#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。 from flask import Flask, request, jsonify import pandas as pd import os import requests app = Flask(__name__) 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 @app.route('/random', methods=['POST']) def upload_file(): try: # 检查请求体中是否包含文件地址 data = request.json if 'file_url' not in data: return jsonify({'code': 400, 'msg': 'No file URL provided'}) file_url = data['file_url'] # 使用requests获取文件内容 response = requests.get(file_url) if response.status_code != 200: return jsonify({'code': 500, 'msg': 'Failed to retrieve file from URL'}) # 读取数据集 df = pd.read_csv(pd.compat.StringIO(response.text), header=None) # 调用扩充数据函数 expansion_ratio = 0.2 # 扩充数据的比例 result_data = extend_data_with_ordered_sampling(df, expansion_ratio) # 将结果保存为CSV文件 result_file_path = os.path.join('/tmp', '扩充数据-随机采样.csv') result_data.to_csv(result_file_path, index=False, header=False) # 构建返回数据，只返回文件路径 return jsonify({ 'code': 200, 'msg': '文件处理完成', 'file_path': result_file_path }) except Exception as e: return jsonify({ 'code': 500, 'msg': str(e) }) if __name__ == '__main__': app.run(debug=True, port=8081, host='0.0.0.0') #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。 from flask import Flask, request, jsonify import pandas as pd import numpy as np import os import requests app = Flask(__name__) 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 @app.route('/perturbation', methods=['POST']) def upload_file(): try: # 检查请求体中是否包含文件地址 data = request.json if 'file_url' not in data: return jsonify({'code': 400, 'msg': 'No file URL provided'}) file_url = data['file_url'] # 使用requests获取文件内容 response = requests.get(file_url) if response.status_code != 200: return jsonify({'code': 500, 'msg': 'Failed to retrieve file from URL'}) # 读取数据集 df = pd.read_csv(pd.compat.StringIO(response.text), header=None) sigma = 0.05 # 扰动的标准差 expansion_ratio = 0.2 # 扩充数据的比例 extended_data = extend_data_with_perturbation(data, sigma, expansion_ratio) result_file_path = os.path.join('/tmp', '扩充数据-数据扰动.csv') extended_data.to_csv(result_file_path, index=False) # 构建返回数据，只返回文件路径 return jsonify({ 'code': 200, 'msg': '文件处理完成', 'file_path': result_file_path }) except Exception as e: return jsonify({ 'code': 500, 'msg': str(e) }) if __name__ == '__main__': app.run(debug=True, port=8081, host='0.0.0.0') #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 from flask import Flask, request, jsonify import os import requests app =Flask(__name__) 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 @app.route('/Wavelet', methods=['POST']) def upload_file(): try: # 检查请求体中是否包含文件地址 data = request.json if 'file_url' not in data: return jsonify({'code': 400, 'msg': 'No file URL provided'}) file_url = data['file_url'] # 使用requests获取文件内容 response = requests.get(file_url) if response.status_code != 200: return jsonify({'code': 500, 'msg': 'Failed to retrieve file from URL'}) # 读取数据集 df = pd.read_csv(pd.compat.StringIO(response.text), header=None) 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 extended_data = extend_data_with_wavelet(data, wavelet, level, sigma, expansion_ratio) result_file_path = os.path.join('/tmp', '扩充数据-wavelet.csv') extended_data.to_csv(result_file_path, index=False) # 构建返回数据，只返回文件路径 return jsonify({ 'code': 200, 'msg': '文件处理完成', 'file_path': result_file_path }) except Exception as e: return jsonify({ 'code': 500, 'msg': str(e) }) if __name__ == '__main__': app.run(debug=True, port=8081, host='0.0.0.0') #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 from flask import Flask, request, jsonify import os import requests app = Flask(__name__) 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 @app.route('/coefficient', methods=['POST']) def upload_file(): try: # 检查请求体中是否包含文件地址 data = request.json if 'file_url' not in data: return jsonify({'code': 400, 'msg': 'No file URL provided'}) file_url = data['file_url'] # 使用requests获取文件内容 response = requests.get(file_url) if response.status_code != 200: return jsonify({'code': 500, 'msg': 'Failed to retrieve file from URL'}) # 读取数据集 df = pd.read_csv(pd.compat.StringIO(response.text), header=None) 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 extended_data = extend_data_with_wavelet(data, wavelet, level, sigma, expansion_ratio) result_file_path = os.path.join('/tmp', '扩充数据-Wavelet变换.csv') extended_data.to_csv(result_file_path, index=False) # 构建返回数据，只返回文件路径 return jsonify({ 'code': 200, 'msg': '文件处理完成', 'file_path': result_file_path }) except Exception as e: return jsonify({ 'code': 500, 'msg': str(e) }) if __name__ == '__main__': app.run(debug=True, port=8081, host='0.0.0.0') #扩充5：小波线性插值 import numpy as np import pandas as pd import pywt from flask import Flask, request, jsonify import os import requests app = Flask(__name__) 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 @app.route('/interpolation', methods=['POST']) def upload_file(): try: # 检查请求体中是否包含文件地址 data = request.json if 'file_url' not in data: return jsonify({'code': 400, 'msg': 'No file URL provided'}) file_url = data['file_url'] # 使用requests获取文件内容 response = requests.get(file_url) if response.status_code != 200: return jsonify({'code': 500, 'msg': 'Failed to retrieve file from URL'}) # 读取数据集 df = pd.read_csv(pd.compat.StringIO(response.text), header=None) wavelet = 'db1' level = 1 sigma = 0.05 expansion_ratio = 0.2 new_length = None extended_data = extend_data_with_wavelet(data, wavelet, level, sigma, expansion_ratio, new_length) result_file_path = os.path.join('/tmp', '扩充数据-小波线性.csv') extended_data.to_csv(result_file_path, index=False) # 构建返回数据，只返回文件路径 return jsonify({ 'code': 200, 'msg': '文件处理完成', 'file_path': result_file_path }) except Exception as e: return jsonify({ 'code': 500, 'msg': str(e) }) if __name__ == '__main__': app.run(debug=True, port=8081, host='0.0.0.0')