Functions used to transform time series into a dataframe that can be used to create tabular dataloaders.
In this case we are using tsfresh that is one of the most widely known libraries used to create features from time series. You can get more details about this library here: https://tsfresh.readthedocs.io/en/latest/
get_ts_features[source]
get_ts_features(X:Union[ndarray,Tensor],y:Union[NoneType,ndarray,Tensor]=None,features:Union[str,dict]='min',n_jobs:Optional[int]=None)
Args: X: np.array or torch.Tesnor of shape [samples, dimensions, timesteps]. y: Not required for unlabeled data. Otherwise, you need to pass it. features: 'min', 'efficient', 'all', or a dictionary. Be aware that 'efficient' and 'all' may required substantial memory and time.
dsid = 'NATOPS'
X, y, splits = get_UCR_data(dsid, return_split=False)
X.shape
(360, 24, 51)
There are 3 levels of fatures you can extract: 'min', 'efficient' and 'all'. I'd encourage you to start with min as feature creation may take a long time.
In addition to this, you can pass a dictionary to build the desired features (see tsfresh documentation in the link above).
ts_features_df = get_ts_features(X, y)
ts_features_df.shape
Feature Extraction: 100%|██████████| 40/40 [00:03<00:00, 10.43it/s]
(360, 193)
The 'min' set creates a dataframe with 8 features per channel + 1 per target (total 193) for each time series sample (360).
cont_names = ts_features_df.columns[:-1]
y_names = 'target'
dls = get_tabular_dls(ts_features_df, splits=splits, cont_names=cont_names, y_names=y_names)
dls.show_batch()
| 0__sum_values | 0__median | 0__mean | 0__length | 0__standard_deviation | 0__variance | 0__maximum | 0__minimum | 1__sum_values | 1__median | 1__mean | 1__length | 1__standard_deviation | 1__variance | 1__maximum | 1__minimum | 2__sum_values | 2__median | 2__mean | 2__length | 2__standard_deviation | 2__variance | 2__maximum | 2__minimum | 3__sum_values | 3__median | 3__mean | 3__length | 3__standard_deviation | 3__variance | 3__maximum | 3__minimum | 4__sum_values | 4__median | 4__mean | 4__length | 4__standard_deviation | 4__variance | 4__maximum | 4__minimum | 5__sum_values | 5__median | 5__mean | 5__length | 5__standard_deviation | 5__variance | 5__maximum | 5__minimum | 6__sum_values | 6__median | 6__mean | 6__length | 6__standard_deviation | 6__variance | 6__maximum | 6__minimum | 7__sum_values | 7__median | 7__mean | 7__length | 7__standard_deviation | 7__variance | 7__maximum | 7__minimum | 8__sum_values | 8__median | 8__mean | 8__length | 8__standard_deviation | 8__variance | 8__maximum | 8__minimum | 9__sum_values | 9__median | 9__mean | 9__length | 9__standard_deviation | 9__variance | 9__maximum | 9__minimum | 10__sum_values | 10__median | 10__mean | 10__length | 10__standard_deviation | 10__variance | 10__maximum | 10__minimum | 11__sum_values | 11__median | 11__mean | 11__length | 11__standard_deviation | 11__variance | 11__maximum | 11__minimum | 12__sum_values | 12__median | 12__mean | 12__length | 12__standard_deviation | 12__variance | 12__maximum | 12__minimum | 13__sum_values | 13__median | 13__mean | 13__length | 13__standard_deviation | 13__variance | 13__maximum | 13__minimum | 14__sum_values | 14__median | 14__mean | 14__length | 14__standard_deviation | 14__variance | 14__maximum | 14__minimum | 15__sum_values | 15__median | 15__mean | 15__length | 15__standard_deviation | 15__variance | 15__maximum | 15__minimum | 16__sum_values | 16__median | 16__mean | 16__length | 16__standard_deviation | 16__variance | 16__maximum | 16__minimum | 17__sum_values | 17__median | 17__mean | 17__length | 17__standard_deviation | 17__variance | 17__maximum | 17__minimum | 18__sum_values | 18__median | 18__mean | 18__length | 18__standard_deviation | 18__variance | 18__maximum | 18__minimum | 19__sum_values | 19__median | 19__mean | 19__length | 19__standard_deviation | 19__variance | 19__maximum | 19__minimum | 20__sum_values | 20__median | 20__mean | 20__length | 20__standard_deviation | 20__variance | 20__maximum | 20__minimum | 21__sum_values | 21__median | 21__mean | 21__length | 21__standard_deviation | 21__variance | 21__maximum | 21__minimum | 22__sum_values | 22__median | 22__mean | 22__length | 22__standard_deviation | 22__variance | 22__maximum | 22__minimum | 23__sum_values | 23__median | 23__mean | 23__length | 23__standard_deviation | 23__variance | 23__maximum | 23__minimum | target | |
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| 0 | -31.687590 | -0.449933 | -0.621325 | 51.0 | 1.131348 | 1.279949 | 1.736834 | -2.283808 | -16.302590 | -0.006616 | -0.319659 | 51.0 | 1.013854 | 1.027900 | 0.882160 | -2.065694 | -61.100647 | -1.349777 | -1.198052 | 51.0 | 0.568826 | 0.323564 | -0.016052 | -1.992497 | 51.926018 | 0.842558 | 1.018157 | 51.0 | 0.951547 | 0.905441 | 2.367542 | -0.546820 | -42.782078 | -0.791868 | -0.838864 | 51.0 | 0.893807 | 0.798892 | 0.423345 | -2.377712 | 3.175629 | -0.184566 | 0.062267 | 51.0 | 0.989049 | 0.978218 | 1.566781 | -1.496043 | -41.555508 | -0.725470 | -0.814814 | 51.0 | 0.294545 | 0.086757 | -0.329041 | -1.266218 | -22.076424 | -0.679716 | -0.432871 | 51.0 | 0.444817 | 0.197862 | 0.220083 | -1.348709 | -34.127652 | -0.693618 | -0.669170 | 51.0 | 0.159478 | 0.025433 | -0.327424 | -1.065561 | 42.054775 | 0.859676 | 0.824603 | 51.0 | 0.210473 | 0.044299 | 1.589184 | 0.574062 | -36.303024 | -0.757318 | -0.711824 | 51.0 | 0.358840 | 0.128766 | -0.210639 | -1.655061 | 20.224211 | 0.330459 | 0.396553 | 51.0 | 0.265177 | 0.070319 | 0.765451 | -0.176420 | -33.866142 | -0.602986 | -0.664042 | 51.0 | 0.847634 | 0.718483 | 1.148847 | -1.766993 | -16.736234 | -0.200915 | -0.328161 | 51.0 | 0.758470 | 0.575276 | 0.647467 | -1.550873 | -53.966419 | -1.090789 | -1.058165 | 51.0 | 0.298422 | 0.089056 | -0.241720 | -1.626734 | 46.019440 | 0.752719 | 0.902342 | 51.0 | 0.637167 | 0.405982 | 1.743538 | -0.238214 | -39.030647 | -0.623139 | -0.765307 | 51.0 | 0.677121 | 0.458493 | 0.133827 | -1.924617 | 6.346338 | -0.071636 | 0.124438 | 51.0 | 0.767484 | 0.589032 | 1.147075 | -1.334729 | -34.825542 | -0.449683 | -0.682854 | 51.0 | 0.963484 | 0.928301 | 1.143906 | -2.074954 | -13.005811 | -0.033904 | -0.255016 | 51.0 | 0.915946 | 0.838957 | 0.793895 | -1.785950 | -58.590382 | -1.264853 | -1.148831 | 51.0 | 0.441024 | 0.194502 | 0.006054 | -1.804485 | 48.292000 | 0.808016 | 0.946902 | 51.0 | 0.858373 | 0.736804 | 2.098662 | -0.508780 | -36.319584 | -0.467445 | -0.712149 | 51.0 | 0.832555 | 0.693147 | 0.375481 | -2.291797 | 4.425817 | -0.131826 | 0.086781 | 51.0 | 0.853803 | 0.728979 | 1.382302 | -1.181882 | 5.0 |
| 1 | -24.011181 | -0.463308 | -0.470807 | 51.0 | 0.022327 | 0.000499 | -0.430550 | -0.519750 | -97.602141 | -1.932483 | -1.913768 | 51.0 | 0.065244 | 0.004257 | -1.744846 | -2.022548 | -40.876679 | -0.822114 | -0.801503 | 51.0 | 0.037470 | 0.001404 | -0.739014 | -0.852205 | 58.312725 | 0.890756 | 1.143387 | 51.0 | 0.625533 | 0.391292 | 2.287177 | 0.488941 | -24.820274 | -1.291028 | -0.486672 | 51.0 | 1.539181 | 2.369080 | 1.607593 | -2.030625 | -23.528324 | -0.470723 | -0.461340 | 51.0 | 0.340889 | 0.116205 | 0.025836 | -0.867550 | -29.485134 | -0.580218 | -0.578140 | 51.0 | 0.010866 | 0.000118 | -0.557524 | -0.598073 | -39.278442 | -0.783769 | -0.770166 | 51.0 | 0.037736 | 0.001424 | -0.710738 | -0.828738 | -9.966043 | -0.219775 | -0.195413 | 51.0 | 0.053768 | 0.002891 | -0.125779 | -0.268663 | 39.935242 | 0.751462 | 0.783044 | 51.0 | 0.217438 | 0.047279 | 1.164140 | 0.542353 | -12.488910 | -0.582633 | -0.244881 | 51.0 | 0.651232 | 0.424103 | 0.718757 | -0.883951 | -10.751954 | -0.203955 | -0.210823 | 51.0 | 0.109824 | 0.012061 | -0.033436 | -0.347595 | -27.786322 | -0.539891 | -0.544830 | 51.0 | 0.017510 | 0.000307 | -0.508702 | -0.577425 | -75.600090 | -1.486838 | -1.482355 | 51.0 | 0.063190 | 0.003993 | -1.327342 | -1.596648 | -30.532627 | -0.613594 | -0.598679 | 51.0 | 0.045950 | 0.002111 | -0.509880 | -0.664597 | 51.931954 | 0.871081 | 1.018274 | 51.0 | 0.424618 | 0.180301 | 1.827167 | 0.569429 | -18.751158 | -0.944382 | -0.367670 | 51.0 | 1.195926 | 1.430238 | 1.347136 | -1.549184 | -20.509649 | -0.400428 | -0.402150 | 51.0 | 0.233003 | 0.054291 | -0.103234 | -0.706466 | -30.697359 | -0.596296 | -0.601909 | 51.0 | 0.019827 | 0.000393 | -0.570799 | -0.636124 | -87.481331 | -1.717072 | -1.715320 | 51.0 | 0.065076 | 0.004235 | -1.576493 | -1.851188 | -33.553829 | -0.659760 | -0.657918 | 51.0 | 0.072796 | 0.005299 | -0.550007 | -0.793064 | 54.696705 | 0.877398 | 1.072484 | 51.0 | 0.548211 | 0.300535 | 2.050468 | 0.381210 | -21.277276 | -1.030952 | -0.417201 | 51.0 | 1.346573 | 1.813258 | 1.466641 | -1.913035 | -25.165306 | -0.649369 | -0.493437 | 51.0 | 0.331068 | 0.109606 | -0.022846 | -0.904735 | 1.0 |
| 2 | -29.636921 | -0.573012 | -0.581116 | 51.0 | 0.019839 | 0.000394 | -0.562071 | -0.638035 | -89.379762 | -1.754199 | -1.752544 | 51.0 | 0.034682 | 0.001203 | -1.695036 | -1.794399 | -31.525926 | -0.618192 | -0.618155 | 51.0 | 0.019983 | 0.000399 | -0.585535 | -0.646959 | 52.735165 | 0.866712 | 1.034023 | 51.0 | 0.479992 | 0.230392 | 2.004829 | 0.571202 | -32.593689 | -1.377992 | -0.639092 | 51.0 | 1.270902 | 1.615192 | 1.225264 | -1.825619 | -29.333990 | -0.617215 | -0.575176 | 51.0 | 0.246258 | 0.060643 | -0.081632 | -0.969561 | -31.207949 | -0.609448 | -0.611921 | 51.0 | 0.007926 | 0.000063 | -0.604111 | -0.635759 | -36.385547 | -0.706627 | -0.713442 | 51.0 | 0.017265 | 0.000298 | -0.683182 | -0.734319 | -4.532246 | -0.089797 | -0.088868 | 51.0 | 0.004149 | 0.000017 | -0.074800 | -0.094186 | 43.812447 | 0.830219 | 0.859068 | 51.0 | 0.251514 | 0.063259 | 1.214150 | 0.596921 | -24.633379 | -0.654848 | -0.483007 | 51.0 | 0.329551 | 0.108604 | 0.058672 | -0.776904 | -2.887452 | -0.064124 | -0.056617 | 51.0 | 0.017578 | 0.000309 | -0.026005 | -0.100879 | -34.023640 | -0.664266 | -0.667130 | 51.0 | 0.014798 | 0.000219 | -0.650468 | -0.712312 | -66.068222 | -1.295266 | -1.295455 | 51.0 | 0.029608 | 0.000877 | -1.254792 | -1.402467 | -19.930524 | -0.387555 | -0.390795 | 51.0 | 0.025839 | 0.000668 | -0.323750 | -0.458687 | 51.761165 | 1.014252 | 1.014925 | 51.0 | 0.343023 | 0.117665 | 1.680628 | 0.653975 | -28.954203 | -1.036182 | -0.567729 | 51.0 | 0.866887 | 0.751493 | 0.714727 | -1.390011 | -17.876280 | -0.401144 | -0.350515 | 51.0 | 0.131877 | 0.017391 | -0.078505 | -0.549548 | -36.563061 | -0.713853 | -0.716923 | 51.0 | 0.012922 | 0.000167 | -0.696873 | -0.750091 | -76.812027 | -1.513128 | -1.506118 | 51.0 | 0.027557 | 0.000759 | -1.450089 | -1.551815 | -23.915725 | -0.468774 | -0.468936 | 51.0 | 0.018951 | 0.000359 | -0.433935 | -0.506069 | 46.672813 | 0.790502 | 0.915153 | 51.0 | 0.438349 | 0.192150 | 1.779223 | 0.468808 | -29.423805 | -1.152751 | -0.576937 | 51.0 | 1.048190 | 1.098701 | 0.963911 | -1.638763 | -30.259670 | -0.652242 | -0.593327 | 51.0 | 0.208057 | 0.043288 | -0.156107 | -0.881726 | 1.0 |
| 3 | -25.047110 | -0.469976 | -0.491120 | 51.0 | 1.039325 | 1.080197 | 1.277576 | -2.058804 | -27.482972 | 0.052630 | -0.538882 | 51.0 | 1.092142 | 1.192774 | 0.847924 | -2.296996 | -59.228195 | -1.158052 | -1.161337 | 51.0 | 0.577929 | 0.334002 | 0.125093 | -1.880435 | 47.830925 | 0.781728 | 0.937861 | 51.0 | 1.001210 | 1.002422 | 2.316732 | -0.775241 | -44.241653 | -0.416005 | -0.867483 | 51.0 | 1.020474 | 1.041367 | 0.490420 | -2.644791 | 1.352823 | -0.140039 | 0.026526 | 51.0 | 0.645263 | 0.416364 | 1.190540 | -0.959072 | -35.933167 | -0.697214 | -0.704572 | 51.0 | 0.347193 | 0.120543 | -0.034316 | -1.154175 | -25.223339 | -0.645148 | -0.494575 | 51.0 | 0.358822 | 0.128754 | 0.050433 | -0.876566 | -37.310913 | -0.828076 | -0.731587 | 51.0 | 0.191002 | 0.036482 | -0.459349 | -1.033536 | 44.102791 | 0.893525 | 0.864761 | 51.0 | 0.195794 | 0.038335 | 1.276629 | 0.602641 | -41.254436 | -0.945527 | -0.808911 | 51.0 | 0.334384 | 0.111812 | -0.289740 | -1.265870 | 16.481028 | 0.273628 | 0.323157 | 51.0 | 0.175884 | 0.030935 | 0.575722 | 0.030430 | -26.781509 | -0.601354 | -0.525128 | 51.0 | 0.842071 | 0.709084 | 0.937225 | -1.677820 | -23.497918 | -0.104672 | -0.460744 | 51.0 | 0.792314 | 0.627761 | 0.495796 | -1.714955 | -53.039357 | -1.034547 | -1.039987 | 51.0 | 0.321692 | 0.103486 | -0.422473 | -1.492176 | 44.988911 | 0.764692 | 0.882135 | 51.0 | 0.652326 | 0.425529 | 1.773704 | -0.289913 | -42.777878 | -0.566373 | -0.838782 | 51.0 | 0.736869 | 0.542975 | 0.090714 | -2.217362 | -0.450630 | -0.011670 | -0.008836 | 51.0 | 0.611221 | 0.373591 | 0.899674 | -1.086155 | -23.856543 | -0.429491 | -0.467775 | 51.0 | 0.940447 | 0.884441 | 1.246905 | -1.898433 | -22.166147 | -0.083299 | -0.434630 | 51.0 | 0.971415 | 0.943647 | 0.817574 | -1.969373 | -58.418483 | -1.192878 | -1.145460 | 51.0 | 0.435533 | 0.189689 | -0.313104 | -1.728791 | 50.293598 | 0.794829 | 0.986149 | 51.0 | 0.746310 | 0.556978 | 2.143597 | -0.172461 | -38.037960 | -0.281492 | -0.745842 | 51.0 | 0.886904 | 0.786598 | 0.367451 | -2.336047 | 0.735820 | -0.181307 | 0.014428 | 51.0 | 0.599972 | 0.359967 | 1.134387 | -0.852797 | 4.0 |
| 4 | -41.201576 | -0.608070 | -0.807874 | 51.0 | 0.889726 | 0.791613 | 0.725645 | -2.195617 | -23.629126 | -0.085047 | -0.463316 | 51.0 | 1.018654 | 1.037656 | 1.068211 | -1.795286 | -21.292732 | -0.389128 | -0.417505 | 51.0 | 0.400357 | 0.160286 | 0.249956 | -1.447868 | 38.498398 | 0.440613 | 0.754871 | 51.0 | 0.808717 | 0.654023 | 2.245723 | -0.438493 | -23.457401 | -0.023015 | -0.459949 | 51.0 | 1.054098 | 1.111123 | 1.131552 | -1.833448 | -31.360987 | -0.646476 | -0.614921 | 51.0 | 0.340407 | 0.115877 | -0.082861 | -1.529216 | -40.306332 | -0.739632 | -0.790320 | 51.0 | 0.230506 | 0.053133 | -0.352509 | -1.184206 | -17.231264 | -0.299905 | -0.337868 | 51.0 | 0.347758 | 0.120935 | 0.503786 | -0.782599 | -4.869106 | 0.016422 | -0.095473 | 51.0 | 0.267548 | 0.071582 | 0.260650 | -0.658415 | 37.220966 | 0.643430 | 0.729823 | 51.0 | 0.281067 | 0.078999 | 1.205101 | 0.321586 | -24.338264 | -0.670313 | -0.477221 | 51.0 | 0.335451 | 0.112527 | 0.104471 | -0.803964 | -21.408007 | -0.335360 | -0.419765 | 51.0 | 0.226118 | 0.051130 | -0.075735 | -0.844365 | -41.397484 | -0.662061 | -0.811715 | 51.0 | 0.655220 | 0.429313 | 0.548439 | -1.776663 | -20.873059 | -0.113856 | -0.409276 | 51.0 | 0.729906 | 0.532763 | 0.857338 | -1.408824 | -16.444862 | -0.282158 | -0.322448 | 51.0 | 0.352216 | 0.124056 | 0.251888 | -1.069958 | 37.894848 | 0.584005 | 0.743036 | 51.0 | 0.630277 | 0.397249 | 1.795148 | -0.144278 | -20.929989 | -0.043612 | -0.410392 | 51.0 | 0.724184 | 0.524443 | 0.697051 | -1.370825 | -28.255297 | -0.558395 | -0.554025 | 51.0 | 0.242117 | 0.058620 | -0.055325 | -1.180921 | -39.820022 | -0.545180 | -0.780785 | 51.0 | 0.775871 | 0.601976 | 0.629833 | -1.975986 | -20.506063 | -0.086851 | -0.402080 | 51.0 | 0.824082 | 0.679111 | 0.906945 | -1.510986 | -18.606042 | -0.438775 | -0.364824 | 51.0 | 0.404515 | 0.163632 | 0.431112 | -1.247529 | 35.120491 | 0.361010 | 0.688637 | 51.0 | 0.792433 | 0.627950 | 2.130472 | -0.434877 | -22.394552 | -0.122457 | -0.439109 | 51.0 | 0.852059 | 0.726005 | 0.838727 | -1.533409 | -33.628571 | -0.686534 | -0.659384 | 51.0 | 0.268276 | 0.071972 | -0.119453 | -1.341451 | 5.0 |
| 5 | -24.823248 | -0.477101 | -0.486730 | 51.0 | 0.036861 | 0.001359 | -0.402602 | -0.539456 | -98.234352 | -1.835871 | -1.926164 | 51.0 | 0.188396 | 0.035493 | -1.700439 | -2.180160 | -53.555866 | -1.050411 | -1.050115 | 51.0 | 0.051149 | 0.002616 | -0.966048 | -1.116951 | 59.429630 | 1.212786 | 1.165287 | 51.0 | 0.602850 | 0.363428 | 2.092470 | 0.392353 | -65.917328 | -1.942046 | -1.292497 | 51.0 | 1.115073 | 1.243388 | 0.652547 | -2.357580 | 6.232958 | 0.022522 | 0.122215 | 51.0 | 0.456835 | 0.208698 | 0.893114 | -0.377309 | -35.642277 | -0.690219 | -0.698868 | 51.0 | 0.043748 | 0.001914 | -0.619525 | -0.756708 | -40.159771 | -0.739370 | -0.787446 | 51.0 | 0.072142 | 0.005204 | -0.700199 | -0.913578 | -26.857824 | -0.533884 | -0.526624 | 51.0 | 0.027091 | 0.000734 | -0.478079 | -0.566838 | 35.622574 | 0.654334 | 0.698482 | 51.0 | 0.172152 | 0.029636 | 0.987900 | 0.493199 | -30.764698 | -0.944417 | -0.603229 | 51.0 | 0.509118 | 0.259201 | 0.251938 | -1.086303 | 18.843518 | 0.335765 | 0.369481 | 51.0 | 0.186673 | 0.034847 | 0.677908 | 0.132334 | -30.103149 | -0.581161 | -0.590258 | 51.0 | 0.035138 | 0.001235 | -0.524841 | -0.637219 | -78.376451 | -1.489799 | -1.536793 | 51.0 | 0.176593 | 0.031185 | -1.311442 | -1.770294 | -45.693409 | -0.910518 | -0.895949 | 51.0 | 0.040035 | 0.001603 | -0.818448 | -0.937067 | 50.819454 | 0.965418 | 0.996460 | 51.0 | 0.413543 | 0.171018 | 1.643847 | 0.482386 | -53.046330 | -1.568685 | -1.040124 | 51.0 | 0.912747 | 0.833107 | 0.495775 | -1.925183 | 12.238467 | 0.217083 | 0.239970 | 51.0 | 0.390782 | 0.152710 | 0.862907 | -0.180789 | -30.803093 | -0.600533 | -0.603982 | 51.0 | 0.045415 | 0.002063 | -0.502313 | -0.678477 | -90.878960 | -1.722590 | -1.781940 | 51.0 | 0.156182 | 0.024393 | -1.552939 | -2.028900 | -54.484894 | -1.060069 | -1.068331 | 51.0 | 0.057381 | 0.003293 | -0.920982 | -1.157738 | 53.226456 | 1.024801 | 1.043656 | 51.0 | 0.600072 | 0.360086 | 1.967641 | 0.370038 | -62.520919 | -1.807466 | -1.225900 | 51.0 | 0.955443 | 0.912871 | 0.461566 | -2.194059 | 3.950203 | -0.022342 | 0.077455 | 51.0 | 0.487613 | 0.237767 | 0.832024 | -0.476938 | 3.0 |
| 6 | -27.848024 | -0.538649 | -0.546040 | 51.0 | 0.015033 | 0.000226 | -0.522586 | -0.570220 | -95.294815 | -1.893335 | -1.868526 | 51.0 | 0.043781 | 0.001917 | -1.780694 | -1.924438 | -36.981468 | -0.727544 | -0.725127 | 51.0 | 0.020072 | 0.000403 | -0.686548 | -0.754100 | 55.414669 | 0.806179 | 1.086562 | 51.0 | 0.654030 | 0.427755 | 2.262416 | 0.445696 | -29.805776 | -1.477438 | -0.584427 | 51.0 | 1.434080 | 2.056585 | 1.527065 | -1.968565 | -24.468878 | -0.628565 | -0.479782 | 51.0 | 0.305268 | 0.093189 | -0.005088 | -0.908052 | -35.164513 | -0.692197 | -0.689500 | 51.0 | 0.009485 | 0.000090 | -0.673945 | -0.705548 | -38.067444 | -0.746727 | -0.746420 | 51.0 | 0.009586 | 0.000092 | -0.727163 | -0.767835 | -11.635359 | -0.225926 | -0.228144 | 51.0 | 0.007739 | 0.000060 | -0.218580 | -0.248116 | 44.761055 | 0.822936 | 0.877668 | 51.0 | 0.216912 | 0.047051 | 1.240380 | 0.648004 | -20.868835 | -0.760081 | -0.409193 | 51.0 | 0.476509 | 0.227060 | 0.348744 | -0.813243 | -5.508397 | -0.085385 | -0.108008 | 51.0 | 0.079066 | 0.006251 | 0.009747 | -0.225309 | -32.452461 | -0.634017 | -0.636323 | 51.0 | 0.015947 | 0.000254 | -0.616828 | -0.663031 | -75.819839 | -1.505544 | -1.486663 | 51.0 | 0.032703 | 0.001070 | -1.427568 | -1.529979 | -29.806185 | -0.585979 | -0.584435 | 51.0 | 0.006767 | 0.000046 | -0.570106 | -0.595026 | 51.620747 | 0.885039 | 1.012172 | 51.0 | 0.422080 | 0.178151 | 1.823669 | 0.575215 | -27.436798 | -1.176618 | -0.537976 | 51.0 | 1.026407 | 1.053511 | 1.058236 | -1.525770 | -17.655451 | -0.339583 | -0.346185 | 51.0 | 0.193007 | 0.037252 | -0.079380 | -0.592352 | -37.255925 | -0.723211 | -0.730508 | 51.0 | 0.019915 | 0.000397 | -0.703212 | -0.762753 | -85.168221 | -1.690704 | -1.669965 | 51.0 | 0.040836 | 0.001668 | -1.589246 | -1.744377 | -31.423119 | -0.622018 | -0.616140 | 51.0 | 0.017410 | 0.000303 | -0.575975 | -0.642287 | 52.871548 | 0.781574 | 1.036697 | 51.0 | 0.495497 | 0.245518 | 1.933541 | 0.409018 | -27.850479 | -1.203557 | -0.546088 | 51.0 | 1.228680 | 1.509654 | 1.249242 | -1.753250 | -24.339149 | -0.652606 | -0.477238 | 51.0 | 0.285133 | 0.081301 | -0.007081 | -0.831571 | 1.0 |
| 7 | -33.276344 | -0.479505 | -0.652477 | 51.0 | 0.869560 | 0.756135 | 0.849287 | -2.133169 | -37.855068 | -0.419435 | -0.742256 | 51.0 | 0.956894 | 0.915645 | 0.624985 | -2.005778 | -43.155506 | -0.750605 | -0.846186 | 51.0 | 0.346998 | 0.120408 | -0.250388 | -1.688274 | 35.110122 | 0.468405 | 0.688434 | 51.0 | 0.908347 | 0.825095 | 2.131079 | -0.782841 | -41.891838 | -0.555969 | -0.821409 | 51.0 | 0.994760 | 0.989548 | 0.625232 | -2.032552 | -27.041738 | -0.717695 | -0.530230 | 51.0 | 0.598307 | 0.357971 | 0.693191 | -1.669508 | -37.228020 | -0.596140 | -0.729961 | 51.0 | 0.247121 | 0.061069 | -0.333970 | -1.222549 | -25.548452 | -0.601536 | -0.500950 | 51.0 | 0.315148 | 0.099318 | 0.093430 | -0.851777 | -15.347136 | -0.237232 | -0.300924 | 51.0 | 0.165886 | 0.027518 | -0.121151 | -0.765126 | 37.993805 | 0.641868 | 0.744977 | 51.0 | 0.227982 | 0.051976 | 1.154039 | 0.446156 | -31.068548 | -0.808422 | -0.609187 | 51.0 | 0.312671 | 0.097763 | 0.060170 | -0.877740 | -10.810991 | -0.308081 | -0.211980 | 51.0 | 0.275323 | 0.075803 | 0.265465 | -0.566001 | -34.890946 | -0.571202 | -0.684136 | 51.0 | 0.624475 | 0.389969 | 0.488460 | -1.755041 | -31.793698 | -0.562911 | -0.623406 | 51.0 | 0.690469 | 0.476747 | 0.304994 | -1.560801 | -34.411724 | -0.565013 | -0.674740 | 51.0 | 0.217068 | 0.047118 | -0.464671 | -1.342274 | 35.677319 | 0.577993 | 0.699555 | 51.0 | 0.625535 | 0.391294 | 1.662605 | -0.328879 | -34.656643 | -0.496127 | -0.679542 | 51.0 | 0.706674 | 0.499388 | 0.320454 | -1.570962 | -22.420255 | -0.618588 | -0.439613 | 51.0 | 0.440747 | 0.194258 | 0.470547 | -1.150325 | -34.909180 | -0.577205 | -0.684494 | 51.0 | 0.804206 | 0.646747 | 0.670274 | -1.959881 | -38.267212 | -0.503481 | -0.750338 | 51.0 | 0.813693 | 0.662097 | 0.490504 | -1.814681 | -38.217155 | -0.602884 | -0.749356 | 51.0 | 0.323900 | 0.104911 | -0.414631 | -1.582031 | 37.202102 | 0.617045 | 0.729453 | 51.0 | 0.739951 | 0.547528 | 1.986095 | -0.486259 | -35.451447 | -0.374829 | -0.695126 | 51.0 | 0.854458 | 0.730099 | 0.628421 | -1.796660 | -28.583450 | -0.702296 | -0.560460 | 51.0 | 0.482700 | 0.233000 | 0.552294 | -1.458844 | 4.0 |
| 8 | -37.221546 | -0.704033 | -0.729834 | 51.0 | 0.737737 | 0.544255 | 0.709278 | -2.387635 | -49.114784 | -1.632605 | -0.963035 | 51.0 | 0.893288 | 0.797964 | 0.625720 | -1.738466 | -42.148529 | -0.789197 | -0.826442 | 51.0 | 0.364045 | 0.132529 | 0.033324 | -1.639284 | 39.506935 | 0.702783 | 0.774646 | 51.0 | 0.736887 | 0.543002 | 2.331514 | -0.687143 | -51.578213 | -1.707612 | -1.011338 | 51.0 | 0.878936 | 0.772528 | 0.655798 | -1.767194 | -30.958001 | -0.663108 | -0.607020 | 51.0 | 0.363462 | 0.132104 | 0.484722 | -1.503030 | -38.975426 | -0.727765 | -0.764224 | 51.0 | 0.187154 | 0.035026 | -0.434912 | -1.149907 | -24.855530 | -0.664124 | -0.487363 | 51.0 | 0.242774 | 0.058939 | 0.013168 | -0.712478 | -12.185540 | -0.152051 | -0.238932 | 51.0 | 0.201546 | 0.040621 | -0.076963 | -0.740207 | 37.004498 | 0.642680 | 0.725578 | 51.0 | 0.204231 | 0.041710 | 1.185161 | 0.454508 | -30.417671 | -0.725361 | -0.596425 | 51.0 | 0.208093 | 0.043303 | -0.140892 | -0.770591 | -3.281179 | 0.000372 | -0.064337 | 51.0 | 0.234173 | 0.054837 | 0.305370 | -0.628432 | -39.282730 | -0.783600 | -0.770250 | 51.0 | 0.525708 | 0.276369 | 0.378127 | -1.808407 | -40.044132 | -1.292149 | -0.785179 | 51.0 | 0.631058 | 0.398234 | 0.335704 | -1.330300 | -31.045876 | -0.557992 | -0.608743 | 51.0 | 0.226520 | 0.051311 | -0.201011 | -1.347596 | 38.689915 | 0.719858 | 0.758626 | 51.0 | 0.515307 | 0.265541 | 1.865181 | -0.360475 | -42.383713 | -1.294223 | -0.831053 | 51.0 | 0.589173 | 0.347125 | 0.261855 | -1.400007 | -19.460783 | -0.369220 | -0.381584 | 51.0 | 0.288380 | 0.083163 | 0.359176 | -1.007585 | -42.013736 | -0.833883 | -0.823799 | 51.0 | 0.608988 | 0.370867 | 0.433874 | -2.403301 | -44.656379 | -1.350125 | -0.875615 | 51.0 | 0.795833 | 0.633350 | 0.495625 | -1.658140 | -37.463715 | -0.618286 | -0.734583 | 51.0 | 0.335002 | 0.112227 | -0.091560 | -1.530195 | 37.702812 | 0.643129 | 0.739271 | 51.0 | 0.681370 | 0.464266 | 2.149762 | -0.648830 | -51.831993 | -1.495859 | -1.016314 | 51.0 | 0.746308 | 0.556975 | 0.454416 | -1.740017 | -28.965262 | -0.586536 | -0.567946 | 51.0 | 0.364034 | 0.132521 | 0.470018 | -1.452238 | 4.0 |
| 9 | -24.292154 | -0.723827 | -0.476317 | 51.0 | 0.701995 | 0.492797 | 0.633993 | -1.616795 | -74.903946 | -1.739034 | -1.468705 | 51.0 | 0.874647 | 0.765007 | -0.283526 | -2.461297 | -58.185723 | -1.010472 | -1.140897 | 51.0 | 0.349332 | 0.122033 | -0.764571 | -1.996507 | 55.119583 | 0.883657 | 1.080776 | 51.0 | 0.379395 | 0.143940 | 1.931519 | 0.667815 | -38.653057 | -1.433481 | -0.757903 | 51.0 | 1.465900 | 2.148863 | 1.174249 | -2.271329 | -39.732315 | -0.775350 | -0.779065 | 51.0 | 0.241241 | 0.058197 | -0.321175 | -1.294348 | -36.798691 | -0.705611 | -0.721543 | 51.0 | 0.130146 | 0.016938 | -0.506952 | -0.942217 | -34.612572 | -0.681793 | -0.678678 | 51.0 | 0.171086 | 0.029270 | -0.312264 | -0.868394 | -19.718096 | -0.343649 | -0.386629 | 51.0 | 0.153238 | 0.023482 | -0.211027 | -0.667914 | 43.239754 | 0.782406 | 0.847838 | 51.0 | 0.123642 | 0.015287 | 1.135982 | 0.731697 | -24.307549 | -0.778194 | -0.476619 | 51.0 | 0.507080 | 0.257130 | 0.460838 | -0.902615 | -18.462799 | -0.210704 | -0.362016 | 51.0 | 0.241684 | 0.058411 | -0.126644 | -0.759889 | -30.686081 | -0.755561 | -0.601688 | 51.0 | 0.436531 | 0.190559 | 0.124602 | -1.240271 | -57.753544 | -1.286138 | -1.132422 | 51.0 | 0.535568 | 0.286833 | -0.341215 | -1.725427 | -43.513134 | -0.932897 | -0.853199 | 51.0 | 0.219806 | 0.048315 | -0.526928 | -1.282452 | 50.507580 | 0.848892 | 0.990345 | 51.0 | 0.265001 | 0.070226 | 1.544412 | 0.719464 | -31.098295 | -1.147269 | -0.609771 | 51.0 | 1.160387 | 1.346499 | 1.133501 | -1.766298 | -29.789145 | -0.551675 | -0.584101 | 51.0 | 0.143176 | 0.020499 | -0.381935 | -0.910881 | -27.278313 | -0.797646 | -0.534869 | 51.0 | 0.636158 | 0.404698 | 0.430965 | -1.549182 | -70.921883 | -1.650485 | -1.390625 | 51.0 | 0.701938 | 0.492716 | -0.426613 | -2.219845 | -49.757236 | -0.831714 | -0.975632 | 51.0 | 0.309763 | 0.095953 | -0.612066 | -1.783972 | 50.518131 | 0.874909 | 0.990552 | 51.0 | 0.390238 | 0.152286 | 1.873247 | 0.571731 | -34.528259 | -1.256407 | -0.677025 | 51.0 | 1.277018 | 1.630776 | 1.000317 | -2.027508 | -39.304932 | -0.779061 | -0.770685 | 51.0 | 0.175208 | 0.030698 | -0.456689 | -1.218413 | 6.0 |
x_cat, x_cont, yb = first(dls.train)
x_cont
tensor([[ 0.2510, -1.0216, 0.2510, ..., -0.8254, -1.2280, -0.4989],
[ 2.8093, 2.8782, 2.8093, ..., 1.1049, 0.1915, -1.3763],
[-0.3896, -0.3842, -0.3896, ..., -0.8492, -1.7897, -0.7025],
...,
[-0.2363, -0.2105, -0.2363, ..., -0.6975, -0.5374, 0.4494],
[ 0.8455, 0.3421, 0.8455, ..., -0.3739, -0.0638, -0.1428],
[ 0.5340, 0.8307, 0.5340, ..., 0.4782, 1.3826, 1.5660]])
from tsai.models.utils import *
from tsai.models.TabModel import *
model = build_tabular_model(TabModel, dls=dls)
learn = Learner(dls, model, metrics=[accuracy, RocAuc()])
learn.fit_one_cycle(5)
| epoch | train_loss | valid_loss | accuracy | roc_auc_score | time |
|---|---|---|---|---|---|
| 0 | 1.806789 | 1.778912 | 0.216667 | 0.614370 | 00:00 |
| 1 | 1.781897 | 1.676545 | 0.422222 | 0.909481 | 00:00 |
| 2 | 1.733134 | 1.567385 | 0.533333 | 0.945778 | 00:00 |
| 3 | 1.678977 | 1.493117 | 0.566667 | 0.949370 | 00:00 |
| 4 | 1.632174 | 1.471912 | 0.577778 | 0.950074 | 00:00 |
b = first(dls.train)
model(*b[:-1]).shape
torch.Size([64, 6])