AI指标源码大全

# AI指标源码 - F1分数计算实现
from sklearn.metrics import f1_score
import numpy as np

def calculate_f1_score(y_true, y_pred, average='binary'):
    """
    计算F1分数
    
    参数:
        y_true: 真实标签数组
        y_pred: 预测标签数组
        average: 平均方式 ('binary', 'micro', 'macro', 'weighted')
    
    返回:
        f1分数值
    """
    return f1_score(y_true, y_pred, average=average)

# 使用示例
y_true = np.array([0, 1, 1, 0, 1, 0])
y_pred = np.array([0, 1, 0, 0, 1, 1])

f1 = calculate_f1_score(y_true, y_pred)
print(f"F1 Score: {f1:.4f}")  # 输出: F1 Score: 0.6667

# AI指标源码 - 回归任务评估指标
import numpy as np
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

class RegressionMetrics:
    """回归任务评估指标计算器"""
    
    def __init__(self, y_true, y_pred):
        self.y_true = np.array(y_true)
        self.y_pred = np.array(y_pred)
    
    def mse(self):
        """均方误差"""
        return mean_squared_error(self.y_true, self.y_pred)
    
    def rmse(self):
        """均方根误差"""
        return np.sqrt(self.mse())
    
    def mae(self):
        """平均绝对误差"""
        return mean_absolute_error(self.y_true, self.y_pred)
    
    def r2(self):
        """R²决定系数"""
        return r2_score(self.y_true, self.y_pred)

# 使用示例
y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]

metrics = RegressionMetrics(y_true, y_pred)
print(f"MSE: {metrics.mse():.4f}")   # 0.3750
print(f"RMSE: {metrics.rmse():.4f}")  # 0.6124
print(f"MAE: {metrics.mae():.4f}")   # 0.5
print(f"R²: {metrics.r2():.4f}")    # 0.9486

# AI指标源码 - BLEU分数计算
import nltk
from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction

# 下载必要资源
nltk.download('punkt')

def calculate_bleu(reference, candidate, n=4):
    """
    计算BLEU分数
    
    参数:
        reference: 参考译文（字符串或分词列表）
        candidate: 候选译文（字符串或分词列表）
        n: n-gram的最大阶数
    
    返回:
        BLEU分数
    """
    # 如果输入是字符串，进行分词
    if isinstance(reference, str):
        reference = nltk.word_tokenize(reference.lower())
    if isinstance(candidate, str):
        candidate = nltk.word_tokenize(candidate.lower())
    
    # 使用平滑函数处理零值情况
    smoothing = SmoothingFunction().method1
    
    # 计算不同n-gram的权重
    weights = tuple([1.0/n] * n)
    
    return sentence_bleu([reference], candidate, weights=weights, smoothing_function=smoothing)

# 使用示例
reference = "the cat is sitting on the mat"
candidate = "the cat sits on the mat"

bleu_score = calculate_bleu(reference, candidate)
print(f"BLEU Score: {bleu_score:.4f}")