tsfresh.examples package¶
Submodules¶
tsfresh.examples.driftbif_datasets module¶
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tsfresh.examples.driftbif_datasets.
load_driftbif
(n, l)[source]¶ Creates and loads the drift bifurcation dataset.
Parameters: Returns: X, y. Time series container and target vector
Rtype X: pandas.DataFrame
Rtype y: pandas.DataFrame
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class
tsfresh.examples.driftbif_datasets.
velocity
(tau=2.87, kappa_3=0.3, Q=1950.0, R=0.0003, delta_t=0.005)[source]¶ Bases:
object
Simulates the velocity of one dissipative soliton (kind of self organized particle)
label 0 means tau<=1/0.3, Dissipative Soliton with Brownian motion (purely noise driven) label 1 means tau> 1/0.3, Dissipative Soliton with Active Brownian motion (intrinsiv velocity with overlaid noise)
References
[6] Andreas Kempa-Liehr (2013, p. 159-170) Dynamics of Dissipative Soliton Dissipative Solitons in Reaction Diffusion Systems. Springer: Berlin >>> ds = velocity(tau=3.5) # Dissipative soliton with equilibrium velocity 1.5e-3 >>> print(ds.label) # Discriminating before or beyond Drift-Bifurcation 1 >>> print(ds.deterministic) # Equilibrium velocity 0.0015191090506254991 >>> v = ds.simulate(20000) # Simulate velocity time series with 20000 time steps being disturbed by Gaussian white noise
tsfresh.examples.har_dataset module¶
This module implements functions to download and load the Human Activity Recognition dataset [4]. A description of the data set can be found in [5].
References
[4] | http://mlr.cs.umass.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones |
[5] | Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. (2013) A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013. |
tsfresh.examples.robot_execution_failures module¶
This module implements functions to download the Robot Execution Failures LP1 Data Set[1] and load it as as DataFrame.
Important: You need to download the data set yourself, either manually or via the function
download_robot_execution_failures()
References
[1] | http://mlr.cs.umass.edu/ml/datasets/Robot+Execution+Failures |
[2] | Lichman, M. (2013). UCI Machine Learning Repository [http://mlr.cs.umass.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. |
[3] | Camarinha-Matos, L.M., L. Seabra Lopes, and J. Barata (1996). Integration and Learning in Supervision of Flexible Assembly Systems. “IEEE Transactions on Robotics and Automation”, 12 (2), 202-219 |
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tsfresh.examples.robot_execution_failures.
download_robot_execution_failures
()[source]¶ Download the Robot Execution Failures LP1 Data Set[1] from the UCI Machine Learning Repository[2] and store it locally. :return:
Examples
>>> from tsfresh.examples import download_robot_execution_failures >>> download_robot_execution_failures_lp1()
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tsfresh.examples.robot_execution_failures.
load_robot_execution_failures
()[source]¶ Load the Robot Execution Failures LP1 Data Set[1]. The Time series are passed as a flat DataFrame.
Examples
>>> from tsfresh.examples import load_robot_execution_failures >>> df, y = load_robot_execution_failures() >>> print(df.shape) (1320, 8)
Returns: time series data as pandas.DataFrame
and target vector aspandas.Series
Return type: tuple
tsfresh.examples.test_tsfresh_baseline_dataset module¶
This module implements a function to download a json timeseries data set that is utilised by tests/baseline/tsfresh_features_test.py to test calculated feature names and their calculated values are consistent with the known baseline.
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tsfresh.examples.test_tsfresh_baseline_dataset.
download_json_dataset
()[source]¶ Download the tests baseline timeseries json data set and store it at tsfresh/examples/data/test_tsfresh_baseline_dataset/data.json.
Examples
>>> from tsfresh.examples import test_tsfresh_baseline_dataset >>> download_json_dataset()
Module contents¶
Module with exemplary data sets to play around with.
See for eample the Quick Start section on how to use them.