A custom feature selection program with various algorithms and their pipelined combination in an easy and descriptive way.
Read Path:
A function to read the path of the CSV file given
>>> import FeatureSelector as fs
>>> X,y = fs.read_path('D:\Internship\dataset.csv')
>>> X,y
( TSPAN6 FGR CFH GCLC NIPAL3 ... IFNGR1 SH2D2A TNFRSF1B ARNTL2 IBSP
0 3.593240 0.714444 3.119220 2.464426 1.291976 ... 4.462061 0.211963 3.097346 0.179946 0.024523
1 3.399946 0.654546 0.136111 2.003238 2.233912 ... 4.708507 0.100548 2.800072 0.076992 0.000000
2 3.469919 0.873279 0.815520 2.537203 1.894536 ... 3.753501 0.103287 2.079879 0.659337 0.084467
3 2.821973 0.648664 0.515149 2.874994 1.227373 ... 4.472364 1.024236 2.113983 0.131032 0.234907
4 2.113599 3.069690 1.088918 2.917765 1.872202 ... 5.070540 0.802297 5.009232 0.480860 4.219627
.. ... ... ... ... ... ... ... ... ... ... ...
495 2.035607 1.083142 1.969201 2.342382 2.938695 ... 4.184614 0.393621 3.387899 0.278416 1.163159
496 2.455688 2.287689 3.733934 2.194991 2.633475 ... 5.299762 2.237891 4.531835 1.525031 0.342196
497 3.077339 2.696516 3.127983 2.612161 2.994722 ... 5.178988 2.594080 4.550842 2.330173 0.931543
498 4.398039 2.100173 3.080552 3.052334 2.392759 ... 4.791044 0.457323 3.195818 0.842771 3.581545
499 2.129841 1.899062 0.582394 2.604003 2.158955 ... 4.414640 0.269112 2.584676 0.116136 1.627639
[500 rows x 198 columns], 0 0
1 0
2 1
3 0
4 1
..
495 1
496 0
497 1
498 0
499 0
Name: 0, Length: 500, dtype: int64)>>> X,y = fs.read_path('D:\Internship\data.csv')
>>> X,y
( mean_radius mean_texture mean_perimeter mean_area mean_smoothness
0 17.99 10.38 122.80 1001.0 0.11840
1 20.57 17.77 132.90 1326.0 0.08474
2 19.69 21.25 130.00 1203.0 0.10960
3 11.42 20.38 77.58 386.1 0.14250
4 20.29 14.34 135.10 1297.0 0.10030
.. ... ... ... ... ...
564 21.56 22.39 142.00 1479.0 0.11100
565 20.13 28.25 131.20 1261.0 0.09780
566 16.60 28.08 108.30 858.1 0.08455
567 20.60 29.33 140.10 1265.0 0.11780
568 7.76 24.54 47.92 181.0 0.05263
[569 rows x 5 columns], 0 0
1 0
2 0
3 0
4 0
..
564 0
565 0
566 0
567 0
568 1
Name: diagnosis, Length: 569, dtype: int64)>>> X_feature_selected = fs.ForwardSelector(X,y)
>>> X_feature_selected
HS3ST1 TSPOAP1 CROT CRLF1 MAPK8IP2 PRICKLE3 SLC38A5
0 0.283871 1.118907 3.199585 0.602297 0.583563 2.728145 0.236678
1 0.778597 1.428139 1.861609 0.326153 0.860734 1.728958 0.138003
2 0.925642 0.928361 2.316242 0.061074 0.343178 2.808658 0.033121
3 2.460786 1.649740 2.610303 1.118434 1.612719 2.117057 0.101612
4 1.176990 0.769621 2.515964 0.164966 1.731133 2.934570 0.197540
.. ... ... ... ... ... ... ...
495 0.491308 0.722332 1.771241 0.525178 4.345678 2.425843 0.140930
496 0.749313 1.367277 2.886011 0.932400 2.602531 1.879879 1.344855
497 1.156488 1.327671 1.679746 0.672549 1.169465 2.129853 2.522259
498 0.622989 2.347412 4.090374 1.727858 2.867727 1.990572 0.845473
499 0.311338 2.481399 3.411503 0.381369 3.573363 2.402876 1.390478
[500 rows x 7 columns]>>> X_feature_selected = fs.Pipelined(X,y)
>>> X_feature_selected
CROT SLC38A5
0 3.199585 0.236678
1 1.861609 0.138003
2 2.316242 0.033121
3 2.610303 0.101612
4 2.515964 0.197540
.. ... ...
495 1.771241 0.140930
496 2.886011 1.344855
497 1.679746 2.522259
498 4.090374 0.845473
499 3.411503 1.390478
[500 rows x 2 columns]