BackofenLab / biofilm

make it easy to generate a FILtering Models for BIOlogical data

install

get conda forge in the channel list
get compiler stuff for auto-sklearn installed

conda install -c smautner biofilm

Feature selection is already nice:

go to biofilm and run python biofilm-features.py -h

# options for feature selection:
--method str lasso  or svm or all or corr or variance
--out str numpycompressdumpgoeshere
--plot bool False
--svmparamrange float+ 0.01 0.15 0.001

# theese are the options for reading data
--infile str myNumpyDump
--randinit int -1
--folds int 5
--subsample int -1
--Z bool False

lets make an overview of how things talk to each other:

data loading

a) tools.ndumpfile([X,y, featurenames, instancenames],fname) where feature and instancenames are optional or b) provide --loader whose read function will be called

defaultformat: X,y in a npz dump, features and instances get enumerated a custom dataloader: X,y, features, instances loadfoldsreturns: (X,Y,x,y) features namesOfTestInstances

outputs

optimize: out.model: {score:score, modelparams:modelparams} out.csv: instanceId, reallabel, predicted label, probability feature selection: out: featuremask, featureproba, featureId