added count, beautified boxplot output
This commit is contained in:
parent
b5b0f2f84b
commit
9bf91d654d
3 changed files with 71 additions and 43 deletions
|
|
@ -1,16 +1,13 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
import argparse
|
||||
import seaborn as sns
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
|
||||
def parse_arguments():
|
||||
parser = argparse.ArgumentParser(description='Analyze chain data from CSV file.')
|
||||
parser.add_argument('--input', '-i', required=True, help='Path to the input CSV file')
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_arguments()
|
||||
|
||||
|
|
@ -28,49 +25,78 @@ def main():
|
|||
# Group data by chain
|
||||
chain_groups = df.groupby('chain')
|
||||
|
||||
# For each chain, create a plot with four boxplots (mean, std, min, max)
|
||||
# For each chain, create a figure with five subplots for boxplots (mean, std, min, max, count)
|
||||
for chain_name, chain_data in chain_groups:
|
||||
# Create a figure for this chain
|
||||
plt.figure(figsize=(12, 8))
|
||||
fig, axs = plt.subplots(1, 5, figsize=(18, 6), constrained_layout=True)
|
||||
|
||||
# Normalize chain name for filename
|
||||
chain_name_fs = str(chain_name).replace('--> /', '-').replace('/', '_').replace(' ', '')
|
||||
|
||||
# Create a DataFrame with the columns we want to plot
|
||||
plot_data = pd.DataFrame({
|
||||
'Mean': chain_data['mean'],
|
||||
'Std': chain_data['std'],
|
||||
'Min': chain_data['min'],
|
||||
'Max': chain_data['max']
|
||||
})
|
||||
plot_data = chain_data[['mean', 'std', 'min', 'max', 'count']].copy()
|
||||
plot_data.columns = ['Mean', 'Std', 'Min', 'Max', 'Count']
|
||||
|
||||
# Make all plots have the same color palette
|
||||
palette = sns.color_palette("husl", 4)
|
||||
# Add a distinct color for the 'Count' plot, as it is a different metric
|
||||
colors = palette + ['lightcoral']
|
||||
|
||||
for idx, (col, color) in enumerate(zip(['Mean', 'Std', 'Min', 'Max', 'Count'], colors)):
|
||||
ax = axs[idx]
|
||||
|
||||
# Create boxplots
|
||||
ax = sns.boxplot(data=plot_data, palette='Set3')
|
||||
sns.boxplot(data=plot_data[col], ax=ax, color=color, showfliers=True, width=0.4)
|
||||
|
||||
# Add individual data points
|
||||
sns.stripplot(data=plot_data, color='black', alpha=0.5, size=4, jitter=True)
|
||||
sns.swarmplot(data=plot_data[col], ax=ax, color='black', size=3, alpha=0.6)
|
||||
|
||||
# Set labels and title
|
||||
plt.title(f'Statistics for Chain: {chain_name}\nAcross {len(chain_data)} Experiment Runs\n{experiment_name}', fontsize=14)
|
||||
plt.ylabel('Latency (ms)', fontsize=12)
|
||||
plt.xlabel('Statistic Type', fontsize=12)
|
||||
ax.set_title(f'{col} Distribution', fontsize=14, fontweight='bold')
|
||||
ax.set_xticks([]) # Remove x-ticks for clarity
|
||||
ax.set_xlabel('') # No x-label needed
|
||||
ax.set_ylabel('Latency (ms)' if col != 'Count' else 'Count', fontsize=12)
|
||||
|
||||
# Calculate statistics of the statistics
|
||||
data_values = plot_data[col]
|
||||
stats_text = (
|
||||
f"Mean: {data_values.mean():.2f}\n"
|
||||
f"Std: {data_values.std():.2f}\n"
|
||||
f"Min: {data_values.min():.2f}\n"
|
||||
f"Max: {data_values.max():.2f}"
|
||||
)
|
||||
|
||||
# --- Place legend in the top right using axes fraction coordinates ---
|
||||
ax.text(
|
||||
0.95, 0.98, # axes fraction: 95% right, 98% up
|
||||
stats_text,
|
||||
transform=ax.transAxes,
|
||||
verticalalignment='top',
|
||||
horizontalalignment='right',
|
||||
fontsize=10,
|
||||
bbox=dict(facecolor='white', alpha=0.9, boxstyle='round,pad=0.3', edgecolor='gray')
|
||||
)
|
||||
|
||||
# Add grid for better readability
|
||||
plt.grid(axis='y', linestyle='--', alpha=0.7)
|
||||
ax.grid(axis='y', linestyle='--', alpha=0.4)
|
||||
|
||||
# Tighten layout and save the figure
|
||||
# Set the overall title for the figure
|
||||
plt.suptitle(
|
||||
f'Statistics for Chain: {chain_name}\nAcross {len(chain_data)} Experiment Runs - {experiment_name}',
|
||||
fontsize=18, fontweight='bold'
|
||||
)
|
||||
|
||||
# Save the figure with a filename that includes the chain name
|
||||
plt.tight_layout()
|
||||
output_file = args.input.replace('.csv', f'_chain_{chain_name_fs}_analysis.png')
|
||||
plt.savefig(output_file, dpi=300)
|
||||
plt.close()
|
||||
|
||||
# Also calculate and print summary statistics for this chain
|
||||
# Print summary statistics for the chain
|
||||
summary = chain_data.describe()
|
||||
print(f"\nSummary for chain: {chain_name}")
|
||||
print(summary[['mean', 'std', 'min', 'max']])
|
||||
print(summary[['mean', 'std', 'min', 'max', 'count']])
|
||||
|
||||
print(f"\nAnalysis complete. Plots saved with base name: {args.input.replace('.csv', '_chain_*_analysis.png')}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -51,7 +51,7 @@ def main(base_dir, name_filter):
|
|||
pm.execute_notebook(
|
||||
"./trace-analysis.ipynb",
|
||||
os.path.join(current_artifact, "output", "trace-analysis.ipynb"),
|
||||
log_output=True
|
||||
log_output=False
|
||||
)
|
||||
except Exception as e:
|
||||
LOGGER.exception(e)
|
||||
|
|
|
|||
|
|
@ -683,6 +683,7 @@
|
|||
" std_latency = np.std(e2e_latencies)\n",
|
||||
" min_latency = np.min(e2e_latencies)\n",
|
||||
" max_latency = np.max(e2e_latencies)\n",
|
||||
" count_latencies = len(e2e_latencies)\n",
|
||||
" ax.axvline(mean_latency, c=\"red\", linewidth=2)\n",
|
||||
" _, max_ylim = ax.get_ylim()\n",
|
||||
" # Create a multi-line string with all stats\n",
|
||||
|
|
@ -690,7 +691,8 @@
|
|||
" f\"Mean: {mean_latency:.2f} ms\\n\"\n",
|
||||
" f\"Std: {std_latency:.2f} ms\\n\"\n",
|
||||
" f\"Min: {min_latency:.2f} ms\\n\"\n",
|
||||
" f\"Max: {max_latency:.2f} ms\"\n",
|
||||
" f\"Max: {max_latency:.2f} ms\\n\"\n",
|
||||
" f\"Count: {count_latencies}\"\n",
|
||||
" )\n",
|
||||
" # Place text near top right of plot\n",
|
||||
" ax.text(\n",
|
||||
|
|
@ -703,10 +705,10 @@
|
|||
" bbox=dict(facecolor='white', alpha=0.7, boxstyle='round,pad=0.3')\n",
|
||||
" )\n",
|
||||
" plt.savefig(os.path.join(OUT_PATH, f\"plot_e2es_{name}.png\"))\n",
|
||||
" result_strings.append(f\"Chain {topics[0]} --> {topics[-1]} E2E stats: Mean: {mean_latency:.2f} ms, Std: {std_latency:.2f} ms, Min: {min_latency:.2f} ms, Max: {max_latency:.2f} ms\")\n",
|
||||
" result_strings.append(f\"Chain {topics[0]} --> {topics[-1]} E2E stats: Mean: {mean_latency:.2f} ms, Std: {std_latency:.2f} ms, Min: {min_latency:.2f} ms, Max: {max_latency:.2f} ms, Count: {count_latencies}\")\n",
|
||||
" # also do it as csv of order: exepriment_name, chain, mean, std, min, max\n",
|
||||
" result_strings_csv.append(\n",
|
||||
" f\"{EXPERIMENT_NAME},{topics[0]} --> {topics[-1]},{mean_latency:.2f},{std_latency:.2f},{min_latency:.2f},{max_latency:.2f}\"\n",
|
||||
" f\"{EXPERIMENT_NAME},{topics[0]} --> {topics[-1]},{mean_latency:.2f},{std_latency:.2f},{min_latency:.2f},{max_latency:.2f},{count_latencies}\"\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" ##################################################\n",
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue