pdaaphm/algorithm/02扩充/源代码/扩充.py

# #扩充1--随机采样：是一种简单直接的数据增强方法，通过从现有数据中随机选择样本来增加数据集的大小。对于不平衡的数据集，可以通过有偏的采样来平衡类别，即对少数类的样本进行过采样，对多数类的样本进行欠采样。
import pandas as pd
import numpy as np

def extend_data_with_ordered_sampling(df, expansion_ratio=0.2):
    """
    通过有序采样的方式扩充整个数据集，不包括时间列（如果存在）。
    :param df: 原始数据帧。
    :param expansion_ratio: 扩充数据的比例，即新数据占原始数据的比例。
    :return: 扩充后的数据帧。
    """
    # 检查是否存在 'Time' 列，如果存在，则删除
    if 'Time' in df.columns:
        df = df.drop(columns=['Time'])

    # 计算需要扩充的样本数量
    n_samples = int(len(df) * expansion_ratio)

    # 对剩余数据进行排序（假设df已经是按照时间排序的，如果没有排序需要添加排序逻辑）
    df_sorted = df.sort_index()

    # 按照顺序取出一部分数据作为扩充数据，不打乱顺序
    sampled_data = df_sorted.tail(n_samples).copy()

    # 合并原始数据帧与采样数据帧
    final_data = pd.concat([df, sampled_data], ignore_index=True)

    return final_data

# 读取数据
data_path = r'D:\验收材料\空工大-装备性能退化评估和故障预测健康管理软件\里程碑最终算法\01补全\源代码\补全后的数据.csv'
data = pd.read_csv(data_path)

expansion_ratio = 0.2  # 扩充数据的比例

try:
    extended_data = extend_data_with_ordered_sampling(data, expansion_ratio)
    extended_data.to_csv('扩充数据-随机采样.csv', index=False)
except Exception as e:
    print(f"发生错误: {e}")


# #扩充2--数据扰动：通过在原始数据上添加小的随机扰动来生成新的数据点。这种方法适用于数值型数据，可以帮助模型学习到更加泛化的特征表示。
# import numpy as np
# import pandas as pd
#
# def add_random_perturbation(series, sigma):
#     """
#     对数值型序列添加随机扰动。
#     """
#     return series + np.random.normal(0, sigma, size=len(series))
#
# def extend_data_with_perturbation(df, sigma, expansion_ratio):
#     """
#     对数据帧中的数值型列添加随机扰动，并扩充数据。
#     """
#     # 检查是否存在 'Time' 列，如果存在，则删除
#     if 'Time' in df.columns:
#         df = df.drop(columns=['Time'])
#
#     numerical_columns = df.select_dtypes(include=[np.number]).columns
#     extended_data = df.copy()
#
#     for col in numerical_columns:
#         extended_data[col] = add_random_perturbation(df[col], sigma)
#
#     # 计算扩充的数据量
#     n_samples = int(len(df) * expansion_ratio)
#
#     # 扩充数据
#     expanded_data = extended_data.iloc[-n_samples:].copy()
#
#     # 合并原始数据和扩充数据
#     final_data = pd.concat([df, expanded_data], ignore_index=True)
#
#     return final_data
#
# # 读取数据
# data_path = r'E:\BaiduSyncdisk\zhiguan\01最近做的\算法调试\自己调试--Y\里程碑最终算法\01补全\源代码\补全后的数据-无time.csv'
# data = pd.read_csv(data_path)
#
# sigma = 0.05  # 扰动的标准差
# expansion_ratio = 0.2  # 扩充数据的比例
#
# try:
#     extended_data = extend_data_with_perturbation(data, sigma, expansion_ratio)
#     extended_data.to_csv('扩充后的数据-数据扰动.csv', index=False)
# except Exception as e:
#     print(f"发生错误: {e}")


# #扩充3--Wavelet变换:可以将信号分解成不同频率的子信号，然后可以通过对这些子信号进行处理来实现数据扩充。
# import numpy as np
# import pandas as pd
# import pywt
#
# def wavelet_transform(series, wavelet='db1', level=1):
#     """
#     对一维数值数据进行小波变换。
#     """
#     return pywt.wavedec(series, wavelet, level=level)
#
# def wavelet_reconstruct(coeffs, wavelet='db1'):
#     """
#     使用小波变换后的系数重构数据。
#     """
#     return pywt.waverec(coeffs, wavelet)
#
# def perturb_coeffs(coeffs, sigma):
#     """
#     对小波变换的系数进行扰动。
#     """
#     perturbed_coeffs = []
#     for coeff in coeffs:
#         # 对细节系数进行扰动，近似系数保持不变
#         if np.issubdtype(coeff.dtype, np.number):
#             perturbed_coeff = coeff + sigma * np.random.randn(*coeff.shape)
#         else:
#             perturbed_coeff = coeff
#         perturbed_coeffs.append(perturbed_coeff)
#     return perturbed_coeffs
#
# def extend_data_with_wavelet(df, wavelet='db1', level=1, sigma=0.05, expansion_ratio=0.2):
#     # 检查是否存在 'Time' 列，如果存在，则删除
#     if 'Time' in df.columns:
#         df = df.drop(columns=['Time'])
#
#     numerical_columns = df.select_dtypes(include=[np.number]).columns
#     extended_data = df.copy()
#
#     for col in numerical_columns:
#         coeffs = wavelet_transform(df[col], wavelet, level)
#         perturbed_coeffs = perturb_coeffs(coeffs, sigma)
#         reconstructed_series = wavelet_reconstruct(perturbed_coeffs, wavelet)
#         extended_data[col] = reconstructed_series[:len(df[col])]  # 保持原数据长度
#
#     # 计算扩充的数据量
#     n_samples = int(len(df) * expansion_ratio)
#
#     # 扩充数据
#     expanded_data = extended_data.iloc[-n_samples:].copy()
#
#     # 合并原始数据和扩充数据
#     final_data = pd.concat([extended_data, expanded_data], ignore_index=True)
#
#     return final_data
#
# # 读取数据
# data_path = r'E:\BaiduSyncdisk\zhiguan\01最近做的\算法调试\自己调试--Y\里程碑最终算法\01补全\源代码\补全后的数据-无time.csv'
# data = pd.read_csv(data_path)
#
# wavelet = 'db1'  # 选择小波基
# level = 1  # 分解层数
# sigma = 0.05  # 扰动的标准差
# expansion_ratio = 0.2  # 扩充数据的比例
#
# try:
#     extended_data = extend_data_with_wavelet(data, wavelet, level, sigma, expansion_ratio)
#     extended_data.to_csv('扩充后的数据-wavelet.csv', index=False)
# except Exception as e:
#     print(f"发生错误: {e}")


# #扩充四--小波系数扰动：对小波变换后的系数进行扰动，即在系数中加入随机噪声。这是通过在系数上加上标准差为sigma的高斯随机数实现的
# import numpy as np
# import pandas as pd
# import pywt
#
# def wavelet_transform(series, wavelet='db1', level=1):
#     return pywt.wavedec(series, wavelet, level=level)
#
# def wavelet_reconstruct(coeffs, wavelet='db1'):
#     return pywt.waverec(coeffs, wavelet)
#
# def perturb_coeffs(coeffs, sigma):
#     perturbed_coeffs = []
#     for coeff in coeffs:
#         if np.issubdtype(coeff.dtype, np.number):
#             perturbed_coeff = coeff + sigma * np.random.randn(*coeff.shape)
#         else:
#             perturbed_coeff = coeff
#         perturbed_coeffs.append(perturbed_coeff)
#     return perturbed_coeffs
#
# def enhance_or_reduce(coeffs, factor):
#     """
#     对小波变换后的高频系数进行增强或衰减。
#     """
#     enhanced_coeffs = []
#     for i, coeff in enumerate(coeffs):
#         # 细节系数从索引1开始，我们对其进行增强或衰减
#         if i > 0:
#             enhanced_coeffs.append(coeff * factor)
#         else:
#             enhanced_coeffs.append(coeff)
#     return enhanced_coeffs
#
# def extend_data_with_wavelet(df, wavelet='db1', level=1, sigma=0.05, expansion_ratio=0.2):
#     # 检查是否存在 'Time' 列，如果存在，则删除
#     if 'Time' in df.columns:
#         df = df.drop(columns=['Time'])
#
#     numerical_columns = df.select_dtypes(include=[np.number]).columns
#     extended_data = df.copy()
#
#     for col in numerical_columns:
#         coeffs = wavelet_transform(df[col], wavelet, level)
#         perturbed_coeffs = perturb_coeffs(coeffs, sigma)
#         enhanced_coeffs = enhance_or_reduce(perturbed_coeffs, factor=1.1)  # 增强高频系数
#         reconstructed_series = wavelet_reconstruct(enhanced_coeffs, wavelet)
#         extended_data[col] = reconstructed_series
#
#     # 计算扩充的数据量
#     n_samples = int(len(df) * expansion_ratio)
#
#     # 扩充数据
#     expanded_data = extended_data.iloc[-n_samples:].copy()
#
#     # 合并原始数据和扩充数据
#     final_data = pd.concat([df, expanded_data], axis=0, ignore_index=True)
#
#     return final_data
#
#
# data = pd.read_csv(r'E:\BaiduSyncdisk\zhiguan\01最近做的\算法调试\自己调试--Y\里程碑最终算法\01补全\源代码\补全后的数据-无time.csv')
# wavelet = 'db1'  # 选择小波基
# level = 1  # 分解层数
# sigma = 0.05  # 扰动的标准差
# expansion_ratio = 0.2  # 扩充数据的比例
#
# try:
#     extended_data = extend_data_with_wavelet(data, wavelet, level, sigma, expansion_ratio)
#     extended_data.to_csv('扩充后的数据-Wavelet变换.csv', index=False)
# except Exception as e:
#     print(f"发生错误: {e}")



# #扩充5：小波线性插值
# import numpy as np
# import pandas as pd
# import pywt
#
# def wavelet_transform(series, wavelet='db1', level=1):
#     """
#     对一维数值数据进行小波变换。
#     """
#     return pywt.wavedec(series, wavelet, level=level)
#
# def wavelet_reconstruct(coeffs, wavelet='db1'):
#     """
#     使用小波变换后的系数重构数据。
#     """
#     return pywt.waverec(coeffs, wavelet)
#
# def perturb_coeffs(coeffs, sigma):
#     """
#     对小波变换的系数进行扰动。
#     """
#     perturbed_coeffs = []
#     for coeff in coeffs:
#         if np.issubdtype(coeff.dtype, np.number):
#             perturbed_coeff = coeff + sigma * np.random.randn(*coeff.shape)
#         else:
#             perturbed_coeff = coeff
#         perturbed_coeffs.append(perturbed_coeff)
#     return perturbed_coeffs
#
# def interpolate_coeffs(coeffs, new_length):
#     """
#     对小波变换的系数进行线性插值。
#     """
#     interpolated_coeffs = []
#     for coeff in coeffs:
#         if new_length:
#             coeff = np.interp(np.arange(new_length), np.arange(len(coeff)), coeff)
#         interpolated_coeffs.append(coeff)
#     return interpolated_coeffs
#
# def extend_data_with_wavelet(df, wavelet='db1', level=1, sigma=0.05, expansion_ratio=0.2, new_length=None):
#     # 检查是否存在 'Time' 列，如果存在，则删除
#     if 'Time' in df.columns:
#         df = df.drop(columns=['Time'])
#
#     numerical_columns = df.select_dtypes(include=[np.number]).columns
#     extended_data = df.copy()
#
#     for col in numerical_columns:
#         coeffs = wavelet_transform(df[col], wavelet, level)
#         perturbed_coeffs = perturb_coeffs(coeffs, sigma)
#         if new_length is not None:
#             perturbed_coeffs = interpolate_coeffs(perturbed_coeffs, new_length)
#         reconstructed_series = wavelet_reconstruct(perturbed_coeffs, wavelet)
#         extended_data[col] = reconstructed_series[:len(df[col])]  # 确保数据长度一致
#
#     # 计算扩充的数据量
#     n_samples = int(len(df) * expansion_ratio)
#
#     # 扩充数据
#     expanded_data = extended_data.iloc[-n_samples:].copy()
#
#     # 合并原始数据和扩充数据
#     final_data = pd.concat([extended_data, expanded_data], ignore_index=True)
#
#     return final_data
#
# # 读取数据
# data = pd.read_csv(r'E:\BaiduSyncdisk\zhiguan\01最近做的\算法调试\自己调试--Y\里程碑最终算法\01补全\源代码\补全后的数据-无time.csv')
#
# wavelet = 'db1'  # 选择小波基
# level = 1  # 分解层数
# sigma = 0.05  # 扰动的标准差
# expansion_ratio = 0.2  # 扩充数据的比例
# new_length = None  # 设置新的数据长度，如果需要
#
# try:
#     extended_data = extend_data_with_wavelet(data, wavelet, level, sigma, expansion_ratio, new_length)
#     extended_data.to_csv('扩充后的数据-小波线性.csv', index=False)
# except Exception as e:
#     print(f"发生错误: {e}")