Autoformat graph pydantic conversion code

profiling/graph_pydantic_conversion/benchmark_function.py

@@ -1,64 +1,67 @@
-import time
-import psutil
-import tracemalloc
 import statistics
-from typing import Callable, Any, Dict
+import time
+import tracemalloc
+from typing import Any, Callable, Dict
+
+import psutil
 
 
 def benchmark_function(func: Callable, *args, num_runs: int = 5) -> Dict[str, Any]:
     """
     Benchmark a function for memory usage and computational performance.
-    
+
     Args:
         func: Function to benchmark
         *args: Arguments to pass to the function
         num_runs: Number of times to run the benchmark
-    
+
     Returns:
         Dictionary containing benchmark metrics
     """
     execution_times = []
     peak_memory_usages = []
     cpu_percentages = []
-    
+
     process = psutil.Process()
-    
+
     for _ in range(num_runs):
         # Start memory tracking
         tracemalloc.start()
         initial_memory = process.memory_info().rss
-        
+
         # Measure execution time and CPU usage
         start_time = time.perf_counter()
         start_cpu_time = process.cpu_times()
-        
+
         result = func(*args)
-        
+
         end_cpu_time = process.cpu_times()
         end_time = time.perf_counter()
-        
+
         # Calculate metrics
         execution_time = end_time - start_time
-        cpu_time = (end_cpu_time.user + end_cpu_time.system) - (start_cpu_time.user + start_cpu_time.system)
+        cpu_time = (end_cpu_time.user + end_cpu_time.system) - (
+            start_cpu_time.user + start_cpu_time.system
+        )
         current, peak = tracemalloc.get_traced_memory()
         final_memory = process.memory_info().rss
         memory_used = final_memory - initial_memory
-        
+
         # Store results
         execution_times.append(execution_time)
         peak_memory_usages.append(peak / 1024 / 1024)  # Convert to MB
         cpu_percentages.append((cpu_time / execution_time) * 100)
-        
+
         tracemalloc.stop()
-        
+
     analysis = {
         "mean_execution_time": statistics.mean(execution_times),
         "mean_peak_memory_mb": statistics.mean(peak_memory_usages),
         "mean_cpu_percent": statistics.mean(cpu_percentages),
-        "num_runs": num_runs
+        "num_runs": num_runs,
     }
 
     if num_runs > 1:
         analysis["std_execution_time"] = statistics.stdev(execution_times)
 
-    return analysis
+    return analysis

profiling/graph_pydantic_conversion/profile_graph_pydantic_conversion.py

@@ -1,37 +1,43 @@
-import time
 import argparse
+import time
 
 from benchmark_function import benchmark_function
-from cognee.modules.graph.utils import get_graph_from_model
 
+from cognee.modules.graph.utils import get_graph_from_model
 from cognee.tests.unit.interfaces.graph.util import (
     PERSON_NAMES,
     create_organization_recursive,
 )
 
-
-
 # Example usage:
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description='Benchmark graph model with configurable recursive depth')
-    parser.add_argument('--recursive-depth', type=int, default=3,
-                       help='Recursive depth for graph generation (default: 3)')
-    parser.add_argument('--runs', type=int, default=5,
-                       help='Number of benchmark runs (default: 5)')
+    parser = argparse.ArgumentParser(
+        description="Benchmark graph model with configurable recursive depth"
+    )
+    parser.add_argument(
+        "--recursive-depth",
+        type=int,
+        default=3,
+        help="Recursive depth for graph generation (default: 3)",
+    )
+    parser.add_argument(
+        "--runs", type=int, default=5, help="Number of benchmark runs (default: 5)"
+    )
     args = parser.parse_args()
 
-
     society = create_organization_recursive(
         "society", "Society", PERSON_NAMES, args.recursive_depth
     )
     nodes, edges = get_graph_from_model(society)
-    
+
     results = benchmark_function(get_graph_from_model, society, num_runs=args.runs)
     print("\nBenchmark Results:")
-    print(f"N nodes: {len(nodes)}, N edges: {len(edges)}, Recursion depth: {args.recursive_depth}")
+    print(
+        f"N nodes: {len(nodes)}, N edges: {len(edges)}, Recursion depth: {args.recursive_depth}"
+    )
     print(f"Mean Peak Memory: {results['mean_peak_memory_mb']:.2f} MB")
     print(f"Mean CPU Usage: {results['mean_cpu_percent']:.2f}%")
     print(f"Mean Execution Time: {results['mean_execution_time']:.4f} seconds")
 
-    if 'std_execution_time' in results:
+    if "std_execution_time" in results:
         print(f"Execution Time Std: {results['std_execution_time']:.4f} seconds")