added new confusion matrix

Data/show_plots.py

@@ -151,7 +151,7 @@ plt.figure(figsize=(10, 8))
 sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
             xticklabels=['0-1', '1-2', '2-3', '3-4', '4-5', '5-6', '6-7', '7-8', '8-9', '9-10'],
             yticklabels=['0-1', '1-2', '2-3', '3-4', '4-5', '5-6', '6-7', '7-8', '8-9', '9-10'])
-plt.title('Confusion Matrix: Ground truth EDSS vs interferred EDSS (Categorized 0-10)')
+#plt.title('Confusion Matrix: Ground truth EDSS vs interferred EDSS (Categorized 0-10)')
 plt.xlabel('LLM Generated EDSS')
 plt.ylabel('Ground Truth EDSS')
 plt.tight_layout()
@@ -168,6 +168,98 @@ print(cm)
 ##
 
 
+# %%  Confusion matrix
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.metrics import confusion_matrix, classification_report
+import seaborn as sns
+
+# Load your data from TSV file
+file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv'
+df = pd.read_csv(file_path, sep='\t')
+
+# Replace comma with dot for numeric conversion in GT.EDSS and result.EDSS
+df['GT.EDSS'] = df['GT.EDSS'].astype(str).str.replace(',', '.')
+df['result.EDSS'] = df['result.EDSS'].astype(str).str.replace(',', '.')
+
+# Convert to float (handle invalid entries gracefully)
+df['GT.EDSS'] = pd.to_numeric(df['GT.EDSS'], errors='coerce')
+df['result.EDSS'] = pd.to_numeric(df['result.EDSS'], errors='coerce')
+
+# Drop rows where either column is NaN
+df_clean = df.dropna(subset=['GT.EDSS', 'result.EDSS'])
+
+# For confusion matrix, we need to categorize the values
+# Let's create categories up to 10 (0-1, 1-2, 2-3, ..., 9-10)
+def categorize_edss(value):
+    if pd.isna(value):
+        return np.nan
+    elif value <= 1.0:
+        return '0-1'
+    elif value <= 2.0:
+        return '1-2'
+    elif value <= 3.0:
+        return '2-3'
+    elif value <= 4.0:
+        return '3-4'
+    elif value <= 5.0:
+        return '4-5'
+    elif value <= 6.0:
+        return '5-6'
+    elif value <= 7.0:
+        return '6-7'
+    elif value <= 8.0:
+        return '7-8'
+    elif value <= 9.0:
+        return '8-9'
+    elif value <= 10.0:
+        return '9-10'
+    else:
+        return '10+'
+
+# Create categorical versions
+df_clean['GT.EDSS_cat'] = df_clean['GT.EDSS'].apply(categorize_edss)
+df_clean['result.EDSS_cat'] = df_clean['result.EDSS'].apply(categorize_edss)
+
+# Remove any NaN categories
+df_clean = df_clean.dropna(subset=['GT.EDSS_cat', 'result.EDSS_cat'])
+
+# Create confusion matrix
+cm = confusion_matrix(df_clean['GT.EDSS_cat'], df_clean['result.EDSS_cat'],
+                     labels=['0-1', '1-2', '2-3', '3-4', '4-5', '5-6', '6-7', '7-8', '8-9', '9-10'])
+
+# Plot confusion matrix
+plt.figure(figsize=(10, 8))
+ax = sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
+            xticklabels=['0-1', '1-2', '2-3', '3-4', '4-5', '5-6', '6-7', '7-8', '8-9', '9-10'],
+            yticklabels=['0-1', '1-2', '2-3', '3-4', '4-5', '5-6', '6-7', '7-8', '8-9', '9-10'])
+
+# Add legend text above the color bar
+# Get the colorbar object
+cbar = ax.collections[0].colorbar
+# Add text above the colorbar
+cbar.set_label('Number of Cases', rotation=270, labelpad=20)
+
+plt.xlabel('LLM Generated EDSS')
+plt.ylabel('Ground Truth EDSS')
+#plt.title('Confusion Matrix: Ground truth EDSS vs inferred EDSS (Categorized 0-10)')
+plt.tight_layout()
+plt.show()
+
+# Print classification report
+print("Classification Report:")
+print(classification_report(df_clean['GT.EDSS_cat'], df_clean['result.EDSS_cat']))
+
+# Print raw counts
+print("\nConfusion Matrix (Raw Counts):")
+print(cm)
+
+
+##
+
+
+
 
 # %% Classification 
 import pandas as pd