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import numpy as np
from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, confusion_matrix
from sklearn.metrics import average_precision_score
# Assuming y_true and y_pred are your data
y_true = [0, 1, 1, 0, 1, 1]
y_pred = [0, 0, 1, 0, 0, 1]
# Assuming y_true and y_pred are your data
y_true = [[0, 1, 1], [0, 1, 1], [1, 0, 1]]
y_pred = [[0, 0, 1], [0, 0, 1], [1, 0, 0]]
class model_metrics:
def __init__(self):
self.clear()
def clear(self):
self.accuracy = 0.0
self.recall = 0.0
self.precision = 0.0
self.f1 = 0.0
self.mAP = 0.0
self.cm = np.asarray([])
self.count = 0
self.total_accuracy = 0.0
self.total_recall = 0.0
self.total_precision = 0.0
self.total_f1 = 0.0
self.total_mAP = 0.0
self.total_cm = np.asarray([])
def get_indicators(self):
return self.total_accuracy / self.count, self.total_recall / self.count, self.total_precision / self.count, self.total_f1 / self.count, self.total_mAP / self.count, self.total_cm / self.count
def dump(self):
print(f"Accuracy: {self.accuracy}")
print(f"Recall: {self.recall}")
print(f"Precision: {self.precision}")
print(f"F1 Score: {self.f1}")
print(f"mAP: {self.mAP}")
print(f"Confusion Matrix: \n{self.cm}")
print(f'average accuracy: {self.total_accuracy / self.count}')
print(f'average recall: {self.total_recall / self.count}')
print(f'average precision: {self.total_precision / self.count}')
print(f'average f1: {self.total_f1 / self.count}')
print(f'average mAP: {self.total_mAP / self.count}')
print(f'average confusion matrix: \n{self.total_cm / self.count}')
def calc_metrics(self, y_true, y_pred, y_score):
self.accuracy = accuracy_score(y_true, y_pred)
self.recall = recall_score(y_true, y_pred, average='weighted')
self.precision = precision_score(y_true, y_pred, average='micro')
self.cm = confusion_matrix(y_true, y_pred)
self.count += 1
self.total_accuracy += self.accuracy
self.total_recall += self.recall
self.total_precision += self.precision
self.total_f1 += self.f1
self.total_mAP += self.mAP
self.total_cm = self.cm # TBD
return self.accuracy, self.recall, self.precision, self.f1, self.mAP, self.cm
def calc_metrics_multi(self, y_true, y_pred):
self.accuracy = accuracy_score(y_true, y_pred)
self.recall = recall_score(y_true, y_pred, average='micro')
self.precision = precision_score(y_true, y_pred, average='micro')
self.f1 = f1_score(y_true, y_pred, average='micro')
self.mAP = average_precision_score(y_true, y_pred, average='micro')
self.count += 1
return self.accuracy, self.recall, self.precision, self.f1, self.mAP, self.cm
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