add test

Author: SimonBlanke

tests/test_parameter_docs_workflow.py

@@ -0,0 +1,85 @@
+#!/usr/bin/env python3
+"""
+Test script to demonstrate the complete parameter coverage documentation workflow.
+
+This script shows how to:
+1. Generate sample data for ML functions
+2. Create parameter coverage documentation
+3. Verify the documentation updates automatically
+"""
+
+import os
+import sys
+import tempfile
+import shutil
+
+# Add the src directory to Python path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))
+
+from surfaces.test_functions.machine_learning.tabular.regression.test_functions.k_neighbors_regressor import KNeighborsRegressorFunction
+
+
+def test_workflow():
+    """Test the complete workflow."""
+    print("🧪 Testing ML Parameter Coverage Documentation Workflow")
+    print("=" * 60)
+    
+    # Test 1: Generate documentation with no data
+    print("\n1. Testing documentation generation with no/minimal data...")
+    os.system("python generate_parameter_coverage_docs.py -f k_neighbors_regressor")
+    
+    # Test 2: Generate some sample data
+    print("\n2. Generating sample search data...")
+    
+    # Create a function instance and collect a small amount of data
+    func = KNeighborsRegressorFunction()
+    
+    # Use a smaller search space for testing
+    custom_search_space = {
+        'n_neighbors': [3, 5, 7],
+        'algorithm': ['auto', 'ball_tree'], 
+        'cv': [2, 3],
+        'dataset': func.search_space()['dataset'][:1]  # Just one dataset
+    }
+    
+    print(f"Collecting data with custom search space: {custom_search_space}")
+    print("(This will take a moment as it actually trains ML models...)")
+    
+    try:
+        stats = func.collect_search_data(
+            search_space=custom_search_space,
+            batch_size=5,
+            verbose=True
+        )
+        print(f"✅ Data collection completed: {stats['evaluations_collected']} new evaluations")
+    except Exception as e:
+        print(f"⚠️ Data collection failed: {e}")
+        print("This is normal if scikit-learn datasets are not available")
+    
+    # Test 3: Generate updated documentation
+    print("\n3. Generating updated documentation...")
+    os.system("python generate_parameter_coverage_docs.py -f k_neighbors_regressor")
+    
+    # Test 4: Test JSON output
+    print("\n4. Testing JSON output...")
+    os.system("python generate_parameter_coverage_docs.py -f k_neighbors_regressor --json")
+    
+    # Test 5: Test full documentation generation
+    print("\n5. Testing full documentation generation...")
+    os.system("python generate_parameter_coverage_docs.py")
+    
+    print("\n✅ Workflow test completed!")
+    print("\nGenerated files:")
+    print("- ML_PARAMETER_COVERAGE.md")
+    print("- docs/ML_PARAMETER_COVERAGE.md") 
+    print("- docs/ml_parameter_coverage.json")
+    
+    print(f"\n📊 View the generated documentation: docs/ML_PARAMETER_COVERAGE.md")
+    
+    # Show current database status
+    print("\n📈 Current database status:")
+    os.system("python collect_ml_search_data.py --list")
+
+
+if __name__ == "__main__":
+    test_workflow()