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IssueID #3924: v0.17.9 - Readded baseline unit tests - Revert to the original sum_of_reoccurring_values v0.4.0 method which was changed and the new feature called sum_of_reoccurring_data_points was added which results in the same value as the original v0.4.0 sum_of_reoccurring_values method. The new sum_of_reoccurring_values method introduced results in different results as per: NOT in baseline :: [['value__sum_of_reoccurring_values', '49922.0']] NOT in calculated :: [['value__sum_of_reoccurring_values', '109822.0']] - Disable estimate_friedrich_coefficients feature added in v0.6.0 - Disable friedrich_coefficients feature added in v0.6.0 - Disabled max_langevin_fixed_point added in v0.6.0 - Disabled friedrich_coefficients and max_langevin_fixed_point in settings added in v0.6.0 - Updated very minor precision changes in the following features which changed in v0.6.0 value__autocorrelation__lag_6 old: 0.5124801685138611, new: 0.5124801685138614, diff: -0.00000000000000022204 value__autocorrelation__lag_8 old: 0.3600822542968588, new: 0.3600822542968586, diff: 0.00000000000000022204 value__autocorrelation__lag_5 old: 0.46463952576506423, new: 0.46463952576506445, diff: -0.00000000000000022204 value__autocorrelation__lag_1 old: 0.5154799442499527, new: 0.5154799442499526, diff: 0.00000000000000011102 value__autocorrelation__lag_7 old: 0.6538534951469427, new: 0.6538534951469428, diff: -0.00000000000000011102 value__autocorrelation__lag_2 old: 0.36765813197781533, new: 0.36765813197781516, diff: 0.00000000000000016653 value__autocorrelation__lag_9 old: 0.21748400096837436, new: 0.21748400096837414, diff: 0.00000000000000022204 value__augmented_dickey_fuller old: -0.8041220342033505, new: -0.8041220342033477, diff: -0.00000000000000277556 value__mean_autocorrelation old: 1.1720475293977406, new: 1.1720475293977404, diff: 0.00000000000000022204 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_2" old: -40.265846960764975, new: -40.26584696076512, diff: 0.00000000000014210855 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_2" old: 5485.741180131765, new: 5485.741180131762, diff: 0.00000000000272848411 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_2" old: 7535.022844459651, new: 7535.02284445965, diff: 0.00000000000181898940 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_2" old: 6017.192007927548, new: 6017.192007927546, diff: 0.00000000000181898940 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_2" old: 3308.4304014332156, new: 3308.4304014332133, diff: 0.00000000000227373675 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_2" old: 1295.7433671924819, new: 1295.7433671924832, diff: -0.00000000000136424205 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_2" old: 39.916767258584514, new: 39.91676725858371, diff: 0.00000000000080291329 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_2" old: 17.955485691823014, new: 17.95548569182395, diff: -0.00000000000093436370 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_2" old: 50.259030087877306, new: 50.25903008787768, diff: -0.00000000000037658765 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_2" old: 35.90470247450105, new: 35.90470247450137, diff: -0.00000000000031974423 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_2" old: -24.14602386100944, new: -24.14602386100941, diff: -0.00000000000002842171 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_2" old: -61.88712524130847, new: -61.88712524130824, diff: -0.00000000000022737368 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_2" old: -33.668504325219715, new: -33.66850432521918, diff: -0.00000000000053290705 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_2" old: 24.20883821024688, new: 24.2088382102474, diff: -0.00000000000051869620 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_5" old: -20.257597134272146, new: -20.25759713427192, diff: -0.00000000000022737368 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_5" old: 3771.325441515319, new: 3771.32544151532, diff: -0.00000000000090949470 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_5" old: 7120.960920890311, new: 7120.960920890312, diff: -0.00000000000090949470 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_5" old: 11207.92940647991, new: 11207.929406479912, diff: -0.00000000000181898940 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_5" old: 11696.157551031656, new: 11696.157551031654, diff: 0.00000000000181898940 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_5" old: 11253.943680982826, new: 11253.943680982822, diff: 0.00000000000363797881 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_5" old: 10110.89944351567, new: 10110.899443515671, diff: -0.00000000000181898940 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_5" old: 8545.47382821769, new: 8545.473828217693, diff: -0.00000000000363797881 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_5" old: 6826.238621617836, new: 6826.238621617837, diff: -0.00000000000181898940 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_5" old: 5169.353887616803, new: 5169.353887616802, diff: 0.00000000000090949470 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_5" old: 3717.969303101324, new: 3717.9693031013257, diff: -0.00000000000181898940 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_5" old: 2542.0196875693546, new: 2542.019687569354, diff: 0.00000000000045474735 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_5" old: 1652.101855511854, new: 1652.1018555118546, diff: -0.00000000000068212103 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_5" old: 1019.5707851504084, new: 1019.5707851504081, diff: 0.00000000000022737368 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_10" old: 836.6419785398183, new: 836.6419785398173, diff: 0.00000000000102318154 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_10" old: 3543.0796763032777, new: 3543.079676303278, diff: -0.00000000000045474735 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_10" old: 8634.724847532967, new: 8634.724847532969, diff: -0.00000000000181898940 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_10" old: 10876.523736377072, new: 10876.52373637707, diff: 0.00000000000181898940 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_10" old: 12835.398940237148, new: 12835.39894023715, diff: -0.00000000000181898940 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_10" old: 14466.10948981898, new: 14466.109489818979, diff: 0.00000000000181898940 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_10" old: 15737.72244365614, new: 15737.722443656134, diff: 0.00000000000545696821 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_10" old: 17169.076640994837, new: 17169.07664099483, diff: 0.00000000000727595761 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_10" old: 17183.302683017104, new: 17183.302683017107, diff: -0.00000000000363797881 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_10" old: 15154.905872253841, new: 15154.905872253847, diff: -0.00000000000545696821 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_20" old: 18718.957258866503, new: 18718.957258866507, diff: -0.00000000000363797881 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_20" old: 20645.63503140842, new: 20645.635031408423, diff: -0.00000000000363797881 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_20" old: 28065.04062099347, new: 28065.040620993466, diff: 0.00000000000363797881 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_20" old: 31428.519814904776, new: 31428.519814904783, diff: -0.00000000000727595761 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_20" old: 32985.81511950059, new: 32985.8151195006, diff: -0.00000000000727595761 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_20" old: 34437.5408408601, new: 34437.54084086011, diff: -0.00000000000727595761 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_20" old: 35770.92323199827, new: 35770.923231998284, diff: -0.00000000001455191523 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_20" old: 36992.814788488264, new: 36992.81478848827, diff: -0.00000000000727595761 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_20" old: 38098.193912726434, new: 38098.19391272645, diff: -0.00000000001455191523 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_20" old: 39076.9898057395, new: 39076.98980573952, diff: -0.00000000002182787284 "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_20" old: 39919.05725014527, new: 39919.05725014526, diff: 0.00000000000727595761 value__spkt_welch_density__coeff_2 old: 1843.821171807498, new: 1843.8211718074986, diff: -0.00000000000045474735 value__spkt_welch_density__coeff_8 old: 2536.9954700088933, new: 2536.9954700088906, diff: 0.00000000000272848411 value__ar_coefficient__k_10__coeff_0 old: 904.439185079118, new: 904.4391850794491, diff: -0.00000000033105607145 value__ar_coefficient__k_10__coeff_1 old: 0.16357894811580564, new: 0.1635789481157781, diff: 0.00000000000002753353 value__ar_coefficient__k_10__coeff_2 old: -0.04324700014744565, new: -0.0432470001474492, diff: 0.00000000000000355271 value__ar_coefficient__k_10__coeff_3 old: -0.06654237068303814, new: -0.06654237068301239, diff: -0.00000000000002575717 value__ar_coefficient__k_10__coeff_4 old: 0.2836853193919353, new: 0.2836853193919273, diff: 0.00000000000000799361 value__fft_coefficient__coeff_1 old: -0.8045103874789135, new: -0.8045103874789561, diff: 0.00000000000004263256 value__fft_coefficient__coeff_2 old: -53.13286168327596, new: -53.13286168327602, diff: 0.00000000000005684342 value__fft_coefficient__coeff_3 old: -338.00000000000006, new: -338.0, diff: -0.00000000000005684342 value__fft_coefficient__coeff_4 old: 122.44503935479224, new: 122.44503935479203, diff: 0.00000000000021316282 value__fft_coefficient__coeff_5 old: -58.930796134231116, new: -58.930796134230846, diff: -0.00000000000027000624 value__fft_coefficient__coeff_6 old: 13.000000000000057, new: 13.0, diff: 0.00000000000005684342 value__fft_coefficient__coeff_7 old: 112.23530652170982, new: 112.23530652170984, diff: -0.00000000000002842171 value__fft_coefficient__coeff_8 old: 118.18782232848393, new: 118.18782232848395, diff: -0.00000000000001421085 - Readded baseline unit tests removed in v0.7.0 - 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 - 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 - Disabled count_above and count_below features that were added in v0.15.0 - Readded the original percentage_of_reoccurring_datapoints_to_all_datapoints before the feature name change to percentage_of_reoccurring_values_to_all_values implemented in v0.17.0 (feature names should be immutable) blue-yonder#725 blue-yonder@6f9c795 blue-yonder#724 - Rename the new feature percentage_of_reoccurring_values_to_all_values to v0170_percentage_of_reoccurring_values_to_all_values and disabled - Readded the original percentage_of_reoccurring_values_to_all_values before the feature name change to percentage_of_reoccurring_datapoints_to_all_datapoints implemented in v0.17.0 (feature names should be immutable) - Rename the new feature percentage_of_reoccurring_datapoints_to_all_datapoints to v0170_percentage_of_reoccurring_datapoints_to_all_datapoints and disabled - Disabled lempel_ziv_complexity,fourier_entropy and permutation_entropy features that were added in v0.17.0 - Revert to the original cwt_coefficients feature names changed in v0.16.0 - Renamed the new sample_entropy introduced in v0.16.0 to v0160_sample_entropy and readded sample_entropy from v0.15.1 as this is a breaking change as per: blue-yonder#681 and blue-yonder@ce493e5 - Configured settings for pre v0.9.0 features - Hardcoded TSFRESH_BASELINE_VERSION = '0.17.9' 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-0.15.2.py3.data.json.features.transposed.csv tests/baseline/tsfresh-0.16.1.py3.data.json.features.transposed.csv tests/baseline/tsfresh-0.17.9.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
IssueID #4456: v0.19.1 - Adding new version based on blue-yonder/tsfresh-v0.20.0 - Added new baseline - Hardcoded TSFRESH_BASELINE_VERSION = '0.19.1' in tests Added: tests/baseline/tsfresh-0.19.1.py3.data.json.features.transposed.csv Modified: tsfresh/examples/test_tsfresh_baseline_dataset.py tests/baseline/tsfresh_features_test.py tsfresh/feature_extraction/feature_calculators.py tsfresh/feature_extraction/settings.py CHANGES.rst README.md requirements.txt
IssueID #5534: v0.20.3-skyline - Adding new version based on blue-yonder/tsfresh-v0.20.3 - Added new baseline - Hardcoded TSFRESH_BASELINE_VERSION = '0.20.3-skyline' in tests - Updated which changes for numpy >= 2 - Maintain skewness method do not use skipna=False which was introduced in 0.20.3 Added: tests/baseline/tsfresh-0.20.3-skyline.py3.data.json.features.transposed.csv tests/baseline/tsfresh-0.20.3.py3.data.json.features.transposed.csv Modified: tests/baseline/tsfresh_features_test.py tsfresh/feature_extraction/feature_calculators.py tsfresh/feature_extraction/settings.py CHANGES.rst
IssueID #5630: v0.21.9 - Adding new version based on blue-yonder/tsfresh-v0.21.0 - Added new baseline - Hardcoded TSFRESH_BASELINE_VERSION = '0.21.9' in tests Added: tests/baseline/last_run.0.21.9.py3.data.json.features.transposed.csv tests/baseline/tsfresh-0.21.9.py3.data.json.features.transposed.csv Modified: .gitignore AUTHORS.rst CHANGES.rst README.md docs/text/how_to_contribute.rst setup.cfg tests/baseline/tsfresh_features_test.py tsfresh/feature_extraction/feature_calculators.py
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