eval_model.py

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