File size: 3,497 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 | 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}")
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