vis

visualize.py

@@ -1,9 +1,12 @@
 import json
 import matplotlib.pyplot as plt
 import pandas as pd
-import numpy as np
 import seaborn as sns
 
+from suite_settings.models import models
+from suite_settings.techniques import techniques
+from suite_settings.tests import tests 
+
 # Load the JSON data
 with open('saved_results.json', 'r') as f:
     data = json.load(f)
@@ -13,9 +16,12 @@ results = []
 for test_hash, test_data in data.items():
     results.append({
         "hash": test_hash,
-        "model": test_data['model_name'],
+        "model_name": models[test_data['model_id']].display_name,
+        "model_size": models[test_data['model_id']].parameter_count_in_b,
+        "technique_name": techniques[test_data['technique_id']].name,
+        "model_technique": f"{models[test_data['model_id']].display_name}:{ techniques[test_data['technique_id']].name}",
         "seed": test_data['seed'],
-        "test_name": test_data['test_name'],
+        "test_name":  tests[test_data['test_id']].name,
         "validation": test_data['validation']
     })
 
@@ -23,21 +29,34 @@ df = pd.DataFrame(results)
 
 
 
+df['technique_name'] = pd.Categorical(df['technique_name'], categories=[techniques[1].name, techniques[572].name, techniques[903].name],ordered=True)
+df['test_name'] = pd.Categorical(df['test_name'], categories=[tests[607].name, tests[693].name, tests[120].name, tests[283].name, tests[260].name, tests[856].name],ordered=True)
+sorted_df = df.sort_values('model_size')
+
+# Perform the groupby and unstack operation
+result_df = (
+    sorted_df.groupby(['model_name', 'validation']).size()
+    .unstack(fill_value=0)  # Unstack and fill NaN with 0
+)
+
 ## 1st Chart
-# Count the number of validation results for each model
+# Count the number of validation results for each technique_name
-validation_counts = df.groupby(['model', 'validation']).size().unstack().fillna(0)
+validation_counts = result_df.loc[sorted_df['model_name'].drop_duplicates()]
 validation_counts.columns = ['Failed', 'Passed']
 
-# Plot the validation results by model
+# Plot the validation results by technique_name
 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.title('Validation Results by Model and Technique')
-plt.xlabel('Model')
+plt.xlabel('Model and Technique')
 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')
+plt.savefig('model-bar-chart.png')
+
+
+
 
 
 
@@ -54,25 +73,46 @@ plt.xlabel('Number of Tests')
 plt.ylabel('Test Name')
 plt.legend(title='Validation')
 plt.tight_layout()
-plt.savefig('validation_results_by_test_name.png')
+plt.savefig('test-bar-chart.png')
 
 
 
+sorted_df = df.sort_values('model_size' )
 
-## 3rd Chart
+# Get the unique order of 'model_technique' based on sorted_df
-pass_rate = pd.pivot_table(df, values='validation', index='model', columns='test_name', aggfunc="mean", fill_value=0)
+ordered_techniques = sorted_df['model_technique'].unique()
-# 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})
 
+# Create the pivot table with the correct order of model_technique
+pass_rate = pd.pivot_table(
+    sorted_df,
+    values='validation',
+    index='model_technique',
+    columns='test_name',
+    aggfunc="mean",
+    fill_value=0
+)
+
+# Reorder the rows in the pivot table based on the ordered techniques
+pass_rate = pass_rate.loc[ordered_techniques]
+
+# Plot the heatmap
+plt.figure(figsize=(8, 10))
+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.title('Model Technique Performance on Each Test', fontsize=16)
 plt.xlabel('Test Name', fontsize=14)
-plt.ylabel('Model', fontsize=14)
+plt.ylabel('Model and Technique', fontsize=14)
 
 # Rotate x-axis labels by 45 degrees
 plt.xticks(rotation=45, ha='right')
@@ -81,6 +121,22 @@ plt.xticks(rotation=45, ha='right')
 plt.tight_layout()
 
 # Save the heatmap
-plt.savefig('model_performance_heatmap.png')
+plt.savefig('modelTechnique_heatmap.png')
 
 
+## 4th Chart: Technique Performance on Each Test (Aggregated Heatmap)
+technique_pass_rate = pd.pivot_table(sorted_df, values='validation', index='test_name', columns='technique_name', aggfunc="mean", fill_value=0)
+plt.figure(figsize=(8, 4))
+sns.heatmap(technique_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('Technique Performance on Each Test', fontsize=16)
+plt.ylabel('Test Name', fontsize=14)
+plt.xlabel('Technique', fontsize=14)
+plt.xticks(rotation=0)
+plt.tight_layout()
+plt.savefig('technique_heatmap.png')