This PipeOp extracts the 22 (or 24) canonical time series characteristics (catch22) from functional columns.
For more details, see Rcatch22::catch22_all(), which is called internally on each curve.
The catch22 set is a low-redundancy subset of the hctsa features, selected for their performance across a diverse collection of time series classification tasks, but applicable as general-purpose features for other tasks such as regression.
For other time series feature extractors, see PipeOpFDATsfeatures.
Parameters
The parameters are the parameters inherited from PipeOpTaskPreprocSimple,
as well as the following parameters:
catch24::logical(1)
IfTRUE, additionally compute the mean and standard deviation (the catch24 set), yielding 24 features instead of 22. Default isFALSE.
Naming
The new names generally append a _{feature} to the corresponding column name.
If a column was called "x" and the feature is "DN_HistogramMode_5", the corresponding new column will
be called "x_DN_HistogramMode_5".
Super classes
mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpTaskPreproc -> mlr3pipelines::PipeOpTaskPreprocSimple -> PipeOpFDACatch22
Methods
PipeOpFDACatch22$new()
Initializes a new instance of this Class.
Usage
PipeOpFDACatch22$new(id = "fda.catch22", param_vals = list())Examples
task = tsk("fuel")
po_catch22 = po("fda.catch22")
task_catch22 = po_catch22$train(list(task))[[1L]]
task_catch22$data()
#> heatan h2o NIR_DN_HistogramMode_5 NIR_DN_HistogramMode_10
#> <num> <num> <num> <num>
#> 1: 26.7810 2.3000 0.65903335 0.89906525
#> 2: 27.4720 3.0000 0.71703535 0.94042738
#> 3: 23.8400 2.0002 0.02528328 0.46411508
#> 4: 18.1680 1.8500 0.69290569 0.19460477
#> 5: 17.5170 2.3898 0.80525107 0.46384266
#> ---
#> 125: 23.8340 2.1100 0.85636136 -0.08530372
#> 126: 11.8050 1.6200 0.27729299 1.03383987
#> 127: 8.8315 1.4200 0.40673933 1.14766493
#> 128: 11.3450 1.4800 0.33106873 1.05249299
#> 129: 28.9940 2.5000 0.07346713 0.36660400
#> NIR_CO_f1ecac NIR_CO_FirstMin_ac NIR_CO_HistogramAMI_even_2_5
#> <num> <num> <num>
#> 1: 28.908320 5 0.7427983
#> 2: 43.589840 4 0.9172264
#> 3: 1.881192 1 0.3994337
#> 4: 1.995276 1 0.1158634
#> 5: 41.162216 13 0.7458997
#> ---
#> 125: 42.553328 4 0.6736657
#> 126: 50.007951 164 1.0588572
#> 127: 50.005547 161 1.2120772
#> 128: 42.865748 128 0.9765346
#> 129: 45.134924 1 0.5444544
#> NIR_CO_trev_1_num NIR_MD_hrv_classic_pnn40
#> <num> <num>
#> 1: 0.029297693 0.5826087
#> 2: -0.018612421 0.5217391
#> 3: -0.751222239 0.8000000
#> 4: 1.200472070 0.8565217
#> 5: 0.024270760 0.4695652
#> ---
#> 125: 0.027756341 0.6521739
#> 126: 0.009860880 0.3608696
#> 127: 0.009078954 0.2869565
#> 128: 0.053411233 0.5217391
#> 129: -0.042982742 0.7043478
#> NIR_SB_BinaryStats_mean_longstretch1
#> <num>
#> 1: 133
#> 2: 134
#> 3: 88
#> 4: 85
#> 5: 138
#> ---
#> 125: 117
#> 126: 117
#> 127: 117
#> 128: 128
#> 129: 103
#> NIR_SB_TransitionMatrix_3ac_sumdiagcov NIR_PD_PeriodicityWang_th0_01
#> <num> <num>
#> 1: 0.06250000 5
#> 2: 0.16666667 10
#> 3: 0.07407407 3
#> 4: 0.06250000 4
#> 5: 0.16666667 6
#> ---
#> 125: 0.16666667 4
#> 126: 0.16666667 0
#> 127: 0.16666667 0
#> 128: 0.07407407 0
#> 129: 0.11111111 8
#> NIR_CO_Embed2_Dist_tau_d_expfit_meandiff
#> <num>
#> 1: 0.42884280
#> 2: 0.40813635
#> 3: 0.03526442
#> 4: 0.06976777
#> 5: 0.55334737
#> ---
#> 125: 0.33277897
#> 126: 0.76350789
#> 127: 0.69434863
#> 128: 0.37350400
#> 129: 0.19036719
#> NIR_IN_AutoMutualInfoStats_40_gaussian_fmmi
#> <num>
#> 1: 1
#> 2: 3
#> 3: 2
#> 4: 3
#> 5: 4
#> ---
#> 125: 1
#> 126: 2
#> 127: 4
#> 128: 1
#> 129: 4
#> NIR_FC_LocalSimple_mean1_tauresrat NIR_DN_OutlierInclude_p_001_mdrmd
#> <num> <num>
#> 1: 0.01923077 0.06493506
#> 2: 0.01234568 0.57142857
#> 3: 0.01470588 -0.61904762
#> 4: 0.02127660 0.20346320
#> 5: 0.01204819 0.59740260
#> ---
#> 125: 0.01176471 0.63636364
#> 126: 0.01162791 0.74025974
#> 127: 0.01162791 0.74458874
#> 128: 0.01449275 0.34199134
#> 129: 0.01315789 -0.70562771
#> NIR_DN_OutlierInclude_n_001_mdrmd NIR_SP_Summaries_welch_rect_area_5_1
#> <num> <num>
#> 1: -0.8181818 0.8985138
#> 2: -0.8701299 0.9509898
#> 3: 0.6103896 0.5333623
#> 4: -0.9047619 0.4864089
#> 5: -0.8528139 0.9132662
#> ---
#> 125: -0.8441558 0.8764749
#> 126: -0.8008658 0.9583462
#> 127: -0.7619048 0.9594831
#> 128: -0.8441558 0.9497204
#> 129: 0.7424242 0.8161740
#> NIR_SB_BinaryStats_diff_longstretch0 NIR_SB_MotifThree_quantile_hh
#> <num> <num>
#> 1: 29 1.315276
#> 2: 4 1.394753
#> 3: 8 1.828160
#> 4: 6 2.010899
#> 5: 4 1.185274
#> ---
#> 125: 5 1.373250
#> 126: 4 1.185274
#> 127: 4 1.185274
#> 128: 10 1.338366
#> 129: 5 1.681533
#> NIR_SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1
#> <num>
#> 1: 0.4883721
#> 2: 0.3023256
#> 3: 0.5348837
#> 4: 0.5813953
#> 5: 0.5813953
#> ---
#> 125: 0.6744186
#> 126: 0.4418605
#> 127: 0.4418605
#> 128: 0.2558140
#> 129: 0.4418605
#> NIR_SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1
#> <num>
#> 1: 0.8604651
#> 2: 0.3488372
#> 3: 0.8139535
#> 4: 0.1860465
#> 5: 0.1395349
#> ---
#> 125: 0.4186047
#> 126: 0.3953488
#> 127: 0.1395349
#> 128: 0.7674419
#> 129: 0.7674419
#> NIR_SP_Summaries_welch_rect_centroid NIR_FC_LocalSimple_mean3_stderr
#> <num> <num>
#> 1: 0.02454369 0.2574234
#> 2: 0.02454369 0.2316203
#> 3: 0.39269908 0.8196781
#> 4: 0.61359232 0.7764270
#> 5: 0.02454369 0.2193613
#> ---
#> 125: 0.02454369 0.4340053
#> 126: 0.02454369 0.1365554
#> 127: 0.02454369 0.1197464
#> 128: 0.02454369 0.2470460
#> 129: 0.02454369 0.5195790
#> UVVIS_DN_HistogramMode_5 UVVIS_DN_HistogramMode_10 UVVIS_CO_f1ecac
#> <num> <num> <num>
#> 1: -0.76548678 -0.9871648 28.544177
#> 2: -0.12166515 -0.3494136 28.338698
#> 3: -0.34608226 -0.1002904 19.841067
#> 4: 0.05213751 -0.5373281 21.263534
#> 5: -0.20955514 -0.4947158 25.893403
#> ---
#> 125: -0.02941803 -0.3306574 2.835635
#> 126: -0.93299051 -0.6707779 11.860308
#> 127: -0.91466333 -1.1307723 19.247700
#> 128: -1.09097234 -1.2774379 20.569486
#> 129: 0.58989186 0.9084871 27.669540
#> UVVIS_CO_FirstMin_ac UVVIS_CO_HistogramAMI_even_2_5 UVVIS_CO_trev_1_num
#> <num> <num> <num>
#> 1: 2 0.8127115 0.005250231
#> 2: 5 0.6000956 -0.182501427
#> 3: 1 0.4424466 -0.169553664
#> 4: 1 0.3072650 -0.481512206
#> 5: 1 0.4781087 -0.053878703
#> ---
#> 125: 2 0.1493133 0.081184787
#> 126: 1 0.3779441 0.110193043
#> 127: 1 0.5517971 -0.046863292
#> 128: 51 0.7275115 -0.096419953
#> 129: 3 0.4685611 -0.963771103
#> UVVIS_MD_hrv_classic_pnn40 UVVIS_SB_BinaryStats_mean_longstretch1
#> <num> <num>
#> 1: 0.7819549 53
#> 2: 0.8345865 59
#> 3: 0.8571429 57
#> 4: 0.9022556 47
#> 5: 0.8571429 52
#> ---
#> 125: 0.9097744 29
#> 126: 0.9022556 26
#> 127: 0.8796992 18
#> 128: 0.8646617 40
#> 129: 0.8721805 62
#> UVVIS_SB_TransitionMatrix_3ac_sumdiagcov UVVIS_PD_PeriodicityWang_th0_01
#> <num> <num>
#> 1: 0.16666667 2
#> 2: 0.16666667 5
#> 3: 0.07407407 4
#> 4: 0.07407407 4
#> 5: 0.11111111 4
#> ---
#> 125: 0.11111111 5
#> 126: 0.04166667 3
#> 127: 0.06250000 5
#> 128: 0.06250000 5
#> 129: 0.16666667 4
#> UVVIS_CO_Embed2_Dist_tau_d_expfit_meandiff
#> <num>
#> 1: 0.47392492
#> 2: 0.19688323
#> 3: 0.13097509
#> 4: 0.09064423
#> 5: 0.14354603
#> ---
#> 125: 0.06411008
#> 126: 0.11065767
#> 127: 0.19322124
#> 128: 0.24498558
#> 129: 0.11421431
#> UVVIS_IN_AutoMutualInfoStats_40_gaussian_fmmi
#> <num>
#> 1: 1
#> 2: 2
#> 3: 2
#> 4: 3
#> 5: 3
#> ---
#> 125: 1
#> 126: 2
#> 127: 4
#> 128: 4
#> 129: 2
#> UVVIS_FC_LocalSimple_mean1_tauresrat UVVIS_DN_OutlierInclude_p_001_mdrmd
#> <num> <num>
#> 1: 0.02222222 0.80970149
#> 2: 0.02173913 0.76492537
#> 3: 0.02272727 0.65671642
#> 4: 0.02564103 0.76119403
#> 5: 0.02272727 0.75373134
#> ---
#> 125: 0.02439024 0.77611940
#> 126: 0.03448276 -0.01492537
#> 127: 0.03333333 0.58955224
#> 128: 0.03333333 0.81343284
#> 129: 0.02173913 0.71641791
#> UVVIS_DN_OutlierInclude_n_001_mdrmd UVVIS_SP_Summaries_welch_rect_area_5_1
#> <num> <num>
#> 1: -0.43656716 0.9217206
#> 2: -0.59701493 0.8562983
#> 3: -0.62686567 0.7654664
#> 4: -0.53731343 0.6951190
#> 5: -0.64179104 0.7898758
#> ---
#> 125: -0.68656716 0.5117292
#> 126: -0.20149254 0.6871688
#> 127: -0.10447761 0.8267342
#> 128: -0.08955224 0.8857187
#> 129: -0.64179104 0.7335173
#> UVVIS_SB_BinaryStats_diff_longstretch0 UVVIS_SB_MotifThree_quantile_hh
#> <num> <num>
#> 1: 4 1.565376
#> 2: 4 1.582308
#> 3: 5 1.914436
#> 4: 6 1.878493
#> 5: 4 1.574847
#> ---
#> 125: 5 1.888721
#> 126: 4 1.926484
#> 127: 4 1.756310
#> 128: 7 1.618036
#> 129: 4 1.607980
#> UVVIS_SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1
#> <num>
#> 1: 0.3589744
#> 2: 0.5128205
#> 3: 0.8461538
#> 4: 0.5128205
#> 5: 0.3076923
#> ---
#> 125: 0.8461538
#> 126: 0.6153846
#> 127: 0.5128205
#> 128: 0.6666667
#> 129: 0.8205128
#> UVVIS_SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1
#> <num>
#> 1: 0.6666667
#> 2: 0.6666667
#> 3: 0.6666667
#> 4: 0.8461538
#> 5: 0.6666667
#> ---
#> 125: 0.8205128
#> 126: 0.4358974
#> 127: 0.6410256
#> 128: 0.5384615
#> 129: 0.5897436
#> UVVIS_SP_Summaries_welch_rect_centroid UVVIS_FC_LocalSimple_mean3_stderr
#> <num> <num>
#> 1: 0.04908739 0.2896300
#> 2: 0.04908739 0.4476950
#> 3: 0.04908739 0.6284882
#> 4: 0.04908739 0.6596766
#> 5: 0.04908739 0.4611458
#> ---
#> 125: 0.46633016 0.8954584
#> 126: 0.07363108 0.6667376
#> 127: 0.04908739 0.4409287
#> 128: 0.04908739 0.4126393
#> 129: 0.04908739 0.5796865