v0.14.1

IssueID #3918: v0.14.1

- Readded baseline unit tests
- Readded large_number_of_peaks removed in v0.9.0
- Readded mean_autocorrelation removed in v0.9.0
- Reverted to original augmented_dickey_fuller that was changed in v0.9.0
- Reverted to original fft_coefficient that was changed in v0.9.0
- Readded mean_abs_change_quantiles that was removed in v0.9.0
- Readded the original time_reversal_asymmetry_statistic that was in use pre
  v0.9.0 - blue-yonder#198
- Readded original autocorrelation that was removed in v0.9.0
- Disabled partial_autocorrelation added in v0.10.0
- Disabled cid_ce added in v0.11.1
- Disabled fft_aggregated added in v0.11.0
- Disabled Fix agg change made to agg_autocorrelation added in v0.11.1
blue-yonder@a53fb6a
- Changed to new value_count and range_count method added in v0.11.1
- Configured settings for pre v0.9.0 features
- Hardcoded TSFRESH_BASELINE_VERSION = '0.9.1' in tests
- Disabled linear_trend_timewise added in v0.12.0
- Readded tsfresh/examples/test_tsfresh_baseline_dataset.py which was removed
  in v0.12.0
- Use v0.11.01 value_count and range_count method not as per v0.13.0
- Configured settings for pre v0.9.0 features
- Hardcoded TSFRESH_BASELINE_VERSION = '0.14.1' in tests

Added:
tests/baseline/tsfresh-0.1.2.py2.data.json.features.transposed.csv
tests/baseline/tsfresh-0.3.0.py2.data.json.features.transposed.csv
tests/baseline/tsfresh-0.3.0.py3.data.json.features.transposed.csv
tests/baseline/tsfresh-0.3.1.py2.data.json.features.transposed.csv
tests/baseline/tsfresh-0.3.1.py3.data.json.features.transposed.csv
tests/baseline/tsfresh-0.4.0.py2.data.json.features.transposed.csv
tests/baseline/tsfresh-0.4.0.py3.data.json.features.transposed.csv
tests/baseline/tsfresh-0.5.0.py2.data.json.features.transposed.csv
tests/baseline/tsfresh-0.5.0.py3.data.json.features.transposed.csv
tests/baseline/tsfresh-0.5.1.py3.data.json.features.transposed.csv
tests/baseline/tsfresh-0.6.0.py2.data.json.features.transposed.csv
tests/baseline/tsfresh-0.6.0.py3.data.json.features.transposed.csv
tests/baseline/tsfresh-0.6.1.py3.data.json.features.transposed.csv
tests/baseline/tsfresh-0.7.2.py3.data.json.features.transposed.csv
tests/baseline/tsfresh-0.8.2.py3.data.json.features.transposed.csv
tests/baseline/tsfresh-0.9.1.py3.data.json.features.transposed.csv
tests/baseline/tsfresh-0.10.2.py3.data.json.features.transposed.csv
tests/baseline/tsfresh-0.11.3.py3.data.json.features.transposed.csv
tests/baseline/tsfresh-0.12.1.py3.data.json.features.transposed.csv
tests/baseline/tsfresh-0.13.1.py3.data.json.features.transposed.csv
tests/baseline/tsfresh-0.14.1.py3.data.json.features.transposed.csv
tests/baseline/tsfresh_features_test.py
Modified:
CHANGES.rst
README.md
tsfresh/feature_extraction/feature_calculators.py
tsfresh/feature_extraction/settings.py

CHANGES.rst

@@ -4,6 +4,11 @@ Changelog
 
 tsfresh uses `Semantic Versioning <http://semver.org/>`_
 
+Version 0.14.1
+==============
+
+- Merged 0.14.0 changes
+
 
 Version 0.14.0
 ==============
@@ -25,6 +30,10 @@ Version 0.14.0
     - Replace Benjamini-Hochberg implementation with statsmodels implementation (#570)
     -  Fix the kernel and the naming of a notebook (#626)
 
+Version 0.13.1
+==============
+
+- Reverted to the v0.11.1 value_count setting
 
 Version 0.13.0
 ==============
@@ -52,6 +61,11 @@ Version 0.13.0
   - Improve performance with Numpy's sum function (#567)
   - Optimize mean_change (fixes issue #542) and correct documentation (#574)
 
+Version 0.12.1
+==============
+
+- Disabled linear_trend_timewise added in v0.12.0
+- Readded tsfresh/examples/test_tsfresh_baseline_dataset.py which was removed in v0.12.0
 
 Version 0.12.0
 ==============
@@ -67,6 +81,12 @@ Version 0.12.0
 - enable the RelevantFeatureAugmenter to be used in cross validated pipelines
 - increased scipy dependency to 1.2.0
 
+Version 0.11.3
+==============
+- Disabled new feature calculators:
+    - fft_aggregated
+    - cid_ce
+- reverted to the original mean_second_derivate_central from mean_second_derivative_central
 
 Version 0.11.2
 ==============
@@ -85,7 +105,7 @@ Version 0.11.1
 ==============
 - general performance improvements
 - removed hard pinning of dependencies
-- fixed bugs
+- fixed
     - the stock price forecasting notebook
     - the multi classification notebook
 
@@ -99,6 +119,11 @@ Version 0.11.0
 - add columns_to_ignore parameter to from_columns method
 - add distribution module, contains support for distributed feature extraction on Dask
 
+Version 0.10.2
+==============
+- Disabled new feature calculators:
+    - partial autocorrelation
+
 Version 0.10.1
 ==============
 - split test suite into unit and integration tests
@@ -118,6 +143,26 @@ Version 0.10.0
 - fixed the following bugs
     - improperly quotation of dickey fuller settings
 
+Version 0.9.1
+=============
+- Disabled new feature calculators:
+    - ratio_beyond_r_sigma
+    - energy_ratio_by_chunks
+    - number_crossing_m
+    - c3
+    - angle & abs for fft coefficients
+    - agg_autocorrelation
+    - p-Value and usedLag for augmented_dickey_fuller
+    - change_quantiles
+- reverted the calculation of the following features:
+    - fft_coefficients
+    - autocorrelation
+    - time_reversal_asymmetry_statistic
+- readded the following feature calculators:
+    - large_number_of_peak
+    - mean_autocorrelation
+    - mean_abs_change_quantiles
+
 Version 0.9.0
 =============
 - new feature calculators:
@@ -172,6 +217,11 @@ Version 0.8.0
 - added chapter in docs about the new API
 - adjusted old notebooks and documentation to new API
 
+Version 0.7.2
+=============
+
+- readded baseline unit tests
+
 Version 0.7.1
 =============
 
@@ -198,6 +248,12 @@ Version 0.7.0
     - an index with same name as id_column was breaking parallelization
     - friedrich_coefficients and max_langevin_fixed_point were occasionally stalling
 
+Version 0.6.1
+=============
+
+- Disabled new feature: estimation of largest fixed point of deterministic dynamics
+- Disabled _esitmate_friedrich_coefficients, friedrich_coefficients and max_langevin_fixed_point
+
 Version 0.6.0
 =============
 
@@ -207,6 +263,11 @@ Version 0.6.0
 - remove no logging handler warning
 - fixed bug in the RelevantFeatureAugmenter regarding the evaluate_only_added_features parameters
 
+Version 0.5.1
+=============
+
+- Disabled new feature and feature renaming: sum_of_recurring_values, sum_of_recurring_data_points
+
 Version 0.5.0
 =============
 

README.md

@@ -8,6 +8,22 @@
 [![Downloads](https://pepy.tech/badge/tsfresh)](https://pepy.tech/project/tsfresh)
 
 
+# MODIFIED FORK
+
+This repository contains a *MODIFIED FORK* of the tsfresh python package for use with Skyline.
+This modified fork maintains the features extracted at v0.4.0 but moves this tsfresh version forward in line with blue-yonder/tsfresh in terms of tsfresh internals and dependencies, etc.
+
+- New features added to blue-yonder/tsfresh are disabled in this version
+- Original methods for features are maintained even if they are changed in blue-yonder/tsfresh
+
+*NOTE*: these branches/versions are tested against the tests/baseline/tsfresh_features_test.py, which was
+removed from blue-yonder/tsfresh in v0.7.0 but has been readded to this fork. These branches/versions are
+only tested via the Skyline build tests, they *are not* tested against the tsfresh tests.  Seeing as this
+fork follows the blue-yonder/tsfresh versions and retrospectively makes backwards compatible changes to the
+settings and feature_calculators.py which work with the Skyline tests.  Therefore these changes are not
+currently backported to the tsfresh tests themselves and the tsfresh tests will fail if run against any
+of theses branches.
+
 # tsfresh
 
 This repository contains the *TSFRESH* python package. The abbreviation stands for

tests/baseline/tsfresh-0.1.2.py2.data.json.features.transposed.csv

@@ -0,0 +1,207 @@
+,tsfresh_features_test
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+value__large_number_of_peaks__n_3,1.0
+value__large_number_of_peaks__n_5,0.0
+value__last_location_of_minimum,0.0333333333333
+value__mean_abs_change_quantiles__qh_0.4__ql_0.0,35.6
+value__mean_abs_change_quantiles__qh_0.4__ql_0.2,12.0
+value__minimum,9856.0
+value__mean_abs_change_quantiles__qh_0.4__ql_0.6,0.0
+value__mean_abs_change_quantiles__qh_0.4__ql_0.8,0.0
+value__maximum,10123.0
+value__value_count__value_-inf,0.0
+value__number_peaks__n_1,21.0
+value__number_peaks__n_3,9.0
+value__longest_strike_above_mean,10.0
+value__number_peaks__n_5,4.0
+value__first_location_of_minimum,0.0166666666667
+value__large_standard_deviation__r_0.25,0.0
+value__large_number_of_peaks__n_1,1.0
+value__count_above_mean,29.0
+value__symmetry_looking__r_0.75,1.0
+value__mean_abs_change,46.6949152542
+value__mean_change,2.38983050847
+value__value_count__value_1,0.0
+value__value_count__value_0,0.0
+value__mean_abs_change_quantiles__qh_0.4__ql_0.4,0.0
+value__mean_autocorrelation,1.1720475294
+value__autocorrelation__lag_5,0.464639525765
+value__median,9981.0
+value__symmetry_looking__r_0.85,1.0
+value__mean_abs_change_quantiles__qh_0.8__ql_0.4,20.0
+value__symmetry_looking__r_0.05,1.0
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+value__value_count__value_inf,0.0
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+value__mean_abs_change_quantiles__qh_0.8__ql_0.2,31.619047619
+value__large_standard_deviation__r_0.45,0.0
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+value__autocorrelation__lag_0,1.0
+value__autocorrelation__lag_1,0.51547994425
+value__autocorrelation__lag_8,0.360082254297
+value__autocorrelation__lag_9,0.217484000968
+value__variance_larger_than_standard_deviation,1.0
+value__variance,3196.67638889
+value__mean,9986.08333333
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+value__mean_second_derivate_central,0.706896551724
+value__skewness,-0.135422921254
+value__length,60.0
+value__first_location_of_maximum,0.95
+value__mean_abs_change_quantiles__qh_1.0__ql_0.2,42.4634146341
+value__mean_abs_change_quantiles__qh_1.0__ql_0.4,45.0740740741
+value__mean_abs_change_quantiles__qh_1.0__ql_0.6,41.125
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+value__kurtosis,-0.0289015217515
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+value__time_reversal_asymmetry_statistic__lag_3,1993963105.11
+value__abs_energy,5983503421.0
+value__time_reversal_asymmetry_statistic__lag_2,1215832201.59
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+value__large_standard_deviation__r_0.3,0.0
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+value__large_standard_deviation__r_0.1,1.0
+value__large_standard_deviation__r_0.0,1.0
+value__large_standard_deviation__r_0.4,0.0
+value__large_standard_deviation__r_0.15,1.0
+value__standard_deviation,56.5391580136
+value__binned_entropy__max_bins_10,2.11974362674
+value__large_standard_deviation__r_0.35,0.0
+value__symmetry_looking__r_0.95,1.0
+value__longest_strike_below_mean,9.0
+value__sum_values,599165.0
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+value__symmetry_looking__r_0.6,1.0
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