357 lines
12 KiB
Python
357 lines
12 KiB
Python
"""
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评估指标模块：实现各种评估指标
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"""
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import numpy as np
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import tensorflow as tf
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from sklearn.metrics import (
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accuracy_score, precision_score, recall_score, f1_score,
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confusion_matrix, classification_report, roc_auc_score,
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precision_recall_curve, average_precision_score
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)
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import matplotlib.pyplot as plt
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from typing import List, Dict, Tuple, Optional, Any, Union, Callable
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import pandas as pd
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from utils.logger import get_logger
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logger = get_logger("Metrics")
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class ClassificationMetrics:
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"""分类评估指标类，计算各种分类评估指标"""
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def __init__(self, class_names: Optional[List[str]] = None):
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"""
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初始化分类评估指标类
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Args:
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class_names: 类别名称列表
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"""
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self.class_names = class_names
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def accuracy(self, y_true: np.ndarray, y_pred: np.ndarray) -> float:
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"""
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计算准确率
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Args:
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y_true: 真实标签
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y_pred: 预测标签
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Returns:
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准确率
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"""
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return accuracy_score(y_true, y_pred)
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def precision(self, y_true: np.ndarray, y_pred: np.ndarray,
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average: str = 'macro') -> Union[float, np.ndarray]:
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"""
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计算精确率
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Args:
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y_true: 真实标签
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y_pred: 预测标签
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average: 平均方式，可选值: 'micro', 'macro', 'weighted', 'samples', None
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Returns:
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精确率
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"""
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return precision_score(y_true, y_pred, average=average, zero_division=0)
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def recall(self, y_true: np.ndarray, y_pred: np.ndarray,
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average: str = 'macro') -> Union[float, np.ndarray]:
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"""
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计算召回率
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Args:
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y_true: 真实标签
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y_pred: 预测标签
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average: 平均方式，可选值: 'micro', 'macro', 'weighted', 'samples', None
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Returns:
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召回率
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"""
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return recall_score(y_true, y_pred, average=average, zero_division=0)
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def f1(self, y_true: np.ndarray, y_pred: np.ndarray,
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average: str = 'macro') -> Union[float, np.ndarray]:
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"""
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计算F1分数
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Args:
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y_true: 真实标签
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y_pred: 预测标签
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average: 平均方式，可选值: 'micro', 'macro', 'weighted', 'samples', None
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Returns:
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F1分数
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"""
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return f1_score(y_true, y_pred, average=average, zero_division=0)
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def confusion_matrix(self, y_true: np.ndarray, y_pred: np.ndarray,
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normalize: Optional[str] = None) -> np.ndarray:
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"""
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计算混淆矩阵
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Args:
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y_true: 真实标签
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y_pred: 预测标签
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normalize: 归一化方式，可选值: 'true', 'pred', 'all', None
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Returns:
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混淆矩阵
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"""
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cm = confusion_matrix(y_true, y_pred)
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if normalize is not None:
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if normalize == 'true':
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cm = cm.astype('float') / cm.sum(axis=1, keepdims=True)
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elif normalize == 'pred':
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cm = cm.astype('float') / cm.sum(axis=0, keepdims=True)
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elif normalize == 'all':
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cm = cm.astype('float') / cm.sum()
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return cm
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def plot_confusion_matrix(self, y_true: np.ndarray, y_pred: np.ndarray,
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normalize: Optional[str] = None,
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figsize: Tuple[int, int] = (10, 8),
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save_path: Optional[str] = None) -> None:
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"""
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绘制混淆矩阵
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Args:
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y_true: 真实标签
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y_pred: 预测标签
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normalize: 归一化方式，可选值: 'true', 'pred', 'all', None
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figsize: 图像大小
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save_path: 保存路径，如果为None则显示图像
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"""
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# 计算混淆矩阵
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cm = self.confusion_matrix(y_true, y_pred, normalize)
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# 确定类别名称
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if self.class_names is None:
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class_names = [str(i) for i in range(cm.shape[0])]
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else:
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class_names = self.class_names
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# 创建图像
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plt.figure(figsize=figsize)
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# 使用热图显示混淆矩阵
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im = plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
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plt.colorbar(im)
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# 设置坐标轴标签
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plt.xticks(np.arange(cm.shape[1]), class_names, rotation=45, ha='right')
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plt.yticks(np.arange(cm.shape[0]), class_names)
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# 设置标题
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if normalize is not None:
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plt.title(f"Normalized ({normalize}) Confusion Matrix")
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else:
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plt.title("Confusion Matrix")
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plt.ylabel('True label')
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plt.xlabel('Predicted label')
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# 在每个单元格中显示数值
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thresh = cm.max() / 2.0
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for i in range(cm.shape[0]):
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for j in range(cm.shape[1]):
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if normalize is not None:
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plt.text(j, i, f"{cm[i, j]:.2f}",
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ha="center", va="center",
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color="white" if cm[i, j] > thresh else "black")
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else:
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plt.text(j, i, f"{cm[i, j]}",
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ha="center", va="center",
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color="white" if cm[i, j] > thresh else "black")
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plt.tight_layout()
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# 保存或显示图像
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if save_path:
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plt.savefig(save_path)
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logger.info(f"混淆矩阵图已保存到: {save_path}")
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else:
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plt.show()
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def classification_report(self, y_true: np.ndarray, y_pred: np.ndarray,
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output_dict: bool = False) -> Union[str, Dict]:
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"""
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生成分类报告
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Args:
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y_true: 真实标签
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y_pred: 预测标签
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output_dict: 是否以字典形式返回
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Returns:
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分类报告
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"""
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target_names = self.class_names if self.class_names else None
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return classification_report(y_true, y_pred,
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target_names=target_names,
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output_dict=output_dict,
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zero_division=0)
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def auc_roc(self, y_true: np.ndarray, y_prob: np.ndarray,
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multi_class: str = 'ovr') -> Union[float, np.ndarray]:
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"""
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计算AUC-ROC
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Args:
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y_true: 真实标签
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y_prob: 预测概率
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multi_class: 多分类处理方式，可选值: 'ovr', 'ovo'
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Returns:
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AUC-ROC
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"""
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try:
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# 如果y_true是one-hot编码，转换为类别索引
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if len(y_true.shape) > 1 and y_true.shape[1] > 1:
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y_true = np.argmax(y_true, axis=1)
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# 多分类
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if y_prob.shape[1] > 2:
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return roc_auc_score(y_true, y_prob, multi_class=multi_class, average='macro')
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# 二分类
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else:
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return roc_auc_score(y_true, y_prob[:, 1])
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except Exception as e:
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logger.error(f"计算AUC-ROC时出错: {e}")
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return 0.0
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def average_precision(self, y_true: np.ndarray, y_prob: np.ndarray,
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average: str = 'macro') -> Union[float, np.ndarray]:
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"""
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计算平均精确率
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Args:
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y_true: 真实标签
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y_prob: 预测概率
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average: 平均方式，可选值: 'micro', 'macro', 'weighted', 'samples', None
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Returns:
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平均精确率
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"""
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try:
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# 如果y_true是one-hot编码，转换为类别索引
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if len(y_true.shape) > 1 and y_true.shape[1] > 1:
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y_true = np.argmax(y_true, axis=1)
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# 多分类：使用sklearn的方法
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return average_precision_score(
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tf.keras.utils.to_categorical(y_true, num_classes=y_prob.shape[1]),
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y_prob,
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average=average
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)
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except Exception as e:
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logger.error(f"计算平均精确率时出错: {e}")
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return 0.0
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def plot_precision_recall_curve(self, y_true: np.ndarray, y_prob: np.ndarray,
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class_id: Optional[int] = None,
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figsize: Tuple[int, int] = (10, 8),
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save_path: Optional[str] = None) -> None:
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"""
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绘制精确率-召回率曲线
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Args:
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y_true: 真实标签
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y_prob: 预测概率
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class_id: 要绘制的类别ID，如果为None则绘制所有类别
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figsize: 图像大小
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save_path: 保存路径，如果为None则显示图像
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"""
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# 如果y_true是one-hot编码，转换为类别索引
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if len(y_true.shape) > 1 and y_true.shape[1] > 1:
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y_true = np.argmax(y_true, axis=1)
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# 创建图像
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plt.figure(figsize=figsize)
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# 确定要绘制的类别
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if class_id is not None and class_id < y_prob.shape[1]:
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# 绘制指定类别的PR曲线
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y_true_bin = (y_true == class_id).astype(int)
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precision, recall, _ = precision_recall_curve(y_true_bin, y_prob[:, class_id])
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avg_prec = average_precision_score(y_true_bin, y_prob[:, class_id])
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plt.step(recall, precision, where='post',
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label=f'Class {class_id} (AP = {avg_prec:.3f})')
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else:
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# 绘制所有类别的PR曲线
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for i in range(y_prob.shape[1]):
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y_true_bin = (y_true == i).astype(int)
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precision, recall, _ = precision_recall_curve(y_true_bin, y_prob[:, i])
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avg_prec = average_precision_score(y_true_bin, y_prob[:, i])
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class_name = self.class_names[i] if self.class_names else f"Class {i}"
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plt.step(recall, precision, where='post',
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label=f'{class_name} (AP = {avg_prec:.3f})')
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plt.xlabel('Recall')
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plt.ylabel('Precision')
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plt.title('Precision-Recall Curve')
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plt.legend(loc='lower left')
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plt.grid(True)
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# 保存或显示图像
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if save_path:
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plt.savefig(save_path)
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logger.info(f"精确率-召回率曲线图已保存到: {save_path}")
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else:
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plt.show()
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def calculate_all_metrics(self, y_true: np.ndarray, y_pred: np.ndarray,
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y_prob: Optional[np.ndarray] = None) -> Dict[str, float]:
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"""
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计算所有评估指标
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Args:
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y_true: 真实标签
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y_pred: 预测标签
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y_prob: 预测概率
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Returns:
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包含所有评估指标的字典
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"""
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metrics = {}
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# 基础指标
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metrics['accuracy'] = self.accuracy(y_true, y_pred)
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metrics['precision_macro'] = self.precision(y_true, y_pred, average='macro')
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metrics['recall_macro'] = self.recall(y_true, y_pred, average='macro')
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metrics['f1_macro'] = self.f1(y_true, y_pred, average='macro')
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# 如果提供了预测概率，计算AUC-ROC和平均精确率
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if y_prob is not None:
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try:
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metrics['auc_roc'] = self.auc_roc(y_true, y_prob)
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metrics['average_precision'] = self.average_precision(y_true, y_prob)
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except Exception as e:
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logger.error(f"计算概率指标时出错: {e}")
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# 类别级别的指标
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for avg in ['micro', 'weighted']:
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metrics[f'precision_{avg}'] = self.precision(y_true, y_pred, average=avg)
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metrics[f'recall_{avg}'] = self.recall(y_true, y_pred, average=avg)
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metrics[f'f1_{avg}'] = self.f1(y_true, y_pred, average=avg)
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return metrics
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def metrics_to_dataframe(self, metrics: Dict[str, float]) -> pd.DataFrame:
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"""
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将评估指标转换为DataFrame
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Args:
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metrics: 评估指标字典
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Returns:
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评估指标DataFrame
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"""
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return pd.DataFrame(metrics.items(), columns=['Metric', 'Value']).set_index('Metric')
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