367 lines
No EOL
16 KiB
Python
367 lines
No EOL
16 KiB
Python
import pandas as pd
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import seaborn as sns
|
|
import os
|
|
import glob
|
|
import argparse
|
|
from pathlib import Path
|
|
|
|
def parse_arguments():
|
|
parser = argparse.ArgumentParser(description='Cross-experiment analysis of chain performance.')
|
|
parser.add_argument('--experiments-dir', '-e', required=True,
|
|
help='Path to directory containing experiment subdirectories')
|
|
parser.add_argument('--supplementary', '-s', required=True,
|
|
help='Path to supplementary.csv file with input delays')
|
|
parser.add_argument('--output', '-o', default='cross_experiment_analysis',
|
|
help='Output filename prefix for the plots (will add experiment type and .png)')
|
|
parser.add_argument('--experiment-duration', '-d', type=int, default=20,
|
|
help='Duration of each experiment in seconds (default: 20)')
|
|
return parser.parse_args()
|
|
|
|
def load_supplementary_data(supplementary_path):
|
|
"""Load the supplementary data with input delays and theoretical perfect times for each chain."""
|
|
supp_df = pd.read_csv(supplementary_path)
|
|
# Create dictionaries for quick lookup
|
|
delay_dict = dict(zip(supp_df['chain'], supp_df['input_delay']))
|
|
|
|
# Load theoretical perfect e2e time (assuming the third column exists)
|
|
if len(supp_df.columns) >= 3:
|
|
perfect_time_dict = dict(zip(supp_df['chain'], supp_df.iloc[:, 2])) # Third column
|
|
return delay_dict, perfect_time_dict
|
|
else:
|
|
print("Warning: No third column found for theoretical perfect times. Using input_delay as fallback.")
|
|
perfect_time_dict = delay_dict.copy() # Fallback to input_delay
|
|
return delay_dict, perfect_time_dict
|
|
|
|
def calculate_theoretical_max_runs(chain, input_delay_ms, experiment_duration_s):
|
|
"""Calculate the theoretical maximum number of runs for a chain."""
|
|
runs_per_second = 1000 / input_delay_ms # Convert ms to runs per second
|
|
max_runs = runs_per_second * experiment_duration_s
|
|
return int(max_runs)
|
|
|
|
def load_experiment_data(experiments_dir, delay_dict, perfect_time_dict, experiment_duration):
|
|
"""Load all experiment data and calculate performance metrics."""
|
|
all_data = []
|
|
|
|
# Find all subdirectories containing results.csv
|
|
experiment_dirs = [d for d in Path(experiments_dir).iterdir()
|
|
if d.is_dir() and (d / 'results.csv').exists()]
|
|
|
|
print(f"Found {len(experiment_dirs)} experiment directories")
|
|
|
|
for exp_dir in experiment_dirs:
|
|
results_path = exp_dir / 'results.csv'
|
|
|
|
try:
|
|
df = pd.read_csv(results_path)
|
|
|
|
# Extract experiment name (remove timestamp if present)
|
|
if 'experiment_name' in df.columns:
|
|
exp_name = df['experiment_name'].iloc[0]
|
|
exp_name = exp_name.split('-')[0] if '-' in exp_name else exp_name
|
|
else:
|
|
exp_name = exp_dir.name
|
|
|
|
# Group by chain and calculate metrics
|
|
for chain, chain_data in df.groupby('chain'):
|
|
if chain in delay_dict and chain in perfect_time_dict:
|
|
# Calculate theoretical maximum runs
|
|
input_delay = delay_dict[chain]
|
|
perfect_time = perfect_time_dict[chain]
|
|
theoretical_max = calculate_theoretical_max_runs(
|
|
chain, input_delay, experiment_duration
|
|
)
|
|
|
|
# Calculate actual performance metrics
|
|
actual_runs = chain_data['count'].mean()
|
|
mean_latency = chain_data['mean'].mean()
|
|
std_latency = chain_data['std'].mean()
|
|
|
|
# Normalize latency by theoretical perfect time
|
|
normalized_latency = mean_latency / perfect_time
|
|
|
|
# Calculate percentage of theoretical maximum
|
|
completion_percentage = (actual_runs / theoretical_max) * 100
|
|
|
|
if completion_percentage > 100:
|
|
print(f"Warning: Completion percentage for {chain} in {exp_name} exceeds 100%: {completion_percentage:.2f}%")
|
|
# Cap at 105% for visualization purposes
|
|
# This is to avoid visual clutter in the plot
|
|
# and to handle cases where the actual runs exceed theoretical max.
|
|
# This is a safeguard and should be adjusted based on actual data characteristics.
|
|
# In practice, this might indicate an issue with the data or the calculation.
|
|
completion_percentage = 105
|
|
|
|
all_data.append({
|
|
'experiment_type': exp_name,
|
|
'experiment_dir': exp_dir.name,
|
|
'chain': chain,
|
|
'mean_latency_ms': mean_latency,
|
|
'normalized_latency': normalized_latency,
|
|
'std_latency_ms': std_latency,
|
|
'actual_runs': actual_runs,
|
|
'theoretical_max_runs': theoretical_max,
|
|
'completion_percentage': completion_percentage,
|
|
'input_delay_ms': input_delay,
|
|
'perfect_time_ms': perfect_time
|
|
})
|
|
else:
|
|
missing_info = []
|
|
if chain not in delay_dict:
|
|
missing_info.append("input delay")
|
|
if chain not in perfect_time_dict:
|
|
missing_info.append("perfect time")
|
|
print(f"Warning: Chain '{chain}' missing {', '.join(missing_info)} in supplementary data")
|
|
|
|
except Exception as e:
|
|
print(f"Error processing {results_path}: {e}")
|
|
|
|
return pd.DataFrame(all_data)
|
|
|
|
def create_visualizations(data_df, output_prefix):
|
|
"""Create separate visualization plots for each experiment type."""
|
|
plt.style.use('seaborn-v0_8-darkgrid')
|
|
|
|
# Get unique experiment types
|
|
experiment_types = sorted(data_df['experiment_type'].unique())
|
|
|
|
print(f"Creating {len(experiment_types)} separate plots for experiment types: {experiment_types}")
|
|
|
|
created_files = []
|
|
|
|
for exp_type in experiment_types:
|
|
# Filter data for this experiment type
|
|
exp_data = data_df[data_df['experiment_type'] == exp_type]
|
|
|
|
# Get unique chains for this experiment
|
|
chains = sorted(exp_data['chain'].unique())
|
|
|
|
# Create color palette for chains
|
|
chain_colors = sns.color_palette("husl", len(chains))
|
|
chain_color_map = dict(zip(chains, chain_colors))
|
|
|
|
# Set up the figure
|
|
fig, ax = plt.subplots(figsize=(14, 10))
|
|
|
|
# Plot data points for each chain
|
|
for chain in chains:
|
|
chain_data = exp_data[exp_data['chain'] == chain]
|
|
|
|
ax.scatter(
|
|
chain_data['completion_percentage'],
|
|
chain_data['normalized_latency'],
|
|
color=chain_color_map[chain],
|
|
label=chain,
|
|
s=120,
|
|
alpha=0.8,
|
|
edgecolors='black',
|
|
linewidth=0.8
|
|
)
|
|
|
|
# Set labels and title
|
|
ax.set_xlabel('Completion Rate (% of Theoretical Maximum)', fontsize=14, fontweight='bold')
|
|
ax.set_ylabel('Normalized Latency (Actual / Theoretical Perfect)', fontsize=14, fontweight='bold')
|
|
ax.set_title(f'Performance Analysis: {exp_type}\nNormalized Latency vs Chain Completion Rate',
|
|
fontsize=16, fontweight='bold', pad=20)
|
|
|
|
# Add grid for better readability
|
|
ax.grid(True, alpha=0.3)
|
|
|
|
# Set axis limits
|
|
ax.set_xlim(0, 107)
|
|
ax.set_ylim(bottom=1)
|
|
|
|
# Create legend for chains
|
|
legend = ax.legend(title='Chain Output',
|
|
loc='best',
|
|
fontsize=10,
|
|
title_fontsize=12,
|
|
framealpha=0.9,
|
|
fancybox=True,
|
|
shadow=True,
|
|
bbox_to_anchor=(1.05, 1))
|
|
|
|
# Adjust layout to accommodate legend
|
|
plt.tight_layout()
|
|
|
|
# Save the plot
|
|
safe_exp_name = exp_type.replace('/', '_').replace(' ', '_')
|
|
output_path = f"{output_prefix}_{safe_exp_name}.png"
|
|
plt.savefig(output_path, dpi=300, bbox_inches='tight')
|
|
created_files.append(output_path)
|
|
|
|
# Show the plot
|
|
plt.show()
|
|
|
|
# Close the figure to free memory
|
|
plt.close()
|
|
|
|
return created_files
|
|
|
|
def create_combined_summary_plot(data_df, output_prefix):
|
|
"""Create a combined summary plot showing all experiment types in subplots."""
|
|
experiment_types = sorted(data_df['experiment_type'].unique())
|
|
n_experiments = len(experiment_types)
|
|
|
|
# Calculate subplot grid dimensions
|
|
n_cols = min(3, n_experiments) # Max 3 columns
|
|
n_rows = (n_experiments + n_cols - 1) // n_cols # Ceiling division
|
|
|
|
fig, axes = plt.subplots(n_rows, n_cols, figsize=(6*n_cols, 5*n_rows))
|
|
|
|
# Ensure axes is always a 2D array
|
|
if n_rows == 1 and n_cols == 1:
|
|
axes = np.array([[axes]])
|
|
elif n_rows == 1:
|
|
axes = axes.reshape(1, -1)
|
|
elif n_cols == 1:
|
|
axes = axes.reshape(-1, 1)
|
|
|
|
plt.style.use('seaborn-v0_8-darkgrid')
|
|
|
|
for i, exp_type in enumerate(experiment_types):
|
|
row = i // n_cols
|
|
col = i % n_cols
|
|
ax = axes[row, col]
|
|
|
|
# Filter data for this experiment type
|
|
exp_data = data_df[data_df['experiment_type'] == exp_type]
|
|
chains = sorted(exp_data['chain'].unique())
|
|
|
|
# Create color palette for chains
|
|
chain_colors = sns.color_palette("husl", len(chains))
|
|
chain_color_map = dict(zip(chains, chain_colors))
|
|
|
|
# Plot data points
|
|
for chain in chains:
|
|
chain_data = exp_data[exp_data['chain'] == chain]
|
|
ax.scatter(
|
|
chain_data['completion_percentage'],
|
|
chain_data['normalized_latency'],
|
|
color=chain_color_map[chain],
|
|
s=60,
|
|
alpha=0.7,
|
|
edgecolors='black',
|
|
linewidth=0.5
|
|
)
|
|
|
|
ax.set_title(exp_type, fontsize=12, fontweight='bold')
|
|
ax.set_xlabel('Completion Rate (%)', fontsize=10)
|
|
ax.set_ylabel('Normalized Latency', fontsize=10)
|
|
ax.grid(True, alpha=0.3)
|
|
|
|
# Set axis limits for consistency
|
|
ax.set_xlim(0, 107)
|
|
ax.set_ylim(bottom=1)
|
|
|
|
# Hide unused subplots
|
|
for i in range(n_experiments, n_rows * n_cols):
|
|
row = i // n_cols
|
|
col = i % n_cols
|
|
axes[row, col].set_visible(False)
|
|
|
|
plt.suptitle('Performance Analysis Summary - All Experiment Types\n(Normalized Latency vs Completion Rate)',
|
|
fontsize=16, fontweight='bold', y=0.98)
|
|
plt.tight_layout()
|
|
|
|
summary_output = f"{output_prefix}_summary.png"
|
|
plt.savefig(summary_output, dpi=300, bbox_inches='tight')
|
|
plt.show()
|
|
plt.close()
|
|
|
|
return summary_output
|
|
|
|
def print_summary_statistics(data_df):
|
|
"""Print summary statistics for the analysis."""
|
|
print("\n" + "="*80)
|
|
print("CROSS-EXPERIMENT ANALYSIS SUMMARY")
|
|
print("="*80)
|
|
|
|
print(f"\nTotal experiments analyzed: {data_df['experiment_type'].nunique()}")
|
|
print(f"Total chains analyzed: {data_df['chain'].nunique()}")
|
|
print(f"Total data points: {len(data_df)}")
|
|
|
|
print("\nPer Experiment Type Summary:")
|
|
exp_summary = data_df.groupby('experiment_type').agg({
|
|
'completion_percentage': ['mean', 'std', 'min', 'max'],
|
|
'normalized_latency': ['mean', 'std', 'min', 'max'],
|
|
'mean_latency_ms': ['mean', 'std', 'min', 'max'],
|
|
'chain': 'count'
|
|
}).round(2)
|
|
print(exp_summary)
|
|
|
|
print("\nPer Chain Summary:")
|
|
chain_summary = data_df.groupby('chain').agg({
|
|
'completion_percentage': ['mean', 'std'],
|
|
'normalized_latency': ['mean', 'std'],
|
|
'mean_latency_ms': ['mean', 'std'],
|
|
'experiment_type': 'count'
|
|
}).round(2)
|
|
print(chain_summary)
|
|
|
|
# Find best and worst performing combinations
|
|
print("\nBest Performance (highest completion rate):")
|
|
best_completion = data_df.loc[data_df['completion_percentage'].idxmax()]
|
|
print(f" {best_completion['experiment_type']} - {best_completion['chain']}")
|
|
print(f" Completion: {best_completion['completion_percentage']:.1f}%, Normalized Latency: {best_completion['normalized_latency']:.2f}x, Raw Latency: {best_completion['mean_latency_ms']:.1f}ms")
|
|
|
|
print("\nWorst Performance (lowest completion rate):")
|
|
worst_completion = data_df.loc[data_df['completion_percentage'].idxmin()]
|
|
print(f" {worst_completion['experiment_type']} - {worst_completion['chain']}")
|
|
print(f" Completion: {worst_completion['completion_percentage']:.1f}%, Normalized Latency: {worst_completion['normalized_latency']:.2f}x, Raw Latency: {worst_completion['mean_latency_ms']:.1f}ms")
|
|
|
|
print("\nBest Normalized Latency (closest to theoretical perfect):")
|
|
best_latency = data_df.loc[data_df['normalized_latency'].idxmin()]
|
|
print(f" {best_latency['experiment_type']} - {best_latency['chain']}")
|
|
print(f" Normalized Latency: {best_latency['normalized_latency']:.2f}x, Completion: {best_latency['completion_percentage']:.1f}%, Raw Latency: {best_latency['mean_latency_ms']:.1f}ms")
|
|
|
|
print("\nWorst Normalized Latency (furthest from theoretical perfect):")
|
|
worst_latency = data_df.loc[data_df['normalized_latency'].idxmax()]
|
|
print(f" {worst_latency['experiment_type']} - {worst_latency['chain']}")
|
|
print(f" Normalized Latency: {worst_latency['normalized_latency']:.2f}x, Completion: {worst_latency['completion_percentage']:.1f}%, Raw Latency: {worst_latency['mean_latency_ms']:.1f}ms")
|
|
|
|
def main():
|
|
args = parse_arguments()
|
|
|
|
print("Starting cross-experiment analysis...")
|
|
|
|
# Load supplementary data
|
|
print(f"Loading supplementary data from: {args.supplementary}")
|
|
delay_dict, perfect_time_dict = load_supplementary_data(args.supplementary)
|
|
print(f"Found delay information for {len(delay_dict)} chains")
|
|
print(f"Found perfect time information for {len(perfect_time_dict)} chains")
|
|
|
|
# Load all experiment data
|
|
print(f"Loading experiment data from: {args.experiments_dir}")
|
|
data_df = load_experiment_data(args.experiments_dir, delay_dict, perfect_time_dict, args.experiment_duration)
|
|
|
|
if data_df.empty:
|
|
print("No data found! Please check your paths and file formats.")
|
|
return
|
|
|
|
print(f"Loaded data for {len(data_df)} experiment-chain combinations")
|
|
|
|
# Create individual visualizations for each experiment type
|
|
print(f"Creating individual visualizations...")
|
|
created_files = create_visualizations(data_df, args.output)
|
|
|
|
# Create combined summary plot
|
|
print(f"Creating combined summary plot...")
|
|
summary_file = create_combined_summary_plot(data_df, args.output)
|
|
created_files.append(summary_file)
|
|
|
|
# Print summary statistics
|
|
print_summary_statistics(data_df)
|
|
|
|
# Save detailed data to CSV for further analysis
|
|
csv_output = f"{args.output}_detailed_data.csv"
|
|
data_df.to_csv(csv_output, index=False)
|
|
print(f"\nDetailed data saved to: {csv_output}")
|
|
|
|
print(f"\nCreated visualization files:")
|
|
for file in created_files:
|
|
print(f" - {file}")
|
|
|
|
if __name__ == "__main__":
|
|
main() |