wip: split plots and normalize y axis as well
This commit is contained in:
parent
ea5754c336
commit
defbc4456e
1 changed files with 194 additions and 78 deletions
|
|
@ -13,18 +13,26 @@ def parse_arguments():
|
|||
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.png',
|
||||
help='Output filename for the plot')
|
||||
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 for each chain."""
|
||||
"""Load the supplementary data with input delays and theoretical perfect times for each chain."""
|
||||
supp_df = pd.read_csv(supplementary_path)
|
||||
# Create a dictionary for quick lookup
|
||||
# Create dictionaries for quick lookup
|
||||
delay_dict = dict(zip(supp_df['chain'], supp_df['input_delay']))
|
||||
return delay_dict
|
||||
|
||||
# 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."""
|
||||
|
|
@ -32,7 +40,7 @@ def calculate_theoretical_max_runs(chain, input_delay_ms, experiment_duration_s)
|
|||
max_runs = runs_per_second * experiment_duration_s
|
||||
return int(max_runs)
|
||||
|
||||
def load_experiment_data(experiments_dir, delay_dict, experiment_duration):
|
||||
def load_experiment_data(experiments_dir, delay_dict, perfect_time_dict, experiment_duration):
|
||||
"""Load all experiment data and calculate performance metrics."""
|
||||
all_data = []
|
||||
|
||||
|
|
@ -57,9 +65,10 @@ def load_experiment_data(experiments_dir, delay_dict, experiment_duration):
|
|||
|
||||
# Group by chain and calculate metrics
|
||||
for chain, chain_data in df.groupby('chain'):
|
||||
if chain in delay_dict:
|
||||
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
|
||||
)
|
||||
|
|
@ -69,6 +78,9 @@ def load_experiment_data(experiments_dir, delay_dict, experiment_duration):
|
|||
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
|
||||
|
||||
|
|
@ -86,96 +98,179 @@ def load_experiment_data(experiments_dir, delay_dict, experiment_duration):
|
|||
'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
|
||||
'input_delay_ms': input_delay,
|
||||
'perfect_time_ms': perfect_time
|
||||
})
|
||||
else:
|
||||
print(f"Warning: Chain '{chain}' not found in supplementary data")
|
||||
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_visualization(data_df, output_path):
|
||||
"""Create the main visualization plot."""
|
||||
def create_visualizations(data_df, output_prefix):
|
||||
"""Create separate visualization plots for each experiment type."""
|
||||
plt.style.use('seaborn-v0_8-darkgrid')
|
||||
|
||||
# Set up the figure
|
||||
fig, ax = plt.subplots(figsize=(20, 12))
|
||||
# Get unique experiment types
|
||||
experiment_types = sorted(data_df['experiment_type'].unique())
|
||||
|
||||
# Get unique experiment types and chains for color/marker assignment
|
||||
experiment_types = data_df['experiment_type'].unique()
|
||||
chains = data_df['chain'].unique()
|
||||
print(f"Creating {len(experiment_types)} separate plots for experiment types: {experiment_types}")
|
||||
|
||||
# Create color palette for experiment types
|
||||
exp_colors = sns.color_palette("husl", len(experiment_types))
|
||||
exp_color_map = dict(zip(experiment_types, exp_colors))
|
||||
created_files = []
|
||||
|
||||
# Create marker styles for chains (cycle through available markers)
|
||||
markers = ['o', 's', '^', 'D', 'v', '<', '>', 'p', '*', 'h', 'H', '+', 'x']
|
||||
chain_markers = dict(zip(chains, markers[:len(chains)]))
|
||||
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()
|
||||
|
||||
# Plot data points
|
||||
for _, row in data_df.iterrows():
|
||||
ax.scatter(
|
||||
row['completion_percentage'],
|
||||
row['mean_latency_ms'],
|
||||
color=exp_color_map[row['experiment_type']],
|
||||
marker=chain_markers[row['chain']],
|
||||
s=100,
|
||||
alpha=0.7,
|
||||
edgecolors='black',
|
||||
linewidth=0.5
|
||||
)
|
||||
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)
|
||||
|
||||
# Set labels and title
|
||||
ax.set_xlabel('Completion Rate (% of Theoretical Maximum)', fontsize=14, fontweight='bold')
|
||||
ax.set_ylabel('Mean End-to-End Latency (ms)', fontsize=14, fontweight='bold')
|
||||
ax.set_title('Cross-Experiment Performance Analysis\nMean Latency vs Chain Completion Rate',
|
||||
fontsize=16, fontweight='bold', pad=20)
|
||||
# Calculate subplot grid dimensions
|
||||
n_cols = min(3, n_experiments) # Max 3 columns
|
||||
n_rows = (n_experiments + n_cols - 1) // n_cols # Ceiling division
|
||||
|
||||
# Add grid for better readability
|
||||
ax.grid(True, alpha=0.3)
|
||||
fig, axes = plt.subplots(n_rows, n_cols, figsize=(6*n_cols, 5*n_rows))
|
||||
|
||||
# Create legends with better positioning
|
||||
# Legend for experiment types (colors)
|
||||
exp_legend_elements = [plt.Line2D([0], [0], marker='o', color='w',
|
||||
markerfacecolor=color, markersize=10,
|
||||
label=exp_type, markeredgecolor='black', markeredgewidth=1)
|
||||
for exp_type, color in exp_color_map.items()]
|
||||
# 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)
|
||||
|
||||
# Legend for chains (markers) - limit to avoid overcrowding
|
||||
max_chains_in_legend = min(len(chains), 11) # Show max 11 chains
|
||||
chain_legend_elements = [plt.Line2D([0], [0], marker=marker, color='w',
|
||||
markerfacecolor='gray', markersize=10,
|
||||
label=chain.split(' --> ')[-1], markeredgecolor='black', markeredgewidth=1)
|
||||
for chain, marker in list(chain_markers.items())[:max_chains_in_legend]]
|
||||
plt.style.use('seaborn-v0_8-darkgrid')
|
||||
|
||||
# Position legends inside the plot area
|
||||
legend1 = ax.legend(handles=exp_legend_elements, title='Experiment Type',
|
||||
loc='upper right', fontsize=10, title_fontsize=12,
|
||||
framealpha=0.9, fancybox=True, shadow=True)
|
||||
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)
|
||||
|
||||
# Temporarily remove first legend to add second one
|
||||
legend1.remove()
|
||||
# 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)
|
||||
|
||||
legend2 = ax.legend(handles=chain_legend_elements, title='Chain Output',
|
||||
loc='lower right', fontsize=9, title_fontsize=11,
|
||||
framealpha=0.9, fancybox=True, shadow=True)
|
||||
plt.suptitle('Performance Analysis Summary - All Experiment Types\n(Normalized Latency vs Completion Rate)',
|
||||
fontsize=16, fontweight='bold', y=0.98)
|
||||
plt.tight_layout()
|
||||
|
||||
# Add both legends back
|
||||
ax.add_artist(legend1)
|
||||
ax.add_artist(legend2)
|
||||
|
||||
# Save the plot
|
||||
plt.savefig(output_path, dpi=300, bbox_inches='tight')
|
||||
summary_output = f"{output_prefix}_summary.png"
|
||||
plt.savefig(summary_output, dpi=300, bbox_inches='tight')
|
||||
plt.show()
|
||||
plt.close()
|
||||
|
||||
return fig
|
||||
return summary_output
|
||||
|
||||
def print_summary_statistics(data_df):
|
||||
"""Print summary statistics for the analysis."""
|
||||
|
|
@ -190,6 +285,7 @@ def print_summary_statistics(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)
|
||||
|
|
@ -198,6 +294,7 @@ def print_summary_statistics(data_df):
|
|||
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)
|
||||
|
|
@ -207,12 +304,22 @@ def print_summary_statistics(data_df):
|
|||
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}%, Latency: {best_completion['mean_latency_ms']:.1f}ms")
|
||||
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}%, Latency: {worst_completion['mean_latency_ms']:.1f}ms")
|
||||
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()
|
||||
|
|
@ -221,12 +328,13 @@ def main():
|
|||
|
||||
# Load supplementary data
|
||||
print(f"Loading supplementary data from: {args.supplementary}")
|
||||
delay_dict = load_supplementary_data(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, args.experiment_duration)
|
||||
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.")
|
||||
|
|
@ -234,18 +342,26 @@ def main():
|
|||
|
||||
print(f"Loaded data for {len(data_df)} experiment-chain combinations")
|
||||
|
||||
# Create visualization
|
||||
print(f"Creating visualization...")
|
||||
create_visualization(data_df, args.output)
|
||||
# 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 = args.output.replace('.png', '_detailed_data.csv')
|
||||
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"Visualization saved to: {args.output}")
|
||||
|
||||
print(f"\nCreated visualization files:")
|
||||
for file in created_files:
|
||||
print(f" - {file}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Add table
Add a link
Reference in a new issue