Task3 特征工程
3.1 学习目标
- 学习时间序列数据的特征预处理方法
- 学习时间序列特征处理工具 Tsfresh（TimeSeries Fresh）的使用
3.2 内容介绍
- 数据预处理
- 时间序列数据格式处理
- 加入时间步特征time
- 特征工程
- 时间序列特征构造
- 特征筛选
- 使用 tsfresh 进行时间序列特征处理
3.3 代码示例
3.3.1 导入包并读取数据
# 包导入
import pandas as pd
import numpy as np
import tsfresh as tsf
from tsfresh import extract_features, select_features
from tsfresh.utilities.dataframe_functions import impute# 数据读取
data_train = pd.read_csv("train.csv")
data_test_A = pd.read_csv("testA.csv")
print(data_train.shape)
print(data_test_A.shape)(100000, 3)
(20000, 2)data_train.head() id heartbeat_signals label
0 0 0.9912297987616655,0.9435330436439665,0.764677… 0.0
1 1 0.9714822034884503,0.9289687459588268,0.572932… 0.0
2 2 1.0,0.9591487564065292,0.7013782792997189,0.23… 2.0
3 3 0.9757952826275774,0.9340884687738161,0.659636… 0.0
4 4 0.0,0.055816398940721094,0.26129357194994196,0… 2.0data_test_A.head() id heartbeat_signals
0 100000 0.9915713654170097,1.0,0.6318163407681274,0.13…
1 100001 0.6075533139615096,0.5417083883163654,0.340694…
2 100002 0.9752726292239277,0.6710965234906665,0.686758…
3 100003 0.9956348033996116,0.9170249621481004,0.521096…
4 100004 1.0,0.8879490481178918,0.745564725322326,0.531…3.3.2 数据预处理
# 对心电特征进行行转列处理，同时为每个心电信号加入时间步特征time
train_heartbeat_df = data_train["heartbeat_signals"].str.split(",", expand=True).stack()
train_heartbeat_df = train_heartbeat_df.reset_index()
train_heartbeat_df = train_heartbeat_df.set_index("level_0")
train_heartbeat_df.index.name = None
train_heartbeat_df.rename(columns={"level_1":"time", 0:"heartbeat_signals"}, inplace=True)
train_heartbeat_df["heartbeat_signals"] = train_heartbeat_df["heartbeat_signals"].astype(float)
train_heartbeat_df time heartbeat_signals
0 0 0.991230
0 1 0.943533
0 2 0.764677
0 3 0.618571
0 4 0.379632
... ... ...
99999 200 0.000000
99999 201 0.000000
99999 202 0.000000
99999 203 0.000000
99999 204 0.000000
20500000 rows × 2 columns# 将处理后的心电特征加入到训练数据中，同时将训练数据label列单独存储
data_train_label = data_train["label"]
data_train = data_train.drop("label", axis=1)
data_train = data_train.drop("heartbeat_signals", axis=1)
data_train = data_train.join(train_heartbeat_df)
data_train id time heartbeat_signals
0 0 0 0.991230
0 0 1 0.943533
0 0 2 0.764677
0 0 3 0.618571
0 0 4 0.379632
... ... ... ...
99999 99999 200 0.0
99999 99999 201 0.0
99999 99999 202 0.0
99999 99999 203 0.0
99999 99999 204 0.0
20500000 rows × 4 columnsdata_train[data_train["id"]==1] id time heartbeat_signals
1 1 0 0.971482
1 1 1 0.928969
1 1 2 0.572933
1 1 3 0.178457
1 1 4 0.122962
... ... ... ...
1 1 200 0.0
1 1 201 0.0
1 1 202 0.0
1 1 203 0.0
1 1 204 0.0
205 rows × 4 columns可以看到，每个样本的心电特征都由205个时间步的心电信号组成。
3.3.3 使用 tsfresh 进行时间序列特征处理
- 特征抽取
Tsfresh（TimeSeries Fresh）是一个Python第三方工具包。 它可以自动计算大量的时间序列数据的特征。此外，该包还包含了特征重要性评估、特征选择的方法，因此，不管是基于时序数据的分类问题还是回归问题，tsfresh都会是特征提取一个不错的选择。官方文档：Introduction — tsfresh 0.17.1.dev24+g860c4e1 documentation
from tsfresh import extract_features
# 特征提取
train_features = extract_features(data_train, column_id='id', column_sort='time')
train_featuresid sum_values abs_energy mean_abs_change mean_change ...
0 38.927945 18.216197 0.019894 -0.004859 ...
1 19.445634 7.705092 0.019952 -0.004762 ...
2 21.192974 9.140423 0.009863 -0.004902 ...
... ... ... ... ... ...
99997 40.897057 16.412857 0.019470 -0.004538 ...
99998 42.333303 14.281281 0.017032 -0.004902 ...
99999 53.290117 21.637471 0.021870 -0.004539 ...
100000 rows × 779 columns- 特征选择
train_features中包含了heartbeat_signals的779种常见的时间序列特征（所有这些特征的解释可以去看官方文档），这其中有的特征可能为NaN值（产生原因为当前数据不支持此类特征的计算），使用以下方式去除NaN值：
from tsfresh.utilities.dataframe_functions import impute
去除抽取特征中的NaN值
impute(train_features)
```python
id sum_values abs_energy mean_abs_change mean_change ...
0 38.927945 18.216197 0.019894 -0.004859 ...
1 19.445634 7.705092 0.019952 -0.004762 ...
2 21.192974 9.140423 0.009863 -0.004902 ...
... ... ... ... ... ...
99997 40.897057 16.412857 0.019470 -0.004538 ...
99998 42.333303 14.281281 0.017032 -0.004902 ...
99999 53.290117 21.637471 0.021870 -0.004539 ...
100000 rows × 779 columns接下来，按照特征和响应变量之间的相关性进行特征选择，这一过程包含两步：首先单独计算每个特征和响应变量之间的相关性，然后利用Benjamini-Yekutieli procedure [1] 进行特征选择，决定哪些特征可以被保留。
from tsfresh import select_features
# 按照特征和数据label之间的相关性进行特征选择
train_features_filtered = select_features(train_features, data_train_label)
train_features_filteredid sum_values fft_coefficient__attr_"abs"__coeff_35 fft_coefficient__attr_"abs"__coeff_34 ...
0 38.927945 1.168685 0.982133 ...
1 19.445634 1.460752 1.924501 ...
2 21.192974 1.787166 2.1469872 ...
... ... ... ... ...
99997 40.897057 1.190514 0.674603 ...
99998 42.333303 1.237608 1.325212 ...
99999 53.290117 0.154759 2.921164 ...
100000 rows × 700 columns可以看到经过特征选择，留下了700个特征。
