pre-commit

Author: SimonBlanke

.github/workflows/test.yml

@@ -84,7 +84,7 @@ jobs:
  run: python -m pip list
 
  - name: Test core package
- run: | 
+ run: |
  python -m pytest src/hyperactive -p no:warnings
 
  test-all-extras:
@@ -155,7 +155,7 @@ jobs:
  name: test-examples
  runs-on: ubuntu-latest
  timeout-minutes: 15
- 
+
  steps:
  - uses: actions/checkout@v4
  with:
@@ -176,7 +176,7 @@ jobs:
  # For pull requests, compare with base branch
  CHANGED_FILES=$(git diff --name-only ${{ github.event.pull_request.base.sha }} ${{ github.sha }} | grep "^examples/" || true)
  fi
- 
+
  if [ -n "$CHANGED_FILES" ]; then
  echo "examples_changed=true" >> $GITHUB_OUTPUT
  echo "Examples changed:"

CLAUDE.md

@@ -0,0 +1,128 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+Hyperactive is an optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models. It provides various optimization algorithms including random search, grid search, Bayesian optimization, and many others for hyperparameter tuning and general optimization problems.
+
+## Architecture
+
+### Core Components
+
+- **`src/hyperactive/`** - Main package with src-layout structure
+ - **`base/`** - Base classes for experiments and optimizers using skbase framework
+ - **`opt/`** - Optimization algorithms organized by backend:
+ - **`gfo/`** - Gradient-Free-Optimizers backend algorithms
+ - **`optuna/`** - Optuna backend integration
+ - **`gridsearch/`** - Scikit-learn grid search integration
+ - **`integrations/`** - Framework integrations (sklearn, sktime)
+ - **`experiment/`** - Experiment definitions and benchmark functions
+ - **`utils/`** - Utility functions for parallel computing and validation
+
+### Key Design Patterns
+
+- Uses **skbase** framework for base classes with tagging system for metadata
+- **Plugin architecture** - optimizers and experiments inherit from base classes
+- **Multiple backends** - supports GFO, Optuna, and sklearn optimization backends
+- **Integration layer** - separate modules for ML framework integrations
+
+## Development Commands
+
+### Build and Install
+```bash
+make build # Build the package using python -m build
+make install # Install from built wheel
+make install-editable # Install in development mode
+make reinstall # Uninstall and reinstall
+```
+
+### Testing
+```bash
+make test # Run all tests (src, pytest, local)
+make test-pytest # Run main test suite with pytest
+make test-src # Run tests within src/hyperactive/
+make test-extensive # Run comprehensive tests including examples
+make test-examples # Test all example files
+```
+
+### Code Quality
+```bash
+make lint # Check code with ruff linter
+make lint-fix # Auto-fix linting issues
+make format # Format code with ruff
+make format-check # Check formatting without changes
+make check # Run all quality checks (lint + format + imports)
+make fix # Fix all auto-fixable issues
+```
+
+### Dependencies
+```bash
+make install-test-requirements # Install test dependencies
+make install-all-extras-for-test # Install all optional dependencies for testing
+```
+
+## Testing Structure
+
+- **`tests/`** - Main test directory (currently minimal, mostly in src/)
+- **`src/hyperactive/*/tests/`** - Component-specific tests within source code
+- **`examples/test_examples.py`** - Example validation tests
+- Uses **pytest** as the test runner
+- Test configuration in `pyproject.toml` under `[tool.test]` dependencies
+
+## Key Configuration Files
+
+- **`pyproject.toml`** - Modern Python packaging configuration with dependencies, build system, and ruff configuration
+- **`Makefile`** - Comprehensive build, test, and quality commands
+- **`.github/workflows/`** - CI/CD workflows for automated testing
+
+## Code Style and Linting
+
+- Uses **ruff** for linting and formatting (configured in pyproject.toml)
+- Follows **numpy docstring convention**
+- Python 3.9+ support
+- Line length: 88 characters
+- Import sorting with ruff's isort functionality
+
+## Dependencies
+
+### Core Dependencies
+- `numpy`, `pandas` - Data handling
+- `tqdm` - Progress bars
+- `gradient-free-optimizers` - Main optimization backend
+- `scikit-base` - Base class framework
+- `scikit-learn` - ML integration
+
+### Optional Dependencies
+- `sklearn-integration` - Enhanced sklearn support
+- `sktime-integration` - Time series forecasting
+- `test` - Testing dependencies (pytest, flake8, etc.)
+- `all_extras` - All optional features
+
+## Examples Structure
+
+- **`examples/gfo/`** - Gradient-Free-Optimizers examples
+- **`examples/optuna/`** - Optuna backend examples 
+- **`examples/sklearn/`** - Scikit-learn integration examples
+- Each example directory has its own README.md
+
+## Common Development Tasks
+
+### Adding New Optimizers
+1. Create optimizer class in appropriate `opt/` subdirectory
+2. Inherit from `BaseOptimizer` and implement required methods
+3. Add appropriate tags for metadata
+4. Register in `_registry/` if needed
+5. Add tests in corresponding `tests/` directory
+6. Add usage example in `examples/`
+
+### Running Single Tests
+```bash
+python -m pytest tests/specific_test.py -v
+python -m pytest src/hyperactive/component/tests/test_file.py
+```
+
+### Version Management
+- Version defined in `pyproject.toml`
+- Uses `importlib.metadata` for runtime version access
+- Current version: 4.8.1

examples/gfo/README.md

@@ -44,7 +44,7 @@ python bayesian_optimization_example.py
 - **RandomSearch**: Pure random sampling - excellent baseline
 - **GridSearch**: Exhaustive enumeration of discrete grids
 
-### Local Search Methods 
+### Local Search Methods
 - **HillClimbing**: Greedy local search from starting points
 - **SimulatedAnnealing**: Hill climbing with probabilistic escapes
 - **StochasticHillClimbing**: Hill climbing with random moves
@@ -55,7 +55,7 @@ python bayesian_optimization_example.py
 - **DifferentialEvolution**: Evolution strategy for continuous spaces
 
 ### Advanced Methods
-- **BayesianOptimizer**: Gaussian Process-based optimization 
+- **BayesianOptimizer**: Gaussian Process-based optimization
 - **TreeStructuredParzenEstimators**: TPE algorithm (similar to Optuna's TPE)
 - **ForestOptimizer**: Random forest-based surrogate optimization
 
@@ -64,7 +64,7 @@ python bayesian_optimization_example.py
 ### Quick Decision Tree
 
 1. **Need a baseline?** → RandomSearch
-2. **Small discrete space?** → GridSearch 
+2. **Small discrete space?** → GridSearch
 3. **Expensive evaluations?** → BayesianOptimizer
 4. **Continuous optimization?** → ParticleSwarmOptimizer or BayesianOptimizer
 5. **Fast local optimization?** → HillClimbing
@@ -92,7 +92,7 @@ python bayesian_optimization_example.py
 All GFO algorithms support:
 - **Random state**: `random_state=42` for reproducibility
 - **Early stopping**: `early_stopping=10` trials without improvement
-- **Max score**: `max_score=0.99` stop when target reached 
+- **Max score**: `max_score=0.99` stop when target reached
 - **Warm start**: `initialize={"warm_start": [points]}` initial solutions
 
 ## Advanced Usage
@@ -103,7 +103,7 @@ All GFO algorithms support:
 random_opt = RandomSearch(n_trials=20, ...)
 initial_results = random_opt.solve()
 
-# Phase 2: Local refinement 
+# Phase 2: Local refinement
 hill_opt = HillClimbing(
  n_trials=30,
  initialize={"warm_start": [initial_results]}
@@ -117,13 +117,13 @@ final_results = hill_opt.solve()
 ```python
 param_space = {
  "learning_rate": (0.001, 0.1), # Log scale recommended
- "n_estimators": (10, 1000), # Integer range 
+ "n_estimators": (10, 1000), # Integer range
  "regularization": (0.0, 1.0), # Bounded continuous
 }
 ```
 
 **Discrete/Categorical:**
-```python 
+```python
 param_space = {
  "algorithm": ["adam", "sgd", "rmsprop"], # Categorical
  "layers": [1, 2, 3, 4, 5], # Discrete integers
@@ -144,4 +144,4 @@ param_space = {
 - [Gradient-Free Optimization Overview](https://en.wikipedia.org/wiki/Derivative-free_optimization)
 - [Bayesian Optimization Tutorial](https://arxiv.org/abs/1807.02811)
 - [Evolutionary Algorithms Survey](https://ieeexplore.ieee.org/document/6900297)
-- [Hyperparameter Optimization Review](https://arxiv.org/abs/1502.02127)
+- [Hyperparameter Optimization Review](https://arxiv.org/abs/1502.02127)

examples/gfo/bayesian_optimization_example.py

@@ -3,7 +3,7 @@
 
 Bayesian optimization uses a probabilistic model (typically Gaussian Process) to
 model the objective function and an acquisition function to decide where to sample
-next. This approach is highly sample-efficient and particularly useful when 
+next. This approach is highly sample-efficient and particularly useful when
 function evaluations are expensive.
 
 Characteristics:

examples/gfo/differential_evolution_example.py

@@ -56,4 +56,4 @@
 # Results
 print("\n=== Results ===")
 print(f"Best parameters: {best_params}")
-print("Differential evolution optimization completed successfully")
+print("Differential evolution optimization completed successfully")

examples/gfo/downhill_simplex_example.py

@@ -61,4 +61,4 @@
 # Results
 print("\n=== Results ===")
 print(f"Best parameters: {best_params}")
-print("Downhill simplex optimization completed successfully")
+print("Downhill simplex optimization completed successfully")

examples/gfo/evolution_strategy_example.py

@@ -56,4 +56,4 @@
 # Results
 print("\n=== Results ===")
 print(f"Best parameters: {best_params}")
-print("Evolution strategy optimization completed successfully")
+print("Evolution strategy optimization completed successfully")

examples/gfo/genetic_algorithm_example.py

@@ -57,4 +57,4 @@
 # Results
 print("\n=== Results ===")
 print(f"Best parameters: {best_params}")
-print("Genetic algorithm optimization completed successfully")
+print("Genetic algorithm optimization completed successfully")

examples/gfo/grid_search_example.py

@@ -62,4 +62,4 @@
 print("\n=== Results ===")
 print(f"Best parameters: {best_params}")
 print(f"Evaluated {total_combinations} parameter combinations")
-print("Grid search completed successfully")
+print("Grid search completed successfully")

examples/gfo/hill_climbing_example.py

@@ -1,7 +1,7 @@
 """
 Hill Climbing Example - Local Search Optimization
 
-Hill climbing is a local search algorithm that starts from a random point and 
+Hill climbing is a local search algorithm that starts from a random point and
 iteratively moves to neighboring solutions with better objective values. It's
 a simple but effective optimization strategy that can quickly find good local
 optima, especially when started from reasonable initial points.
@@ -33,7 +33,7 @@
 # Define search space - discrete values for hill climbing
 search_space = {
  "n_estimators": list(range(10, 201)), # Discrete integer values
- "max_depth": list(range(1, 21)), # Discrete integer values 
+ "max_depth": list(range(1, 21)), # Discrete integer values
  "min_samples_split": list(range(2, 21)), # Discrete integer values
  "min_samples_leaf": list(range(1, 11)), # Discrete integer values
 }
@@ -60,4 +60,4 @@
 # Results
 print("\n=== Results ===")
 print(f"Best parameters: {best_params}")
-print("Hill climbing optimization completed successfully")
+print("Hill climbing optimization completed successfully")