Help with findgroups/splitapply, and general deep learning help.

2025 2월 4
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업데이트 시간: 2025 2월 8
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Hi All,
I am trying to use findgroups, and splitapply to organise a table into a cellarray in a certain way, but I am getting some issues. One of the issues is that the number of groups returned isn't what I am expecting, i.e. the number of uniques in group 1, and the number of uniques in group 2. The file is attached. What the end goal is, is having a cellarray, with the trade dates going down, and the EFAs going across. When I combine, just originally, it isn't dividable by either the trade dates, or the number of unique EFA. The code I used originally is this:
G=findgroups( dateshift(FCTable.tradeDate,'start','day'), categorical(FCTable.EFA));
xAll = splitapply(@(x) {x} , FCTable{:,[2:7,10:13]} , G);
The end goal is for use in Deep Learning, as I originally have a model that does predictions for sequence to label classification for trade dates, however, I want to have the individual blocks included as well. So if there's any hints regarding this as well - please let me know. I'm assuming I will have to combine LSTM's, but I'm not sure. i.e. for tomorrow, there will be a prediction made for each EFA (time period basically) of the trade date.

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Voss
Voss 2025년 2월 4일
Ran in:

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Ran in:
FCTable = load('FCTable.mat').FCTable
FCTable = 17590x14 table
EFA Variable1 Variable2 Variable3 Variable4 Variable5 Variable6 Variable7 Variable8 Variable9 Variable10 Variable11 Variable12 tradeDate _____ _________ _________ _________ _________ _________ _________ _________ _________ _________ __________ __________ __________ __________ {'1'} 2572 2149 2996 4305 2604 3118 55 0 1255 67.705 31.71 20.106 03/02/2024 {'1'} 2507 2068 2947 4186 2523 3036 55 0 1196 66.822 31.71 19.139 03/02/2024 {'1'} 2438 2003 2872 4093 2460 2956 52.11 0 1088 66.263 25.384 18.124 03/02/2024 {'1'} 2363 1955 2770 4033 2386 2880 52.11 0 1125 65.468 25.384 17.051 03/02/2024 {'1'} 2298 1904 2691 3997 2290 2809 52.71 0 1149 64.822 20.722 16.309 03/02/2024 {'1'} 2243 1850 2635 3945 2236 2762 52.71 0 1190 64.671 20.722 15.919 03/02/2024 {'2'} 2201 1809 2592 3876 2188 2694 50.71 0 1134 63.675 15.052 15.274 03/02/2024 {'2'} 2172 1781 2563 3823 2112 2555 50.71 0 1115 63.525 15.052 14.386 03/02/2024 {'2'} 2112 1687 2536 3796 2062 2417 54.44 5.35 976 62.454 17.032 13.381 03/02/2024 {'2'} 2019 1527 2511 3798 1992 2298 54.44 6.53 847 62.719 17.032 12.239 03/02/2024 {'2'} 1920 1377 2463 3826 1916 2188 51.42 3.11 750 62.608 23.416 11.156 03/02/2024 {'2'} 1815 1239 2392 3873 1876 2108 51.42 29.97 605 62.999 23.416 10.121 03/02/2024 {'2'} 1737 1109 2365 3942 1869 2027 76 29.86 404 63.427 36.313 9.2818 03/02/2024 {'2'} 1686 989 2383 4065 1827 1956 76 128.25 294 62.802 36.313 8.6376 03/02/2024 {'3'} 1657 925 2389 4231 1777 1897 83.33 151.66 181 59.111 52.666 8.2306 03/02/2024 {'3'} 1650 917 2382 4377 1760 1873 83.33 125.57 72 57.294 52.666 7.9755 03/02/2024
T = FCTable;
T.tradeDate_shift = dateshift(T.tradeDate,'start','day');
T.EFA_categorical = categorical(T.EFA);
old_var_name = "Variable"+[1:6,9:12];
new_var_name = "Data";
T = mergevars(T,old_var_name,'NewVariableName',new_var_name);
T = removevars(T,"Variable"+[7,8])
T = 17590x5 table
EFA Data tradeDate tradeDate_shift EFA_categorical _____ ______________________________________________________________________________________________ __________ _______________ _______________ {'1'} 2572 2149 2996 4305 2604 3118 1255 67.705 31.71 20.106 03/02/2024 03/02/2024 1 {'1'} 2507 2068 2947 4186 2523 3036 1196 66.822 31.71 19.139 03/02/2024 03/02/2024 1 {'1'} 2438 2003 2872 4093 2460 2956 1088 66.263 25.384 18.124 03/02/2024 03/02/2024 1 {'1'} 2363 1955 2770 4033 2386 2880 1125 65.468 25.384 17.051 03/02/2024 03/02/2024 1 {'1'} 2298 1904 2691 3997 2290 2809 1149 64.822 20.722 16.309 03/02/2024 03/02/2024 1 {'1'} 2243 1850 2635 3945 2236 2762 1190 64.671 20.722 15.919 03/02/2024 03/02/2024 1 {'2'} 2201 1809 2592 3876 2188 2694 1134 63.675 15.052 15.274 03/02/2024 03/02/2024 2 {'2'} 2172 1781 2563 3823 2112 2555 1115 63.525 15.052 14.386 03/02/2024 03/02/2024 2 {'2'} 2112 1687 2536 3796 2062 2417 976 62.454 17.032 13.381 03/02/2024 03/02/2024 2 {'2'} 2019 1527 2511 3798 1992 2298 847 62.719 17.032 12.239 03/02/2024 03/02/2024 2 {'2'} 1920 1377 2463 3826 1916 2188 750 62.608 23.416 11.156 03/02/2024 03/02/2024 2 {'2'} 1815 1239 2392 3873 1876 2108 605 62.999 23.416 10.121 03/02/2024 03/02/2024 2 {'2'} 1737 1109 2365 3942 1869 2027 404 63.427 36.313 9.2818 03/02/2024 03/02/2024 2 {'2'} 1686 989 2383 4065 1827 1956 294 62.802 36.313 8.6376 03/02/2024 03/02/2024 2 {'3'} 1657 925 2389 4231 1777 1897 181 59.111 52.666 8.2306 03/02/2024 03/02/2024 3 {'3'} 1650 917 2382 4377 1760 1873 72 57.294 52.666 7.9755 03/02/2024 03/02/2024 3
TG = groupsummary(T,{'EFA_categorical','tradeDate_shift'},@(x){x}, ...
new_var_name,'IncludeEmptyGroups',true);
TG = renamevars(TG,"fun1_"+new_var_name,new_var_name)
TG = 2202x4 table
EFA_categorical tradeDate_shift GroupCount Data _______________ _______________ __________ _____________ 1 03/02/2024 6 {6x10 double} 1 04/02/2024 8 {8x10 double} 1 05/02/2024 8 {8x10 double} 1 06/02/2024 8 {8x10 double} 1 07/02/2024 8 {8x10 double} 1 08/02/2024 8 {8x10 double} 1 09/02/2024 8 {8x10 double} 1 10/02/2024 8 {8x10 double} 1 11/02/2024 8 {8x10 double} 1 12/02/2024 8 {8x10 double} 1 13/02/2024 8 {8x10 double} 1 14/02/2024 8 {8x10 double} 1 15/02/2024 8 {8x10 double} 1 16/02/2024 8 {8x10 double} 1 17/02/2024 8 {8x10 double} 1 18/02/2024 8 {8x10 double}
ND = numel(unique(T.tradeDate_shift));
NE = numel(unique(T.EFA_categorical));
assert(height(TG) == ND*NE)
% your cell array
C = reshape(TG.Data,ND,NE)
C = 367x6 cell array
{6x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double} {8x10 double}

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James McBrearty
James McBrearty 2025년 2월 7일
Hi Voss,
This is excellent. I did change some things around on my end, but I never knew the functionality of groupsummary. I have it working now in a couple of parallel LSTM's now, and it's looking like it's doing a great job.
Many Thanks, James
Voss
Voss 2025년 2월 8일
You're welcome!

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James McBrearty
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2025년 2월 4일

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