Feature Selection

Feature_scML fs func

fs module is feature selection function, including F-score algorithm (fscore), the squared coefficient of variation (cv2), principal component analysis (pca), random forest classifier (rfc), ANOVA Pvalue (ano), the maximal information coefficient measure (mic), ReliefF (turf), and linear support vector machine (linearsvm). These methods are referred in the index.

$Feature_scML fs -h
usage: fs

optional arguments:
-h, --help            show this help message and exit
-i INPUT, --input INPUT
                        Input train data (CSV)
-m {fscore,pca,cv2,rfc,mic,turf,linearsvm}, --method {fscore,pca,cv2,rfc,mic,turf,linearsvm}
                        Feature selection method
-o OUTPUT, --output OUTPUT
                        Output directory
--njobs NJOBS         The number of jobs to run in parallel (Only turf,
                        default=1)

Command

Parameters

optional

Descripton

–method, -m

fscore, pca, cv2, rfc, mic, turf, linearsvm

  • fscore (F-score algorithm)

  • pca (principal component analysis)

  • cv2 (squared coefficient of variation)

  • rfc (random forest classifier)

  • mic (the maximal information coefficient)

  • turf (ReliefF)

  • linearsvm (linear support vector machine)

–input,-i

filename path

input filename path (CSV format)

–output, -o

output directory

output directory (default:Current directory)

–njobs

int, default=1

The number of jobs to run in parallel

Example

# default start, end, and step
$Feature_scML fs -i example.csv -m cv2

The result will generate two dataframe. As follow:

example_cv2.csv

Feature

cv2_score

CCKBR

122.898

IFITM1

15.624

CLDN10

11.051

…

…

The dataframe sorted by the features of the original data.

example_cv2_data.csv

Label

CCKBR

IFITM1

…

6

229.00

32.21

…

3

25.14

76.82

…

6

154.65

52.62

…