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Added weighted MCC

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Arne van Iterson 2023-11-05 19:41:51 +01:00
parent 17af113484
commit 39a39ba47e
1 changed files with 23 additions and 4 deletions
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27
src/suite.py
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@ -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)