From 39a39ba47e1e88fb42dffffeb002f970250b8a41 Mon Sep 17 00:00:00 2001
From: Arne van Iterson <arne@arnweb.nl>
Date: Sun, 5 Nov 2023 19:41:51 +0100
Subject: [PATCH] Added weighted MCC

---
 src/suite.py | 27 +++++++++++++++++++++++----
 1 file changed, 23 insertions(+), 4 deletions(-)

diff --git a/src/suite.py b/src/suite.py
index 3725deb..d47e398 100644
--- a/src/suite.py
+++ b/src/suite.py
@@ -14,7 +14,7 @@ import json
 # OpenCV
 import numpy as np
 import cv2
-from sklearn.metrics import confusion_matrix
+from sklearn.metrics import confusion_matrix, matthews_corrcoef
 from sklearn.preprocessing import (
     MinMaxScaler,
     StandardScaler,
@@ -395,21 +395,40 @@ class CVSuite:
                 modelnr = (idx * 2) + idy
                 guesses = self.models[modelnr][2]
                 
+                # Get accuracy
+                guess_total = 0
+                guess_ok = 0
                 for guess in guesses:
-                    print(guess)
+                    guess_total += 1
+                    if guess[0] == guess[1]:
+                        guess_ok += 1
                 
                 # Convert guess array
                 tag_true = [guess[0] for guess in guesses ]
                 tag_predict = [guess[1] for guess in guesses ]
                 
+                # calculate weighted average
+                data_len_class = [tag_true.count(tag) for tag in range(0, 8)]
+                data_len = len(tag_true)
+                data_class_weight = [data_len_class[tag] / data_len for tag in range(0, 8)]
+                
+                tag_weighted = []
+                for tag in tag_true:
+                    tag_weighted.append(data_class_weight[tag])
+                
+                print(C_DBUG, f"Data length per class: {data_len_class}; Total: {data_len}")
+                
+                # Get MCC
+                mcc = matthews_corrcoef(tag_true, tag_predict, sample_weight=tag_weighted)
+                
                 labels = [Tree(tag).name for tag in range(0, 8)]
                 
                 cm = confusion_matrix(tag_true, tag_predict)
                 cmn = cmn = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
                 sns.heatmap(cmn, xticklabels=labels, yticklabels=labels, ax=graph, annot=True, cbar=False, fmt='.2f')
                 
-                graph.set_title(self.models[modelnr][0])
-                graph.set_xlabel("Predicted")
+                graph.set_title(f"{self.models[modelnr][0]}; MCC: {mcc:.2f}; Acc: {((guess_ok / guess_total) * 100):.2f}%" )
+                graph.set_xlabel("Predicted") 
                 graph.set_ylabel("Actual")
                 graph.set_xticklabels(labels, rotation=0)
                 graph.set_yticklabels(labels, rotation=0)