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