File size: 2,854 Bytes
ba9f51f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | import pandas as pd
import numpy as np
#
#
#
# def load_data(file_path):
# return pd.read_csv(file_path)
#
#
# def preprocess_data(data):
# # 按指定列分组
# grouped = data.groupby(['ParticipantID', 'BlockID', 'TrialID'])
#
# # 计算每个组的均值和标准差，再计算尺寸的上限
# group_sizes = grouped.size()
# upper_limit = group_sizes.mean() + 1 * group_sizes.std()
#
# # 使用filter方法保留组大小小于上限的数据
# filtered_data = grouped.filter(lambda x: len(x) < upper_limit)
# return filtered_data
#
#
# def save_data(data, path):
# # 确保目标文件夹存在
# os.makedirs(os.path.dirname(path), exist_ok=True)
# # 保存数据
# data.to_csv(path, index=False)
#
#
# # 主函数
# if __name__ == "__main__":
# for i in range(79, 80):
# output_train_path = f'../Data/Study2Evaluation/Preprocessed/{i}_train_data_preprocessed_evaluation.csv'
# output_test_path = f'../Data/Study2Evaluation/Preprocessed/{i}_test_data_preprocessed_evaluation.csv'
#
# data1 = load_data(output_train_path)
# data2 = load_data(output_test_path)
#
# cleaned_data1 = preprocess_data(data1)
# cleaned_data2 = preprocess_data(data2)
#
# # 定义保存路径
# save_path_train = f'../Data/Study2Evaluation/Preprocessed/cleaned/{i}_train_data_preprocessed_evaluation.csv'
# save_path_test = f'../Data/Study2Evaluation/Preprocessed/cleaned/{i}_test_data_preprocessed_evaluation.csv'
#
# # 保存清洗后的数据
# save_data(cleaned_data1, save_path_train)
# save_data(cleaned_data2, save_path_test)
#
# print(f"Cleaned data saved for {i} train and test.")
def load_data(file_path):
return pd.read_csv(file_path)
# 数据预处理，包括特征选择、后填充和展平
def preprocess_data(data):
grouped = data.groupby(['ParticipantID', 'BlockID', 'TrialID'])
group_sizes = grouped.size()
max_group_index = group_sizes.idxmax()
max_group_rows = grouped.get_group(max_group_index).shape[0]
return max_group_rows
if __name__ == "__main__":
max_rows=0
for i in range(79, 80):
# if i ==3 or i ==6 or i ==15 or i ==19 or i== 22:
# continue
output_train_path = f'../Data/Study2Evaluation/Preprocessed/cleaned/{i}_train_data_preprocessed_evaluation.csv'
output_test_path = f'../Data/Study2Evaluation/Preprocessed/cleaned/{i}_test_data_preprocessed_evaluation.csv'
data1=load_data(output_train_path)
data2=load_data(output_test_path)
max_group_rows1 = preprocess_data(data1)
max_group_rows2 = preprocess_data(data2)
max_rows=max(max_group_rows1,max_group_rows2)
print(max_rows)
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