87 lines
2.4 KiB
Python
87 lines
2.4 KiB
Python
import json
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import matplotlib.pyplot as plt
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import pandas as pd
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import numpy as np
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import seaborn as sns
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# Load the JSON data
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with open('saved_results.json', 'r') as f:
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data = json.load(f)
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# Convert JSON data into a DataFrame
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results = []
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for test_hash, test_data in data.items():
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results.append({
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"hash": test_hash,
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"model": test_data['model_name'],
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"seed": test_data['seed'],
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"test_name": test_data['test_name'],
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"validation": test_data['validation']
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})
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df = pd.DataFrame(results)
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## 1st Chart
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# Count the number of validation results for each model
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validation_counts = df.groupby(['model', 'validation']).size().unstack().fillna(0)
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validation_counts.columns = ['Failed', 'Passed']
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# Plot the validation results by model
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plt.figure(figsize=(10, 6))
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validation_counts.plot(kind='bar', stacked=True, color=['red', 'green'], ax=plt.gca())
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plt.title('Validation Results by Model')
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plt.xlabel('Model')
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plt.ylabel('Number of Tests')
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plt.xticks(rotation=45, ha='right')
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plt.legend(title='Validation')
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plt.tight_layout()
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plt.savefig('validation_results_by_model.png')
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## 2nd Chart
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# Plot the validation results by test name
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test_name_counts = df.groupby(['test_name', 'validation']).size().unstack().fillna(0)
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test_name_counts.columns = ['Failed', 'Passed']
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plt.figure(figsize=(10, 6))
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test_name_counts.plot(kind='barh', stacked=True, color=['red', 'green'], ax=plt.gca())
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plt.title('Validation Results by Test Name')
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plt.xlabel('Number of Tests')
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plt.ylabel('Test Name')
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plt.legend(title='Validation')
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plt.tight_layout()
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plt.savefig('validation_results_by_test_name.png')
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## 3rd Chart
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pass_rate = pd.pivot_table(df, values='validation', index='model', columns='test_name', aggfunc="mean", fill_value=0)
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# Create a heatmap
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plt.figure(figsize=(8, 8))
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sns.heatmap(pass_rate*100, annot=True, fmt=".0f", cmap=sns.color_palette("blend:#100,#255,#4a3", as_cmap=True), cbar=True, annot_kws={"size": 10})
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# Add percentage sign to annotations
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for text in plt.gca().texts:
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text.set_text(f"{text.get_text()}%")
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# Customize the plot with labels and a title
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plt.title('Model Performance on Each Test', fontsize=16)
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plt.xlabel('Test Name', fontsize=14)
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plt.ylabel('Model', fontsize=14)
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# Rotate x-axis labels by 45 degrees
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plt.xticks(rotation=45, ha='right')
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# Adjust layout to ensure labels don't get cut off
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plt.tight_layout()
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# Save the heatmap
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plt.savefig('model_performance_heatmap.png')
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