visualize

visualize.py

@@ -0,0 +1,132 @@
+import json
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import seaborn as sns
+from math import pi
+
+# Load the JSON data
+with open('saved_results.json', 'r') as f:
+    data = json.load(f)
+
+# Convert JSON data into a DataFrame
+results = []
+for test_hash, test_data in data.items():
+    results.append({
+        "hash": test_hash,
+        "model": test_data['model'],
+        "seed": test_data['seed'],
+        "test_name": test_data['test_name'],
+        "validation": test_data['validation']
+    })
+
+df = pd.DataFrame(results)
+
+
+
+## 1st Chart
+# Count the number of validation results for each model
+validation_counts = df.groupby(['model', 'validation']).size().unstack().fillna(0)
+validation_counts.columns = ['Failed', 'Passed']
+
+# Plot the validation results by model
+plt.figure(figsize=(10, 6))
+validation_counts.plot(kind='bar', stacked=True, color=['red', 'green'], ax=plt.gca())
+plt.title('Validation Results by Model')
+plt.xlabel('Model')
+plt.ylabel('Number of Tests')
+plt.xticks(rotation=45, ha='right')
+plt.legend(title='Validation')
+plt.tight_layout()
+plt.savefig('validation_results_by_model.png')
+
+
+
+
+## 2nd Chart
+# Plot the validation results by test name
+test_name_counts = df.groupby(['test_name', 'validation']).size().unstack().fillna(0)
+test_name_counts.columns = ['Failed', 'Passed']
+
+plt.figure(figsize=(10, 6))
+test_name_counts.plot(kind='barh', stacked=True, color=['red', 'green'], ax=plt.gca())
+plt.title('Validation Results by Test Name')
+plt.xlabel('Number of Tests')
+plt.ylabel('Test Name')
+plt.legend(title='Validation')
+plt.tight_layout()
+plt.savefig('validation_results_by_test_name.png')
+
+
+
+
+## 3rd Chart
+# Prepare data for the spider chart
+models = df['model'].unique()
+
+# Calculate the pass rate for each model on each test
+pass_rate = pd.pivot_table(df, values='validation', index='model', columns='test_name', aggfunc="mean", fill_value=0)
+tests = df['test_name'].unique().tolist()
+
+# Initialize the spider plot
+num_vars = len(pass_rate)-1
+angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()
+angles += [ angles[0] ]
+
+fig, ax = plt.subplots(figsize=(8, 8), subplot_kw=dict(polar=True))
+
+# Plot each model's performance
+for model in models:
+    values = pass_rate.loc[model].tolist()
+    values += [ values[0] ]
+    ax.fill(angles, values, alpha=0.25)
+    ax.plot(angles, values, label=model)
+# 
+
+# Configure the spider chart
+ax.set_theta_offset(pi / 2)
+ax.set_theta_direction(-1)
+
+tests.append(tests[0])
+tests.pop(0)
+
+ax.set_xticks(angles[:-1])
+ax.set_xticklabels(tests)
+
+ax.set_yticks(np.linspace(0, 1, 5))
+ax.set_yticklabels([f'{int(i * 100)}%' for i in np.linspace(0, 1, 5)], color="grey", size=8)
+ax.set_ylim(0, 1)
+
+plt.title('Model Performance on Each Test')
+plt.legend(loc='upper right', bbox_to_anchor=(1.1, 1.1))
+plt.tight_layout()
+plt.savefig('model_performance_spider_chart.png')
+
+
+
+
+
+# 4th chart
+# Create a heatmap
+plt.figure(figsize=(8, 8))
+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})
+
+# Add percentage sign to annotations
+for text in plt.gca().texts: 
+    text.set_text(f"{text.get_text()}%")
+
+# Customize the plot with labels and a title
+plt.title('Model Performance on Each Test', fontsize=16)
+plt.xlabel('Test Name', fontsize=14)
+plt.ylabel('Model', fontsize=14)
+
+# Rotate x-axis labels by 45 degrees
+plt.xticks(rotation=45, ha='right')
+
+# Adjust layout to ensure labels don't get cut off
+plt.tight_layout()
+
+# Save the heatmap
+plt.savefig('model_performance_heatmap.png')
+
+