Add confusion matrix for all model predictions

src/suite.py

@@ -14,6 +14,7 @@ import json
 # OpenCV
 import numpy as np
 import cv2
+from sklearn.metrics import confusion_matrix
 from sklearn.preprocessing import (
     MinMaxScaler,
     StandardScaler,
@@ -25,6 +26,7 @@ import joblib
 # GUI
 import pygubu
 import matplotlib.pyplot as plt
+import seaborn as sns
 
 # Helpers
 from helpers.statistics import imgStats
@@ -85,6 +87,7 @@ class CVSuite:
 
         # Plots
         self.axs = self.createPlot(2, 2)
+        self.axs_cm = None
 
         # UI Variables
         self.canny_thr1 = None
@@ -133,17 +136,18 @@ class CVSuite:
                     print(C_INFO, f"Loading model {model}")
                     mpath = config_json["models"][model]
                     if model == "knn":
-                        self.models.append(("KNN", CVSuiteTestKNN(mpath)))
+                        # Tuple with name, class instance and array of guesses for confusion matrix
+                        self.models.append(("KNN", CVSuiteTestKNN(mpath), []))
                     elif model == "dectree":
                         self.models.append(
-                            ("Decision Tree", CVSuiteTestDecisionTree(mpath))
+                            ("Decision Tree", CVSuiteTestDecisionTree(mpath), [])
                         )
                     elif model == "randforest":
                         self.models.append(
-                            ("Random Forest", CVSuiteTestRandomForest(mpath))
+                            ("Random Forest", CVSuiteTestRandomForest(mpath), [])
                         )
                     elif model == "extratree":
-                        self.models.append(("Extra tree", CVSuiteTestExtraTrees(mpath)))
+                        self.models.append(("Extra tree", CVSuiteTestExtraTrees(mpath), []))
                     else:
                         print(
                             C_WARN, f"Model {model} does not exist or is not supported!"
@@ -246,7 +250,7 @@ class CVSuite:
         self.update()
 
     def createPlot(self, columns, rows):
-        fig, axs = plt.subplots(columns, rows)
+        fig, axs = plt.subplots(columns, rows, num=100)
         return axs
 
     def drawHist(self, image, labels, column, row):
@@ -362,7 +366,7 @@ class CVSuite:
 
         data = np.array([data], dtype=np.float32)
 
-        for name, ins in self.models:
+        for name, ins, guesses in self.models:
             output.insert("end", f"{name} Result:\n")
 
             # Predict result using model instance
@@ -370,9 +374,42 @@ class CVSuite:
 
             # Prediciton result should be an array
             for idx, value in enumerate(result):
+                if idx == 0:
+                    guesses.append([Tree[tag.upper()].value, value])
                 output.insert("end", f"  [{idx + 1}]\t{Tree(value).name}\n")
+                
+            print(C_DBUG, f"Guesses for {name}:", guesses)
 
         output.configure(state="disabled")
+        
+    def drawConfusionMatrix(self, event=None):
+        if self.axs_cm is not None:
+            for ays in self.axs_cm:
+                for graph in ays:
+                    graph.remove()
+        
+        fig, axs = plt.subplots(2, 2, num=101)
+        self.axs_cm = axs
+        
+        for idx, ays in enumerate(axs):
+            for idy, graph in enumerate(ays):
+                # Get guesses for current model
+                modelnr = (idx * 2) + idy
+                guesses = self.models[modelnr][2]
+                
+                # Convert guess array
+                tag_true = [guess[0] for guess in guesses ]
+                tag_predict = [guess[1] for guess in guesses ]
+                
+                labels = [Tree(tag).name for tag in range(0, 7)]
+                
+                sns.heatmap(confusion_matrix(tag_true, tag_predict), xticklabels=labels, yticklabels=labels, ax=graph, annot=True, cbar=False, fmt='g')
+                graph.set_title(self.models[modelnr][0])
+                graph.set_xlabel("Predicted")
+                graph.set_ylabel("Actual")
+                graph.set_xticklabels(labels, rotation=0)
+                graph.set_yticklabels(labels, rotation=0)
+        # exit()
 
     def updatePath(self):
         """
@@ -515,6 +552,8 @@ class CVSuite:
             # Write results to CSV file
             if not part_update:
                 self.runTest(self.log.data)
+                self.drawConfusionMatrix()
+                
                 self.log.update()
             else:
                 self.log.clear()  # Prevent partial updates from breaking log