| import matplotlib.pyplot as plt | |
| import pandas as pd | |
| import numpy as np | |
| import seaborn as sns | |
| import os # Import os for path joining | |
| # Provided data (shortened for brevity, full data used in execution) | |
| # data = { | |
| # "SYNTHETIC": { | |
| # "status": "Success", | |
| # "category_counts": { | |
| # "point_prediction_prism": 32.85, "simple_direction_sat": 11.1, "yes_no": 8.51, "short_answer_other": 8.31, | |
| # "number_only": 5.37, "json_answer:small": 3.15, "json_answer:large": 3.14, "json_answer:no": 2.68, | |
| # "json_answer:yes": 2.6, "multiple_choice_letter_spar7m": 1.85, "object_movement_sat": 1.51, | |
| # "json_answer:1": 1.49, "json_answer:0": 1.2, "json_answer:2": 0.95, # ... other json_answers omitted | |
| # "long_descriptive_other": 0.82 | |
| # # ... other categories omitted for brevity in this display | |
| # } | |
| # }, | |
| # "REAL": { | |
| # "status": "Success", | |
| # "category_counts": { | |
| # "yes_no": 33.33, "multiple_choice_letter_spar7m": 22.7, "point_list_robospatial": 16.67, | |
| # "number_only": 12.29, "long_descriptive_other": 10.35, "descriptive_sentence_spar7m": 4.66 | |
| # } | |
| # }, | |
| # "STATIC": { | |
| # "status": "Success", | |
| # "category_counts": { | |
| # "point_prediction_prism": 20.0, "yes_no": 17.12, "multiple_choice_letter_spar7m": 13.26, | |
| # "simple_direction_sat": 7.77, "point_list_robospatial": 6.67, "long_descriptive_other": 6.14, | |
| # "number_only": 3.87, "short_answer_other": 2.68, "descriptive_sentence_spar7m": 2.51, | |
| # "json_answer:large": 2.15, "json_answer:small": 2.15, "json_answer:no": 1.81, "json_answer:yes": 1.78, | |
| # "json_answer:1": 1.01 # ... other categories omitted | |
| # } | |
| # }, | |
| # "DYNAMIC": { | |
| # "status": "Success", | |
| # "category_counts": { | |
| # "number_only": 48.02, "short_answer_other": 23.16, "yes_no": 16.92, "object_movement_sat": 7.87, | |
| # "json_answer:large": 0.62, "json_answer:small": 0.57 # ... other categories omitted | |
| # } | |
| # }, | |
| # "PERCEPTION": { | |
| # "status": "Success", | |
| # "category_counts": { | |
| # "number_only": 98.89, "short_answer_other": 0.27, "json_answer:no": 0.11, "json_answer:small": 0.1, | |
| # "json_answer:large": 0.1, "json_answer:yes": 0.1 # ... other categories omitted | |
| # } | |
| # }, | |
| # "REASONING": { | |
| # "status": "Success", | |
| # "category_counts": { | |
| # "point_prediction_prism": 20.0, "yes_no": 18.64, "multiple_choice_letter_spar7m": 13.11, | |
| # "simple_direction_sat": 6.93, "point_list_robospatial": 6.67, "long_descriptive_other": 6.07, | |
| # "short_answer_other": 5.15, "descriptive_sentence_spar7m": 2.51, "json_answer:small": 2.17, | |
| # "json_answer:large": 2.13, "json_answer:no": 1.78, "json_answer:yes": 1.71, "json_answer:1": 1.03, | |
| # "object_movement_sat": 0.94 # ... other categories omitted | |
| # } | |
| # }, | |
| # "_2D": { | |
| # "status": "Success", | |
| # "category_counts": { | |
| # "number_only": 56.27, "simple_direction_sat": 25.85, "yes_no": 4.43, "json_answer:no": 2.68, | |
| # "json_answer:yes": 2.6, "json_answer:1": 1.49, "json_answer:0": 1.2, "json_answer:small": 0.72, | |
| # "json_answer:large": 0.7, "json_answer:2": 0.58 # ... other categories omitted | |
| # } | |
| # }, | |
| # "_3D": { | |
| # "status": "Success", | |
| # "category_counts": { | |
| # "yes_no": 21.13, "point_prediction_prism": 20.91, "multiple_choice_letter_spar7m": 14.48, | |
| # "short_answer_other": 8.87, "point_list_robospatial": 6.97, "long_descriptive_other": 6.68, | |
| # "descriptive_sentence_spar7m": 2.62, "json_answer:large": 2.44, "json_answer:small": 2.44, | |
| # "object_movement_sat": 1.62 # ... other categories omitted | |
| # } | |
| # } | |
| # } | |
| # --- Data Preparation --- | |
| import json | |
| with open('__finetuning_data_analysis.json', 'r') as f: | |
| data = json.load(f) | |
| # Simplify 'json_answer:...' categories into one 'json_answer' | |
| simplified_data = {} | |
| all_categories = set() | |
| for dataset, info in data.items(): | |
| if info['status'] == 'Success': | |
| counts = info['category_counts'] | |
| new_counts = {} | |
| json_sum = 0 | |
| for cat, perc in counts.items(): | |
| if cat.startswith("json_answer:"): | |
| json_sum += perc | |
| else: | |
| new_counts[cat] = perc | |
| all_categories.add(cat) | |
| if json_sum > 0: | |
| new_counts['json_answer'] = round(json_sum, 2) | |
| all_categories.add('json_answer') | |
| simplified_data[dataset] = new_counts | |
| # Convert to DataFrame | |
| df = pd.DataFrame(simplified_data).fillna(0).T # Transpose for plotting | |
| # --- Select Top Categories and Group Others --- | |
| # Calculate total percentage for each category across all datasets | |
| category_totals = df.sum().sort_values(ascending=False) | |
| # Keep top N categories (adjust N as needed) | |
| top_n = 10 | |
| top_categories = category_totals.head(top_n).index.tolist() | |
| other_categories = category_totals.iloc[top_n:].index.tolist() | |
| # Group remaining categories into 'Other' | |
| if other_categories: | |
| df['Other'] = df[other_categories].sum(axis=1) | |
| df = df[top_categories + ['Other']] # Keep only top N + Other | |
| else: | |
| df = df[top_categories] # Keep only top N if no 'Other' needed | |
| # Ensure DataFrame columns are sorted for consistent legend order | |
| df = df[sorted(df.columns)] | |
| # --- Plotting --- | |
| plt.style.use('seaborn-v0_8-whitegrid') # Use a clean style | |
| fig, ax = plt.subplots(figsize=(14, 8)) | |
| # Define a color palette | |
| colors = sns.color_palette("tab20", n_colors=len(df.columns)) | |
| # Create the stacked bar chart | |
| df.plot(kind='bar', stacked=True, ax=ax, color=colors, width=0.8) | |
| # --- Formatting --- | |
| ax.set_xlabel("Dataset Category", fontsize=12, fontweight='bold') | |
| ax.set_ylabel("Percentage of Answer Formats (%)", fontsize=12, fontweight='bold') | |
| ax.set_title("Distribution of Answer Formats Across Fine-tuning Datasets", fontsize=16, fontweight='bold', pad=20) | |
| ax.tick_params(axis='x', rotation=45, labelsize=11) | |
| ax.tick_params(axis='y', labelsize=11) | |
| ax.set_ylim(0, 100) | |
| ax.yaxis.grid(True, linestyle='--', alpha=0.7) | |
| ax.xaxis.grid(False) # Remove vertical grid lines | |
| # Add percentage labels inside the bars (only for segments > 5%) | |
| # Labels removed for clarity in this version | |
| # --- Legend --- | |
| # Place legend outside the plot area | |
| ax.legend(title="Answer Format Categories", bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0., fontsize=10, title_fontsize=11) | |
| plt.tight_layout(rect=[0, 0, 0.85, 1]) # Adjust layout to make space for legend | |
| # --- Save the plot instead of showing it --- | |
| output_filename = "answer_format_distribution.png" | |
| plt.savefig(output_filename, dpi=300, bbox_inches='tight') # Use bbox_inches='tight' to include legend | |
| print(f"Plot saved as {output_filename}") | |
| # plt.show() # Replaced with savefig |