Install with pip:
python -m pip install "featuretools[tsfresh]"In tsfresh, this is how you can calculate a feature.
from tsfresh.feature_extraction.feature_calculators import agg_autocorrelation
data = list(range(10))
param = [{'f_agg': 'mean', 'maxlag': 5}]
agg_autocorrelation(data, param=param)[('f_agg_"mean"__maxlag_5', 0.1717171717171717)]With tsfresh primtives in featuretools, this is how you can calculate the same feature.
from featuretools.tsfresh import AggAutocorrelation
data = list(range(10))
AggAutocorrelation(f_agg='mean', maxlag=5)(data)0.1717171717171717In featuretools, this is how to combine tsfresh primitives with built-in or other installed primitives.
import featuretools as ft
from featuretools.tsfresh import AggAutocorrelation, Mean
entityset = ft.demo.load_mock_customer(return_entityset=True)
agg_primitives = [Mean, AggAutocorrelation(f_agg='mean', maxlag=5)]
feature_matrix, features = ft.dfs(entityset=entityset, target_dataframe_name='sessions', agg_primitives=agg_primitives)
feature_matrix[[
'MEAN(transactions.amount)',
'AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)',
]].head() MEAN(transactions.amount) AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)
session_id
1 76.813125 0.044268
2 74.696000 -0.053110
3 88.600000 0.007520
4 64.557200 -0.034542
5 70.638182 -0.100571
Notice that tsfresh primtives are applied across relationships in an entityset generating many features that are otherwise not possible.
feature_matrix[['customers.AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)']].head() customers.AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)
session_id
1 0.011102
2 -0.001686
3 -0.010679
4 0.011204
5 -0.010679
