File size: 4,252 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 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | import pandas as pd
import numpy as np
from concurrent.futures import ThreadPoolExecutor
max_timesteps=299
feature_num=16
label_nums=9
def generate_partial_sequences(row, max_timesteps=max_timesteps, features_per_timestep=feature_num, fill_value=-10):
# 确定实际长度
actual_length_indices = np.where(row[:-label_nums] != fill_value)[0] # 排除最后的标签列
if len(actual_length_indices) > 0:
actual_length = (actual_length_indices[-1] // features_per_timestep) + 1
else:
actual_length = 0
partial_sequences = []
# step_size = max(1, int(actual_length * 0.1)) # 步长为实际长度的10%，至少为1
step_size =5
for end_length in range(step_size, actual_length + step_size, step_size):
# 计算结束点，不超过实际长度
end_length = min(end_length, actual_length)
partial_sequence_list = row[:end_length * features_per_timestep].tolist()
selected_features_list = [partial_sequence_list[i:i + 10] for i in
range(0, len(partial_sequence_list), features_per_timestep)]
selected_features_list = [item for sublist in selected_features_list for item in sublist]
ProgressOfTask= end_length/actual_length
hand_rotation_axis = partial_sequence_list[-6:-3]
hand_direction = partial_sequence_list[-3:]
padding_length = (max_timesteps - end_length) * (features_per_timestep-6) # 计算填充长度
selected_features_list.extend([fill_value] * padding_length) # 添加填充
selected_features_list.extend(row[-9:]) # 添加标签
selected_features_list.extend(hand_rotation_axis) # 添加HandRotationAxis和HandDirection
selected_features_list.extend(hand_direction)
selected_features_list.append(ProgressOfTask) # 添加ProgressOfTask
partial_sequences.append(selected_features_list)
return partial_sequences
def process_row(index, df):
"""Wrapper function to handle the DataFrame row."""
row=df.iloc[index]
return generate_partial_sequences(row)
def main(df, num_threads=20):
"""Process the DataFrame using multiple threads."""
with ThreadPoolExecutor(max_workers=num_threads) as executor:
# 创建一个future列表，对每一行数据并行应用 process_row 函数
futures = [executor.submit(process_row, index, df) for index in range(len(df))]
# 使用 as_completed 来获取已完成的future结果
results = []
for future in futures:
results.extend(future.result())
# 将结果转换为 DataFrame
columns = df.columns
# 选择不以'HandRotationAxis'和'HandDirection'开头的列
columns_to_keep = [column for column in columns if
not column.startswith('HandRotationAxis') and not column.startswith('HandDirection')]
# 现在添加具体的旋转轴和方向列到列表的末尾
# 如果有特定的顺序要求，这里按照特定顺序添加
columns_to_keep.extend([
'HandRotationAxis_X', 'HandRotationAxis_Y', 'HandRotationAxis_Z',
'HandDirection_X', 'HandDirection_Y', 'HandDirection_Z',
'ProgressOfTask'
])
#print(len(columns_to_keep))
partial_sequences_df = pd.DataFrame(results, columns=columns_to_keep)
return partial_sequences_df
if __name__ == '__main__':
#加载数据集
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/Supervised/{i}_train_data_preprocessed_evaluation.csv'
df = pd.read_csv(file_path)
partial_sequences_df = main(df)
save_path_csv = f'../Data/Study2Evaluation/Dataset/{i}_traindataset.csv'
partial_sequences_df.to_csv(save_path_csv, index=False)
file_path = f'../Data/Study2Evaluation/Supervised/{i}_test_data_preprocessed_evaluation.csv'
df = pd.read_csv(file_path)
partial_sequences_df = main(df)
save_path_csv = f'../Data/Study2Evaluation/Dataset/{i}_testdataset.csv'
partial_sequences_df.to_csv(save_path_csv, index=False)
|