data_processing_code/WIdeFormat.py

import pandas as pd
import numpy as np

# 加载数据
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, features, label):
    # 根据ParticipantID, BlockID, 和 TrialID对数据进行分组
    grouped = data.groupby(['ParticipantID', 'BlockID', 'TrialID'])
    # 提取特征和标签，进行后填充处理
    sequences = []
    labels = []
    for _, group in grouped:
        sequence = group[features].values
        sequence_label = group[label].values[0]  # 假设每个序列的标签是相同的
        sequences.append(sequence)
        labels.append(sequence_label)
    # 获取最大序列长度
    max_len = 299
    # 后填充处理
    sequences_padded = [np.pad(seq, ((0, max_len - len(seq)), (0, 0)), 'constant', constant_values=(-10, -10)) for seq
                        in sequences]

    # 展平每个序列
    flattened_sequences = np.array([seq.flatten() for seq in sequences_padded])

    return flattened_sequences, labels, max_len

# 保存处理后的数据
def save_transformed_data(flattened_sequences, labels_sequence, max_len, features,labels,output_file_path):
    # 创建列名
    column_names = [f"{feature}_t{time_step}" for time_step in range(1, max_len + 1) for feature in features]
    # 创建DataFrame
    flattened_df = pd.DataFrame(flattened_sequences, columns=column_names)
    label_column_names=[f"{label}" for label in labels]
    flattened_df[label_column_names]=labels_sequence
    # 保存到Excel
    flattened_df.to_csv(output_file_path, index=False)

# 主函数
if __name__ == "__main__":
    # 定义文件路径和特征
    for i in range(79, 80):
        # if i ==3 or i ==6 or i ==15 or i ==19 or i== 22:
        #     continue
        file_path = f'../Data/Study2Evaluation/Preprocessed/cleaned/{i}_train_data_preprocessed_evaluation.csv'  # 更新为实际文件路径
        output_file_path = f'../Data/Study2Evaluation/Supervised/{i}_train_data_preprocessed_evaluation.csv'

        features = ["HMDA", "HMDAV", "HandA", "HandAV", "LeyeA", "LeyeAV", 'HMDL', "HMDLV", "HandL", "HandLV",
                    'HandRotationAxis_X', 'HandRotationAxis_Y', 'HandRotationAxis_Z', 'HandDirection_X',
                    'HandDirection_Y', 'HandDirection_Z']
        labels = ['ParticipantID', 'BlockID', 'TrialID', 'TargetLocationX', 'TargetLocationY', 'TargetLocationZ',
                  'TargetScale', 'LLabel', 'ALabel']
        # 加载和处理数据
        data = load_data(file_path)
        flattened_sequences, labels_sequence, max_len = preprocess_data(data, features, labels)
        # 保存转换后的数据
        save_transformed_data(flattened_sequences, labels_sequence, max_len, features, labels, output_file_path)
        print(f"Data transformed and saved to {output_file_path}")
        file_path = f'../Data/Study2Evaluation/Preprocessed/cleaned/{i}_test_data_preprocessed_evaluation.csv'  # 更新为实际文件路径
        output_file_path = f'../Data/Study2Evaluation/Supervised/{i}_test_data_preprocessed_evaluation.csv'
        data = load_data(file_path)
        flattened_sequences, labels_sequence, max_len = preprocess_data(data, features, labels)
        save_transformed_data(flattened_sequences, labels_sequence, max_len, features, labels, output_file_path)
        print(f"Data transformed and saved to {output_file_path}")