Merge pull request #181 from richardcsuwandi/main

codelion · web-flow · commit 599cd5403674 · 2025-08-13T10:11:52.000+08:00
Add AlgoTune tasks for OpenEvolve
diff --git a/examples/algotune/README.md b/examples/algotune/README.md
@@ -0,0 +1,188 @@
+# AlgoTune Task Adapter for OpenEvolve
+
+This directory contains tools to convert AlgoTune tasks to OpenEvolve format for evolutionary optimization.
+
+## Overview
+
+The `AlgoTuneTaskAdapter` extracts tasks from an external AlgoTune repository and converts them to OpenEvolve format, creating:
+- `initial_program.py` - The initial implementation to be evolved
+- `evaluator.py` - Evaluation logic with baseline comparison
+- `config.yaml` - OpenEvolve configuration
+
+## Prerequisites
+
+**AlgoTune Repository**: Clone the AlgoTune repository
+   ```bash
+   git clone https://github.com/oripress/AlgoTune.git
+   ```
+
+
+## Usage
+
+### Basic Usage
+
+```python
+from task_adapter import AlgoTuneTaskAdapter
+
+# Initialize with AlgoTune repository path
+adapter = AlgoTuneTaskAdapter(algotune_path="/path/to/AlgoTune")
+
+# List available tasks
+tasks = adapter.list_available_tasks()
+print(f"Available tasks: {tasks}")
+
+# Create OpenEvolve files for a specific task
+output_dir = adapter.create_task_files("svm")
+print(f"Created files in: {output_dir}")
+```
+
+### Command Line Usage
+
+#### List Available Tasks
+```bash
+python generate_all_tasks.py --algotune-path /path/to/AlgoTune --list
+```
+
+#### Generate Files for a Specific Task
+```bash
+python create_task.py --algotune-path /path/to/AlgoTune --task svm
+```
+
+#### Generate All Tasks
+```bash
+python generate_all_tasks.py --algotune-path /path/to/AlgoTune
+```
+
+## File Structure
+
+For each task, the adapter creates:
+
+```
+task_name/
+├── initial_program.py    # Initial implementation to evolve
+├── evaluator.py         # Evaluation logic with baseline comparison
+└── config.yaml          # OpenEvolve configuration
+```
+
+## Configuration
+
+The adapter automatically generates OpenEvolve configurations with:
+- Baseline comparison against original AlgoTune implementations
+- Speedup-based fitness scoring
+- Cascade evaluation for efficiency
+- Task-specific problem generation
+
+## Evaluation Features
+
+The generated evaluators include:
+- **Baseline Comparison**: Compares evolved solutions against original AlgoTune implementations
+- **Speedup Measurement**: Calculates performance improvements
+- **Correctness Validation**: Ensures solutions are valid
+
+## Example: SVM Task
+
+Here's an example of how the adapter transforms an AlgoTune SVM task:
+
+### Generated Initial Program (`initial_program.py`)
+
+```python
+# EVOLVE-BLOCK-START
+"""
+SVM Task
+Given labels y ∈ {-1, 1}^n and a feature matrix X ∈ R^{n x p} with rows x_1,...,x_n, 
+solve the support vector machine (SVM) task
+
+min        1/2 || β ||_2^2 + C sum_{i=1}^n ξ_i
+β,β_0,ξ  
+
+subject to ξ_i ≥ 0, i = 1,...,n
+	   y_i (x_i^T β + β_0) ≥ 1 - ξ_i, i = 1,...,n
+
+Input: Dictionary with keys "X", "y", "C"
+Output: Dictionary with keys "beta0", "beta", "optimal_value", "misclass_error"
+"""
+import cvxpy as cp
+import numpy as np
+
+class SVMTask:
+    def solve(self, problem):
+        """Solve the SVM problem using CVXPY."""
+        X = np.array(problem["X"])
+        y = np.array(problem["y"])[:, None]
+        C = float(problem["C"])
+        
+        p, n = X.shape[1], X.shape[0]
+        beta = cp.Variable((p, 1))
+        beta0 = cp.Variable()
+        xi = cp.Variable((n, 1))
+        
+        objective = cp.Minimize(0.5 * cp.sum_squares(beta) + C * cp.sum(xi))
+        constraints = [
+            xi >= 0,
+            cp.multiply(y, X @ beta + beta0) >= 1 - xi,
+        ]
+        
+        problem_cp = cp.Problem(objective, constraints)
+        problem_cp.solve()
+        
+        return {
+            "beta0": float(beta0.value),
+            "beta": beta.value.flatten().tolist(),
+            "optimal_value": float(problem_cp.value),
+            "misclass_error": self._compute_misclassification_error(X, y, beta.value, beta0.value)
+        }
+# EVOLVE-BLOCK-END
+```
+
+### Generated Configuration (`config.yaml`)
+
+```yaml
+# Configuration for svm task
+max_iterations: 100
+checkpoint_interval: 10
+
+# LLM configuration
+llm:
+  primary_model: "gpt-4o-mini"
+  temperature: 0.7
+  max_tokens: 4096
+
+# Prompt configuration
+prompt:
+  system_message: "You are an expert programmer specializing in convex_optimization algorithms. Your task is to improve the svm algorithm implementation..."
+
+# AlgoTune task-specific configuration
+algotune:
+  num_trials: 5
+  data_size: 5
+  timeout: 30
+```
+
+### Generated Evaluator (`evaluator.py`)
+
+The evaluator:
+- Loads the evolved solve method from `initial_program.py`
+- Generates test problems using the original AlgoTune task
+- Runs the evolved solution and measures performance
+- Validates correctness using the original task's validation method
+- Compares against reference solutions from AlgoTune
+
+## Files
+
+- `task_adapter.py` - Main adapter that converts AlgoTune tasks to OpenEvolve format
+- `create_task.py` - Script to create OpenEvolve files for a single task
+- `generate_all_tasks.py` - Script to generate all 155 AlgoTune tasks
+
+## Integration with OpenEvolve
+
+Once files are generated, you can run OpenEvolve:
+
+```bash
+# Navigate to the task directory
+cd svm/
+
+# Run OpenEvolve on the task
+ python ../../../openevolve-run.py ./initial_program.py ./evaluator.py \
+    --config ./config.yaml \
+    --output openevolve_output/
+```
diff --git a/examples/algotune/create_task.py b/examples/algotune/create_task.py
@@ -0,0 +1,73 @@
+#!/usr/bin/env python3
+"""
+Script to create OpenEvolve task files from AlgoTune tasks.
+
+This script demonstrates how to use the AlgoTuneTaskAdapter to convert
+AlgoTune tasks to OpenEvolve format using external AlgoTune repositories.
+"""
+
+import sys
+import argparse
+from pathlib import Path
+from task_adapter import AlgoTuneTaskAdapter
+
+def main():
+    """Main function to create OpenEvolve task files."""
+    
+    parser = argparse.ArgumentParser(description="Create OpenEvolve task files from AlgoTune tasks")
+    parser.add_argument(
+        "--task",
+        type=str,
+        required=True,
+        help="Task name to create OpenEvolve files for"
+    )
+    
+    parser.add_argument(
+        "--algotune-path",
+        type=str,
+        required=True,
+        help="Path to AlgoTune repository directory (e.g., /path/to/AlgoTune)"
+    )
+
+    args = parser.parse_args()
+    
+    try:
+        # Initialize the adapter with AlgoTune path
+        adapter = AlgoTuneTaskAdapter(algotune_path=args.algotune_path, task=args.task)
+        
+        # List available tasks
+        available_tasks = adapter.list_available_tasks()
+        print(f"Available tasks: {len(available_tasks)}")
+        print(available_tasks)
+        
+        task_name = args.task
+        
+        # Check if the task exists
+        if task_name not in available_tasks:
+            print(f"Error: Task '{task_name}' not found")
+            print(f"Available tasks: {available_tasks[:10]}...")
+            return 1
+        
+        # Create the OpenEvolve files
+        print(f"\nCreating OpenEvolve files for task: {task_name}")
+        output_dir = adapter.create_task_files(task_name)
+        print(f"✅ Created files in: {output_dir}")
+        
+        # List the created files
+        output_path = Path(output_dir)
+        created_files = list(output_path.glob("*"))
+        print("Created files:")
+        for file in created_files:
+            print(f"  - {file.name}")
+        
+        print(f"\n✅ Successfully created OpenEvolve files for '{task_name}'")
+        return 0
+    
+    except Exception as e:
+        print(f"Error: {e}")
+        print("\nExample usage:")
+        print("  python create_task.py --algotune-path /path/to/AlgoTune --task svm")
+        return 1
+
+if __name__ == "__main__":
+    sys.exit(main()) 
diff --git a/examples/algotune/generate_all_tasks.py b/examples/algotune/generate_all_tasks.py
@@ -0,0 +1,86 @@
+#!/usr/bin/env python3
+"""
+Script to generate OpenEvolve task files for all AlgoTune tasks.
+
+This script uses the AlgoTuneTaskAdapter to convert all available AlgoTune tasks
+to OpenEvolve format using external AlgoTune repositories.
+"""
+
+import sys
+import argparse
+from pathlib import Path
+from task_adapter import AlgoTuneTaskAdapter
+
+def main():
+    """Main function to generate OpenEvolve task files."""
+    
+    parser = argparse.ArgumentParser(description="Generate OpenEvolve task files from AlgoTune tasks")
+    parser.add_argument(
+        "--algotune-path",
+        type=str,
+        required=True,
+        help="Path to AlgoTune repository directory (e.g., /path/to/AlgoTune)"
+    )
+    parser.add_argument(
+        "--list",
+        action="store_true",
+        help="List all available tasks"
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=str,
+        help="Output directory for generated files (default: task name subdirectories)"
+    )
+    
+    args = parser.parse_args()
+    
+    try:
+        # Initialize the adapter with AlgoTune path
+        adapter = AlgoTuneTaskAdapter(algotune_path=args.algotune_path)
+        
+        # List available tasks
+        available_tasks = adapter.list_available_tasks()
+        print(f"Found {len(available_tasks)} available AlgoTune tasks")
+        
+        if args.list:
+            print("\nAvailable tasks:")
+            for i, task_name in enumerate(available_tasks, 1):
+                print(f"  {i:3d}. {task_name}")
+            return 0
+        
+        # Generate files for all tasks
+        tasks_to_process = available_tasks
+        print(f"\nGenerating OpenEvolve files for {len(tasks_to_process)} tasks...")
+        
+        successful = 0
+        failed = 0
+        
+        for i, task_name in enumerate(tasks_to_process, 1):
+            try:
+                print(f"[{i:3d}/{len(tasks_to_process)}] Processing {task_name}...")
+                output_dir = adapter.create_task_files(task_name, args.output_dir)
+                print(f"  ✅ Success: {output_dir}")
+                successful += 1
+            except Exception as e:
+                print(f"  ❌ Failed: {e}")
+                failed += 1
+        
+        print(f"\nSummary:")
+        print(f"  Successful: {successful}")
+        print(f"  Failed: {failed}")
+        print(f"  Total: {len(tasks_to_process)}")
+        
+        if failed > 0:
+            print(f"\nSome tasks failed to generate. Check the errors above.")
+            return 1
+        
+        return 0
+    
+    except Exception as e:
+        print(f"Error: {e}")
+        print("\nExample usage:")
+        print("  python generate_all_tasks.py --algotune-path /path/to/AlgoTune")
+        return 1
+
+if __name__ == "__main__":
+    sys.exit(main()) 
diff --git a/examples/algotune/requirements.txt b/examples/algotune/requirements.txt
@@ -0,0 +1,36 @@
+botorch>=0.10.0
+orjson>=3.11.1
+cvxopt>=1.3.2
+numpy
+pandas
+cython
+numba
+dask
+pulp
+scipy
+ortools>=9.7.0,<9.8.0
+pyomo
+highspy
+networkx
+python-sat
+jax
+diffrax
+sympy
+faiss-cpu
+cryptography
+scikit-learn
+hdbscan
+cvxpy
+torch
+pot
+ecos
+litellm
+google-generativeai
+pylint
+line_profiler
+toml
+pyaml
+pillow
+pythran
+dace
+psutil
diff --git a/examples/algotune/task_adapter.py b/examples/algotune/task_adapter.py
diff --git a/openevolve/_version.py b/openevolve/_version.py
