Initial commit before Ralph loop
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Andrew Charlwood
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"""
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Performance benchmark for the Patient Pathway Analysis tool.
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This script measures:
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1. Module import time
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2. Data loading time (SQLite)
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3. Analysis pipeline execution time
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4. Peak memory usage
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Run with: python -m tests.benchmark_performance
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"""
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import gc
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import sys
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import time
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import tracemalloc
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from datetime import date
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from pathlib import Path
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from typing import Any
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# Store results for final report
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results: dict[str, Any] = {}
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def measure_time(func, *args, **kwargs):
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"""Measure execution time of a function."""
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gc.collect() # Clean up before timing
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start = time.perf_counter()
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result = func(*args, **kwargs)
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elapsed = time.perf_counter() - start
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return result, elapsed
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def measure_memory(func, *args, **kwargs):
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"""Measure peak memory usage of a function."""
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gc.collect() # Clean up before measuring
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tracemalloc.start()
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result = func(*args, **kwargs)
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current, peak = tracemalloc.get_traced_memory()
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tracemalloc.stop()
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return result, peak
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def benchmark_imports():
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"""Benchmark module import times."""
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print("\n" + "=" * 60)
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print("1. MODULE IMPORT BENCHMARKS")
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print("=" * 60)
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import_times = {}
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# Benchmark core imports
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start = time.perf_counter()
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from core import PathConfig, AnalysisFilters, default_paths
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import_times['core'] = time.perf_counter() - start
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# Benchmark data_processing imports
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start = time.perf_counter()
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from data_processing import DatabaseManager, get_loader
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import_times['data_processing'] = time.perf_counter() - start
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# Benchmark analysis imports
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start = time.perf_counter()
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from analysis.pathway_analyzer import generate_icicle_chart
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import_times['analysis'] = time.perf_counter() - start
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# Benchmark visualization imports
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start = time.perf_counter()
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from visualization.plotly_generator import create_icicle_figure
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import_times['visualization'] = time.perf_counter() - start
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# Benchmark pandas/numpy
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start = time.perf_counter()
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import pandas as pd
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import numpy as np
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import_times['pandas+numpy'] = time.perf_counter() - start
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total_import_time = sum(import_times.values())
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print(f"\n{'Module':<25} {'Time (ms)':<15}")
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print("-" * 40)
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for module, elapsed in import_times.items():
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print(f"{module:<25} {elapsed*1000:>10.1f} ms")
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print("-" * 40)
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print(f"{'TOTAL':<25} {total_import_time*1000:>10.1f} ms")
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results['import_times'] = import_times
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results['total_import_time'] = total_import_time
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return import_times
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def benchmark_data_loading():
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"""Benchmark data loading from different sources."""
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print("\n" + "=" * 60)
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print("2. DATA LOADING BENCHMARKS")
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print("=" * 60)
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from data_processing import get_loader
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from core import default_paths
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import pandas as pd
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load_times = {}
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row_counts = {}
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# Check if SQLite database exists
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db_path = default_paths.data_dir / "pathways.db"
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if db_path.exists():
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print(f"\nLoading from SQLite: {db_path}")
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# SQLite loading
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loader = get_loader('sqlite')
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result, elapsed = measure_time(loader.load)
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load_times['sqlite'] = elapsed
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row_counts['sqlite'] = result.row_count if result is not None else 0
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print(f" Rows loaded: {row_counts['sqlite']:,}")
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print(f" Time: {elapsed*1000:.1f} ms ({elapsed:.2f} seconds)")
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print(f" Internal load time: {result.load_time_seconds*1000:.1f} ms")
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# Store for later use
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results['loaded_df'] = result.df
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else:
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print(f"SQLite database not found at {db_path}")
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load_times['sqlite'] = None
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results['load_times'] = load_times
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results['row_counts'] = row_counts
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return load_times
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def benchmark_analysis_pipeline():
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"""Benchmark the full analysis pipeline."""
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print("\n" + "=" * 60)
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print("3. ANALYSIS PIPELINE BENCHMARKS")
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print("=" * 60)
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from analysis.pathway_analyzer import (
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generate_icicle_chart,
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prepare_data,
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calculate_statistics,
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build_hierarchy,
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prepare_chart_data,
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)
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from core import default_paths
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import pandas as pd
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# Get loaded data or load it
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df = results.get('loaded_df')
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if df is None or len(df) == 0:
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print("No data available for analysis benchmarks")
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return {}
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analysis_times = {}
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# Get available trusts, drugs, directories from data
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trusts = df['Provider Code'].unique().tolist()[:10] # Limit to 10 trusts
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drugs = ['ADALIMUMAB', 'ETANERCEPT', 'INFLIXIMAB', 'SECUKINUMAB', 'RITUXIMAB']
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directories = df['Directory'].dropna().unique().tolist()
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# Filter to drugs that exist in data
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available_drugs = [d for d in drugs if d in df['Drug Name'].values]
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if not available_drugs:
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available_drugs = df['Drug Name'].unique().tolist()[:5]
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print(f"\nAnalysis parameters:")
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print(f" Trusts: {len(trusts)}")
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print(f" Drugs: {available_drugs}")
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print(f" Directories: {len(directories)}")
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print(f" Data rows: {len(df):,}")
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# Load org_codes for mapping trust codes to names
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org_codes = pd.read_csv(default_paths.org_codes_csv, index_col=1)
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trust_names = []
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for t in trusts:
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if t in org_codes.index:
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trust_names.append(org_codes.loc[t, 'Name'])
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if not trust_names:
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trust_names = org_codes['Name'].tolist()[:10]
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# Benchmark full pipeline
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print("\n Running full pipeline benchmark...")
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# Use date range that should include data
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# Look at actual data dates
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if 'Intervention Date' in df.columns:
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min_date = df['Intervention Date'].min()
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max_date = df['Intervention Date'].max()
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print(f" Data date range: {min_date} to {max_date}")
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# Use a reasonable analysis window
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start_date = "2020-01-01"
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end_date = "2025-01-01"
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last_seen_date = "2020-01-01"
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else:
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start_date = "2020-01-01"
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end_date = "2025-01-01"
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last_seen_date = "2020-01-01"
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print(f" Analysis window: {start_date} to {end_date}")
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print(f" Last seen filter: > {last_seen_date}")
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# Full pipeline with memory tracking
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gc.collect()
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tracemalloc.start()
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start_time = time.perf_counter()
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try:
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ice_df, title = generate_icicle_chart(
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df=df,
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start_date=start_date,
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end_date=end_date,
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last_seen_date=last_seen_date,
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trust_filter=trust_names,
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drug_filter=available_drugs,
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directory_filter=directories,
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minimum_num_patients=1,
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title="Performance Benchmark",
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paths=default_paths,
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)
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elapsed = time.perf_counter() - start_time
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current, peak = tracemalloc.get_traced_memory()
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tracemalloc.stop()
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analysis_times['full_pipeline'] = elapsed
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results['analysis_memory_peak'] = peak
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if ice_df is not None:
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print(f"\n Pipeline completed:")
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print(f" Execution time: {elapsed*1000:.1f} ms ({elapsed:.2f} seconds)")
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print(f" Peak memory: {peak / 1024 / 1024:.1f} MB")
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print(f" Result rows: {len(ice_df)}")
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print(f" Chart title: {title}")
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else:
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print("\n Pipeline returned no data (likely date filtering)")
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print(f" Execution time: {elapsed*1000:.1f} ms")
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except Exception as e:
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tracemalloc.stop()
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print(f"\n Pipeline error: {e}")
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traceback_str = ''.join(tracemalloc.format_exc() if hasattr(tracemalloc, 'format_exc') else [])
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print(f" {str(e)}")
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analysis_times['full_pipeline'] = None
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results['analysis_times'] = analysis_times
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return analysis_times
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def benchmark_visualization():
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"""Benchmark chart generation."""
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print("\n" + "=" * 60)
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print("4. VISUALIZATION BENCHMARKS")
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print("=" * 60)
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from visualization.plotly_generator import create_icicle_figure
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import pandas as pd
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import numpy as np
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viz_times = {}
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# Create sample data for visualization benchmark
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n_rows = 1000
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sample_data = {
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'parents': ['N&WICS'] * n_rows,
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'ids': [f'N&WICS - Test{i}' for i in range(n_rows)],
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'labels': [f'Test{i}' for i in range(n_rows)],
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'value': np.random.randint(1, 100, n_rows),
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'colour': np.random.random(n_rows),
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'cost': np.random.randint(1000, 100000, n_rows),
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'costpp': np.random.randint(100, 10000, n_rows),
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'cost_pp_pa': [str(np.random.randint(100, 10000)) for _ in range(n_rows)],
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'First seen': pd.to_datetime(['2024-01-01'] * n_rows),
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'Last seen': pd.to_datetime(['2024-12-31'] * n_rows),
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'First seen (Parent)': ['2024-01-01'] * n_rows,
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'Last seen (Parent)': ['2024-12-31'] * n_rows,
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'average_spacing': ['Test spacing'] * n_rows,
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'avg_days': pd.to_timedelta([100] * n_rows, unit='D'),
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}
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sample_df = pd.DataFrame(sample_data)
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print(f"\n Sample data: {n_rows} rows")
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# Benchmark figure creation
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fig, elapsed = measure_time(create_icicle_figure, sample_df, "Benchmark Test")
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viz_times['figure_creation'] = elapsed
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print(f" Figure creation: {elapsed*1000:.1f} ms")
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results['viz_times'] = viz_times
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return viz_times
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def print_summary():
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"""Print final summary report."""
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print("\n" + "=" * 60)
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print("PERFORMANCE SUMMARY")
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print("=" * 60)
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print("\nRESULTS:")
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# Import times
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if 'total_import_time' in results:
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print(f"\n Import time (all modules): {results['total_import_time']*1000:.1f} ms")
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# Data loading
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if 'load_times' in results and results['load_times'].get('sqlite'):
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print(f" SQLite load time: {results['load_times']['sqlite']*1000:.1f} ms")
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if 'row_counts' in results:
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print(f" Rows loaded: {results['row_counts'].get('sqlite', 0):,}")
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# Analysis
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if 'analysis_times' in results and results['analysis_times'].get('full_pipeline'):
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print(f" Analysis pipeline: {results['analysis_times']['full_pipeline']*1000:.1f} ms")
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# Memory
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if 'analysis_memory_peak' in results:
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print(f" Peak memory (analysis): {results['analysis_memory_peak'] / 1024 / 1024:.1f} MB")
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# Visualization
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if 'viz_times' in results:
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print(f" Figure creation: {results['viz_times'].get('figure_creation', 0)*1000:.1f} ms")
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# Calculate total startup time (imports + data loading)
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startup_time = results.get('total_import_time', 0)
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if results.get('load_times', {}).get('sqlite'):
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startup_time += results['load_times']['sqlite']
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print(f"\n Estimated startup time: {startup_time*1000:.1f} ms ({startup_time:.2f} seconds)")
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print("\n" + "=" * 60)
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def main():
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"""Run all benchmarks."""
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print("\n" + "=" * 60)
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print("PATIENT PATHWAY ANALYSIS - PERFORMANCE BENCHMARK")
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print("=" * 60)
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print(f"\nPython version: {sys.version}")
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print(f"Platform: {sys.platform}")
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# Run benchmarks in order
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benchmark_imports()
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benchmark_data_loading()
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benchmark_analysis_pipeline()
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benchmark_visualization()
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# Print summary
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print_summary()
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return results
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if __name__ == "__main__":
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main()
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