python对csv数据集实现贝叶斯算法

下面是一个简单的示例代码，用于演示如何使用Python中的贝叶斯算法来分类CSV数据集：

import csv
import math

# 读取CSV文件
def load_csv(filename):
    dataset = []
    with open(filename, 'r') as file:
        csv_reader = csv.reader(file)
        for row in csv_reader:
            dataset.append(row)
    return dataset

# 将字符串列转换为浮点数列
def str_column_to_float(dataset, column):
    for row in dataset:
        row[column] = float(row[column].strip())

# 将字符串列转换为整数列
def str_column_to_int(dataset, column):
    class_values = [row[column] for row in dataset]
    unique = set(class_values)
    lookup = {}
    for i, value in enumerate(unique):
        lookup[value] = i
    for row in dataset:
        row[column] = lookup[row[column]]
    return lookup

# 将数据集分成n个折叠
def cross_validation_split(dataset, n_folds):
    fold_size = int(len(dataset) / n_folds)
    folds = []
    dataset_copy = list(dataset)
    for i in range(n_folds):
        fold = []
        while len(fold) < fold_size:
            index = randrange(len(dataset_copy))
            fold.append(dataset_copy.pop(index))
        folds.append(fold)
    return folds

# 计算每个类别的概率
def class_probabilities(dataset):
    total_instances = len(dataset)
    probabilities = {}
    for row in dataset:
        class_value = row[-1]
        if class_value not in probabilities:
            probabilities[class_value] = 0
        probabilities[class_value] += 1
    for class_value, count in probabilities.items():
        probabilities[class_value] = count / total_instances
    return probabilities

# 计算Gaussian分布的概率密度函数
def calculate_probability(x, mean, stdev):
    exponent = math.exp(-(math.pow(x-mean,2)/(2*math.pow(stdev,2))))
    return (1 / (math.sqrt(2*math.pi) * stdev)) * exponent

# 计算每个属性在每个类别中的均值和标准差
def summarize_dataset(dataset):
    summaries = [(mean(column), stdev(column)) for column in zip(*dataset)]
    del(summaries[-1])
    return summaries

# 计算均值
def mean(numbers):
    return sum(numbers)/float(len(numbers))

# 计算标准差
def stdev(numbers):
    avg = mean(numbers)
    variance = sum([pow(x-avg,2) for x in numbers])/float(len(numbers)-1)
    return math.sqrt(variance)

# 将数据集分成类别
def separate_by_class(dataset):
    separated = {}
    for i in range(len(dataset)):
        vector = dataset[i]
        class_value = vector[-1]
        if (class_value not in separated):
            separated[class_value] = []
        separated[class_value].append(vector)
    return separated

# 计算每个属性在每个类别中的均值和标准差
def summarize_by_class(dataset):
    separated = separate_by_class(dataset)
    summaries = {}
    for class_value, rows in separated.items():
        summaries[class_value] = summarize_dataset(rows)
    return summaries

# 使用贝叶斯算法进行分类
def calculate_class_probabilities(summaries, input_vector):
    probabilities = {}
    for class_value, class_summaries in summaries.items():
        probabilities[class_value] = 1
        for i in range(len(class_summaries)):
            mean, stdev = class_summaries[i]
            x = input_vector[i]
            probabilities[class_value] *= calculate_probability(x, mean, stdev)
    return probabilities

# 对数据集进行分类
def predict(summaries, input_vector):
    probabilities = calculate_class_probabilities(summaries, input_vector)
    best_label, best_prob = None, -1
    for class_value, probability in probabilities.items():
        if best_label is None or probability > best_prob:
            best_prob = probability
            best_label = class_value
    return best_label

# 评估算法
def evaluate_algorithm(dataset, algorithm, n_folds, *args):
    folds = cross_validation_split(dataset, n_folds)
    scores = []
    for fold in folds:
        train_set = list(folds)
        train_set.remove(fold)
        train_set = sum(train_set, [])
        test_set = []
        for row in fold:
            row_copy = list(row)
            test_set.append(row_copy)
            row_copy[-1] = None
        predicted = algorithm(train_set, test_set, *args)
        actual = [row[-1] for row in fold]
        accuracy = accuracy_metric(actual, predicted)
        scores.append(accuracy)
    return scores

# 计算分类的准确度
def accuracy_metric(actual, predicted):
    correct = 0
    for i in range(len(actual)):
        if actual[i] == predicted[i]:
            correct += 1
    return correct / float(len(actual))

# 主函数
def main():
    # 加载数据集
    dataset = load_csv('dataset.csv')
    for i in range(len(dataset[0])-1):
        str_column_to_float(dataset, i)
    # 将类别转换为整数
    str_column_to_int(dataset, len(dataset[0])-1)
    # 评估算法
    n_folds = 5
    scores = evaluate_algorithm(dataset, naive_bayes, n_folds)
    print('Accuracy: %.3f%%' % (sum(scores)/float(len(scores)) * 100))

if __name__ == '__main__':
    main()

需要注意的是，这是一个简单的示例代码，不一定适用于所有的CSV数据集。对于不同的数据集，可能需要进行一些调整和优化。