Feature extraction with tsfresh transformer

In this tutorial, we show how you can use aeon with tsfresh to first extract features from time series, so that we can then use any scikit-learn estimator.

Preliminaries

You have to install tsfresh if you haven’t already. To install it, uncomment the cell below:

# !pip install --upgrade tsfresh

import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline

from aeon.datasets import load_arrow_head, load_basic_motions
from aeon.transformations.collection.feature_based import TSFreshFeatureExtractor

Univariate time series classification data

For more details on the data set, see the univariate time series classification notebook.

X, y = load_arrow_head(return_X_y=True, return_type="nested_univ")
X_train, X_test, y_train, y_test = train_test_split(X, y)
print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)

(158, 1) (158,) (53, 1) (53,)

X_train.head()

	dim_0
69	0 -1.7998 1 -1.7987 2 -1.7942 3 ...
103	0 -1.8091 1 -1.8067 2 -1.7866 3 ...
34	0 -2.0417 1 -2.0572 2 -2.0522 3 ...
14	0 -2.1888 1 -2.1855 2 -2.1765 3 ...
121	0 -1.9586 1 -1.9371 2 -1.8798 3 ...

#  binary classification task
np.unique(y_train)

array(['0', '1', '2'], dtype=object)

Using tsfresh to extract features

# tf = TsFreshTransformer()
t = TSFreshFeatureExtractor(default_fc_parameters="efficient", show_warnings=False)
Xt = t.fit_transform(X_train)
Xt.head()

/Users/mloning/Documents/Research/software/aeon/aeon/aeon/transformations/panel/tsfresh.py:164: UserWarning: tsfresh requires a unique index, but found non-unique. To avoid this warning, please make sure the index of X contains only unique values.
  "tsfresh requires a unique index, but found "
Feature Extraction: 100%|██████████| 5/5 [00:10<00:00,  2.05s/it]

	dim_0__variance_larger_than_standard_deviation	dim_0__has_duplicate_max	dim_0__has_duplicate_min	dim_0__has_duplicate	dim_0__sum_values	dim_0__abs_energy	dim_0__mean_abs_change	dim_0__mean_change	dim_0__mean_second_derivative_central	dim_0__median	...	dim_0__fourier_entropy__bins_2	dim_0__fourier_entropy__bins_3	dim_0__fourier_entropy__bins_5	dim_0__fourier_entropy__bins_10	dim_0__fourier_entropy__bins_100	dim_0__permutation_entropy__dimension_3__tau_1	dim_0__permutation_entropy__dimension_4__tau_1	dim_0__permutation_entropy__dimension_5__tau_1	dim_0__permutation_entropy__dimension_6__tau_1	dim_0__permutation_entropy__dimension_7__tau_1
0	0.0	0.0	0.0	1.0	-0.000080	249.998516	0.052357	-0.000001	-0.000005	-0.024066	...	0.046288	0.092513	0.092513	0.092513	0.250609	1.323194	1.819631	2.183824	2.463220	2.707387
1	0.0	0.0	1.0	1.0	-0.000525	250.000756	0.049118	0.000000	-0.000006	-0.031622	...	0.046288	0.046288	0.092513	0.092513	0.184769	1.213529	1.668744	2.081159	2.418614	2.707518
2	0.0	0.0	0.0	1.0	-0.000034	249.998998	0.069971	0.000084	0.000025	0.018880	...	0.081510	0.092513	0.092513	0.138673	0.311663	1.116706	1.545256	1.889777	2.155644	2.374722
3	0.0	0.0	0.0	1.0	0.000202	249.999702	0.067601	-0.000002	-0.000010	0.384770	...	0.046288	0.092513	0.092513	0.204643	0.414263	1.323315	1.915330	2.406197	2.794719	3.117007
4	0.0	0.0	0.0	1.0	-0.000146	249.998674	0.050355	-0.000004	-0.000046	-0.045353	...	0.046288	0.092513	0.092513	0.092513	0.230801	1.173933	1.628543	2.003443	2.303091	2.559695

5 rows × 773 columns

Using tsfresh with aeon

classifier = make_pipeline(
    TSFreshFeatureExtractor(default_fc_parameters="efficient", show_warnings=False),
    RandomForestClassifier(),
)
classifier.fit(X_train, y_train)
classifier.score(X_test, y_test)

/Users/mloning/Documents/Research/software/aeon/aeon/aeon/transformations/panel/tsfresh.py:164: UserWarning: tsfresh requires a unique index, but found non-unique. To avoid this warning, please make sure the index of X contains only unique values.
  "tsfresh requires a unique index, but found "
Feature Extraction: 100%|██████████| 5/5 [00:11<00:00,  2.21s/it]
/Users/mloning/Documents/Research/software/aeon/aeon/aeon/transformations/panel/tsfresh.py:164: UserWarning: tsfresh requires a unique index, but found non-unique. To avoid this warning, please make sure the index of X contains only unique values.
  "tsfresh requires a unique index, but found "
Feature Extraction: 100%|██████████| 5/5 [00:03<00:00,  1.45it/s]

0.8490566037735849

Multivariate time series classification data

X, y = load_basic_motions(return_X_y=True, return_type="nested_univ")
X_train, X_test, y_train, y_test = train_test_split(X, y)
print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)

(60, 6) (60,) (20, 6) (20,)

#  multivariate input data
X_train.head()

	dim_0	dim_1	dim_2	dim_3	dim_4	dim_5
20	0 -0.294498 1 -0.294498 2 -0.050044 3...	0 0.540218 1 0.540218 2 -0.515245 3...	0 0.218114 1 0.218114 2 -0.301108 3...	0 -0.045277 1 -0.045277 2 0.103872 3...	0 -0.002663 1 -0.002663 2 -0.183773 3...	0 0.031960 1 0.031960 2 0.037287 3...
26	0 -0.761604 1 -0.761604 2 0.121078 3...	0 0.260125 1 0.260125 2 -1.423255 3...	0 -0.064487 1 -0.064487 2 0.075600 3...	0 0.069248 1 0.069248 2 -0.282318 3...	0 0.242367 1 0.242367 2 -0.332922 3...	0 -0.007990 1 -0.007990 2 0.239704 3...
7	0 -0.352746 1 -0.352746 2 -1.354561 3...	0 0.316845 1 0.316845 2 0.490525 3...	0 -0.473779 1 -0.473779 2 1.454261 3...	0 -0.327595 1 -0.327595 2 -0.269001 3...	0 0.106535 1 0.106535 2 0.021307 3...	0 0.197090 1 0.197090 2 0.460763 3...
8	0 -0.342233 1 -0.342233 2 -0.298542 3...	0 0.327415 1 0.327415 2 -0.527154 3...	0 0.157229 1 0.157229 2 0.248585 3...	0 0.394179 1 0.394179 2 -0.037287 3...	0 0.074574 1 0.074574 2 -0.087891 3...	0 -0.037287 1 -0.037287 2 -0.050604 3...
10	0 0.206148 1 0.206148 2 6.53436...	0 -0.658294 1 -0.658294 2 4.597327 3...	0 0.469612 1 0.469612 2 -2.723661 3...	0 -0.106535 1 -0.106535 2 -0.439456 3...	0 0.306288 1 0.306288 2 1.717875 3...	0 0.950824 1 0.950824 2 -1.041379 3...

t = TSFreshFeatureExtractor(default_fc_parameters="efficient", show_warnings=False)
Xt = t.fit_transform(X_train)
Xt.head()

/Users/mloning/Documents/Research/software/aeon/aeon/aeon/transformations/panel/tsfresh.py:164: UserWarning: tsfresh requires a unique index, but found non-unique. To avoid this warning, please make sure the index of X contains only unique values.
  "tsfresh requires a unique index, but found "
Feature Extraction: 100%|██████████| 5/5 [00:18<00:00,  3.69s/it]

	dim_0__variance_larger_than_standard_deviation	dim_0__has_duplicate_max	dim_0__has_duplicate_min	dim_0__has_duplicate	dim_0__sum_values	dim_0__abs_energy	dim_0__mean_abs_change	dim_0__mean_change	dim_0__mean_second_derivative_central	dim_0__median	...	dim_5__fourier_entropy__bins_2	dim_5__fourier_entropy__bins_3	dim_5__fourier_entropy__bins_5	dim_5__fourier_entropy__bins_10	dim_5__fourier_entropy__bins_100	dim_5__permutation_entropy__dimension_3__tau_1	dim_5__permutation_entropy__dimension_4__tau_1	dim_5__permutation_entropy__dimension_5__tau_1	dim_5__permutation_entropy__dimension_6__tau_1	dim_5__permutation_entropy__dimension_7__tau_1
0	0.0	0.0	0.0	1.0	33.334188	110.735119	0.822452	0.000639	0.001751	0.164096	...	0.165443	0.165443	0.165443	0.192626	0.545824	1.279774	1.910772	2.565051	3.096812	3.567632
1	1.0	0.0	0.0	1.0	73.888480	220.949429	0.964075	-0.002087	-0.003908	0.613719	...	0.096509	0.096509	0.261160	0.261160	0.451359	1.313299	1.987599	2.593635	3.173890	3.696247
2	0.0	0.0	0.0	1.0	-17.428760	7.940863	0.170422	0.002326	-0.000244	-0.152038	...	0.223718	0.261160	0.356468	0.545824	1.821690	1.438857	2.291659	3.140440	3.819994	4.207710
3	0.0	0.0	0.0	1.0	-18.154841	5.568890	0.135705	0.001051	0.000688	-0.196623	...	0.399949	0.705356	1.127853	1.742820	3.274497	1.683010	2.766048	3.748502	4.303872	4.449241
4	1.0	0.0	0.0	1.0	395.985445	11192.658970	6.583700	0.099344	0.000000	8.608970	...	0.165443	0.165443	0.165443	0.165443	0.706253	1.483926	2.279149	3.014130	3.525453	3.919983

5 rows × 4638 columns

Using tsfresh for forecasting

You can also use tsfresh to do univariate forecasting. To find out more about forecasting, check out our forecasting tutorial notebook.

from sklearn.ensemble import RandomForestRegressor

from aeon.datasets import load_airline
from aeon.forecasting.base import ForecastingHorizon
from aeon.forecasting.compose import make_reduction
from aeon.forecasting.model_selection import temporal_train_test_split

y = load_airline()
y_train, y_test = temporal_train_test_split(y)

regressor = make_pipeline(
    TSFreshFeatureExtractor(show_warnings=False, disable_progressbar=True),
    RandomForestRegressor(),
)
forecaster = make_reduction(
    regressor, scitype="time-series-regressor", window_length=12
)
forecaster.fit(y_train)

fh = ForecastingHorizon(y_test.index, is_relative=False)
y_pred = forecaster.predict(fh)

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