from tsfresh.feature_extraction import ComprehensiveFCParameters from tsfresh.utilities.dataframe_functions import impute extraction_settings = ComprehensiveFCParameters() X = extract_features(data0, column_id='id', column_sort='DateTime', default_fc_parameters=extraction_settings, # impute就是自动移除所有...
column_sort="time", default_fc_parameters=EfficientFCParameters(), n_jobs=0 ) print("\nMultivariate features:") print(features_multivariate.head()) 自定义特征提取方法 TSFresh框架允许通过tsfresh.feature_extraction.feature_calculators模块定制特征提取函数。 # 多变量特征提取实现 # 构造附加时间序列变量 t...
default_fc_parameters=EfficientFCParameters(), n_jobs=0 ) print("\nMultivariate features:") print(features_multivariate.head()) 以下展示了使用matplotlib进行数据分布可视化: # 计算时间序列均值特征 custom_features = time_series.groupby("id")["value"].apply(mean) print("\nCustom features (mean of...
# 多变量特征提取实现 # 构造附加时间序列变量 time_series["value2"] = time_series["value"] * 0.5 + np.random.normal(0, 0.05, len(time_series)) # 执行多变量特征提取 features_multivariate = extract_features( time_series, column_id="id", column_sort="time", default_fc_parameters=Efficient...
default_fc_parameters=extraction_settings, # impute就是自动移除所有NaN的特征 impute_function=impute) X.head() 1. 2. 3. 4. 5. 6. 7. X现在包含每个机器人执行(= id)的单行,所有特征tsfresh都是根据该id的测量时间序列值计算出来的。 查看特征提取之后原始特征变为了哪些特征: ...
settings_time = TimeBasedFCParameters() settings_time 1. 2. 提取特征,指定列值、类型和id。 X_tsfresh = extract_features(df, column_id="id", column_value='value', column_kind='kind', default_fc_parameters=settings_time) X_tsfresh.head() ...
default_fc_parameters=extraction_settings, # we impute = remove all NaN features automatically impute_function=impute, show_warnings=False) X_extracted= pd.DataFrame(X_extracted, index=X_extracted.index, columns=X_extracted.columns) values = list(range(1, 13)) ...
default_fc_parameters=extraction_settings, # we impute = remove all NaN features automatically impute_function=impute, show_warnings=False) X_extracted= pd.DataFrame(X_extracted, index=X_extracted.index, columns=X_extracted.columns) values = list(range(1, 13)) ...
default_fc_parameters=extraction_settings, # we impute = remove all NaN features automatically impute_function=impute, show_warnings=False) X_extracted= pd.DataFrame(X_extracted, index=X_extracted.index, columns=X_extracted.columns) values = list(range(1, 13)) ...
time_series,column_id="id",column_sort="time",default_fc_parameters=EfficientFCParameters(),n_jobs=0 )print("\nMultivariate features:")print(features_multivariate.head()) 自定义特征提取方法 TSFresh框架允许通过 tsfresh.feature_extraction.feature_calculators ...