I applied ColumnTransformer, but the results are unexpected. It could be my lack of python skill, but it seems like the value of p1_1 in the original should persist at 0,0 in the transformed? ------- pre-scale p1_1 p1_2 p1_3 p1_4 p2_1 ... resp1_4 resp2_1 resp2_2 resp2_3 resp2_4 760 1.382658 1.440719 1.555705 1.120171 1.717319 ... 0.598736 0.659797 0.376331 0.403887 0.390283 ------- scaled [[0.17045455 0.04680535 0.04372197 ... 0.37633118 0.40388673 0.39028345] Thanks, Bill Fingers crossed on the formatting. column_trans = make_column_transformer( (MinMaxScaler(), ['order_in_session','big_stime','big_time','load_time','user_time','user_time2','mouse_down_time','mouse_time','mouse_dist','mouse_dist2','dot_count','mouse_dx','mouse_dy','mouse_vecx','mouse_vecy','dot_vec_len','mouse_maxv','mouse_maxa','mouse_mina','mouse_maxj','dot_max_vel','dot_max_acc','dot_max_jerk','dot_start_scrn','dot_end_scrn','dot_vec_ang']), remainder='passthrough') print('------- pre-scale') print( str(X_train) ) X_train = column_trans.fit_transform(X_train) print('------- scaled') print( str(X_train) ) print('------- /scaled') split 414 414 ------- pre-scale p1_1 p1_2 p1_3 p1_4 p2_1 ... resp1_4 resp2_1 resp2_2 resp2_3 resp2_4 760 1.382658 1.440719 1.555705 1.120171 1.717319 ... 0.598736 0.659797 0.376331 0.403887 0.390283 218 0.985645 0.532462 0.780601 0.687588 0.781293 ... 0.890886 1.072392 0.536962 0.715136 0.792722 603 0.783806 0.437074 0.694766 0.371121 0.995891 ... 1.055465 1.518875 1.129209 1.201864 1.476702 0 0.501352 0.253304 0.427804 0.283380 0.571035 ... 1.035323 1.621431 0.838613 1.031724 1.131344 604 1.442482 1.019641 0.798387 1.055465 1.518875 ... 2.779447 1.636363 1.212313 1.274595 1.723697 ... ... ------- scaled [[0.17045455 0.04680535 0.04372197 ... 0.37633118 0.40388673 0.39028345] [0.27272727 0.04502229 0.04204036 ... 0.53696203 0.7151355 0.7927222 ] [0.30681818 0.04517088 0.04456278 ... 1.1292094 1.201864 1.4767016 ] ... [0.02272727 0.04457652 0.1680213 ... 1.796316 1.939811 2.1776829 ] [0.55681818 0.04546805 0.04176009 ... 0.48330075 0.37375322 0.29931256] [0.5 0.04457652 0.04091928 ... 0.6759416 0.7517819 0.8801653 ]] --- -- Phobrain.com On 2025-01-23 01:21, Bill Ross wrote:
ColumnTransformer
Thanks!
I was also thinking of trying TabPFN, not researched yet, in case you can comment. <peeks/> Their attribution requirement seems overboard for what I want, unless it's flat-out miraculous for the flat-footed. :-)
Some of us are working on a related package, skrub (https://skrub-data.org), which is more focused to on heterogeneous dataframes. It does not currently have something that would help you much, but we are heavily brain-storming a variety of APIs to do flexible transformations of dataframes, including easily doing what you want. The challenge is to address the variety of cases.
Those are the storms we want. I'd love to know if/how/which ML tools are helping with that work, if appropriate here.
Regards, Bill