest_range("n", 1_i64..=100).unwrap();
-
-    assert_eq!(n1, n2, "repeated suggest_range should return cached value");
-}
-
-#[test]
-fn test_suggest_range_multiple_parameters() {
-    let mut trial = Trial::new(0);
-
-    let x = trial.suggest_range("x", 0.0..1.0).unwrap();
-    let y = trial.suggest_range("y", -5.0..=5.0).unwrap();
-    let n = trial.suggest_range("n", 1_i64..10).unwrap();
-    let m = trial.suggest_range("m", 100_i64..=200).unwrap();
-
-    // All should be cached independently
-    assert_eq!(x, trial.suggest_range("x", 0.0..1.0).unwrap());
-    assert_eq!(y, trial.suggest_range("y", -5.0..=5.0).unwrap());
-    assert_eq!(n, trial.suggest_range("n", 1_i64..10).unwrap());
-    assert_eq!(m, trial.suggest_range("m", 100_i64..=200).unwrap());
-}
-
-#[test]
-fn test_suggest_range_in_optimization() {
-    let study: Study<f64> = Study::new(Direction::Minimize);
-
-    study
-        .optimize(10, |trial| {
-            let x = trial.suggest_range("x", -10.0..10.0)?;
-            let n = trial.suggest_range("n", 1_i64..=5)?;
-            Ok::<_, Error>(x * x + n as f64)
-        })
-        .unwrap();
-
-    assert_eq!(study.n_trials(), 10);
-}
-
-#[test]
-fn test_suggest_range_with_tpe() {
-    let sampler = TpeSampler::builder()
-        .seed(42)
-        .n_startup_trials(5)
-        .build()
-        .unwrap();
-
-    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
-
-    study
-        .optimize_with_sampler(30, |trial| {
-            let x = trial.suggest_range("x", -5.0..=5.0)?;
-            let n = trial.suggest_range("n", 1_i64..=10)?;
-            Ok::<_, Error>(x * x + (n as f64 - 5.0).powi(2))
-        })
-        .unwrap();
-
-    let best = study.best_trial().unwrap();
-    // TPE should find near-optimal solution
-    assert!(best.value < 10.0, "TPE should find good solution");
-}
-
-#[test]
-fn test_suggest_range_empty_int_range_error() {
-    let mut trial = Trial::new(0);
-
-    // 5..5 is empty (no valid integers)
-    let result = trial.suggest_range("n", 5_i64..5);
-    assert!(
-        matches!(result, Err(Error::InvalidBounds { .. })),
-        "empty range should return InvalidBounds error"
-    );
-}
-
-#[test]
-fn test_suggest_range_single_value_int() {
-    let mut trial = Trial::new(0);
-
-    // 5..=5 has exactly one value: 5
-    let n = trial.suggest_range("n", 5_i64..=5).unwrap();
-    assert_eq!(n, 5, "single-value range should return that value");
-}
-
-#[test]
-fn test_suggest_range_single_value_float() {
-    let mut trial = Trial::new(0);
-
-    let x = trial.suggest_range("x", 3.14..=3.14).unwrap();
-    assert!(
-        (x - 3.14).abs() < f64::EPSILON,
-        "single-value range should return that value"
-    );
-}
Author	SHA1	Message	Date
Manuel Raimann	28b1687be6	chore: release v0.2.0	2026-01-30 18:40:03 +01:00
Manuel Raimann	fc0559a02d	Remove Serde	2026-01-30 18:38:43 +01:00
Manuel Raimann	6fb39d17c1	fix: remove unused dependency 'ordered-float' from Cargo.toml	2026-01-30 18:32:14 +01:00