def submeth(Idx):
if Idx == '0': # Select all features
def MFmeth(x): return select_all(x)
StrMeth = 'SelectAll'
if Idx == '1': # use a binomial law to select features
def MFmeth(x): return select_bino(
x, y, p=p, classifier=clfIn, **kwargs)
StrMeth = 'Binomial selection at p<'+str(p)
if Idx == '2': # use permutations to select features
def MFmeth(x): return select_perm(
x, y, p=p, classifier=clfIn, **kwargs)
StrMeth = 'Permutation selection at p<'+str(p)
# use 'forward'/'backward'/'exhaustive'to select features
if Idx == '3':
clf = classifier_choice(clfIn, n_tree=kwargs['n_tree'],
n_knn=kwargs['n_knn'],
kern=kwargs['kern'])
def MFmeth(x): return sequence_inner(clf, x, y,
direction=direction,
inner_folds=kwargs['n_folds'],
display=display)
StrMeth = direction+' feature selection'
if Idx == '4': # nbest features
def MFmeth(x): return select_nbest(
x, y, nbest=nbest, classifier=clfIn, **kwargs)
StrMeth = str(nbest)+' best features'
return MFmeth, StrMeth
def mf(x, y, Id=0, featList=None, clfIn=0, clfOut=0, p=0.05, combineGroup=False,
n_knn=3, n_tree=100, kern='rbf', cvIn='sss', n_foldsIn=10, repIn=1,
cvOut='skfold', n_foldsOut=3, repOut=10, probOccur='rpercent',
display=True, threshold=None, direction='forward', nbest=10):
"""Compute the multi-features contain in an array x, using the target
vector y. The Id serve to combine MF methods.
Parameters
----------
x : array-like
The features. Dimension [nfeat x ntrial]
y : array-like
The target variable to try to predict in the case of
supervised learning
Id : string
'0': No selection. All the features are used
'1': Select <p significant features using a binomial law
'2': Select <p significant features using permutations
'3': use 'forward'/'backward'/'exhaustive'to select features
clf : estimator object implementing 'fit'
The object to use to fit the data
p : float < 1, default: 0.05
The significiance level to select features
threshold : float, default: None
variable equivalent to p. If threshold is defined, the programm
will search for the p value associed
display : 'on' or 'off'
display informations
direction : string, optional, default: 'forward'
Use 'forward', 'backward' or 'exhaustive'
nbest : int, optional [def: 10]
For the Id 4, use this parameter to control the number of
features to select
**kwargs : dictionnary
Optional parameter for the classify function
Returns
-------
MFmeth : list
list of selected methods
MFstr : list
list of the name of the selected methods
"""
# Get size elements :
nfeat, ntrial = x.shape
if featList is None:
featList, combineGroup = [0] * nfeat, False
if threshold is not None:
p = binostatinv(y, threshold)
# Manage group of features :
groupinfo = combineInfo(x, featList, combineGroup=combineGroup)
# Define classifier option :
clfOutMod = classifier_choice(
clfOut, n_tree=n_tree, n_knn=n_knn, kern=kern)
# Keep the info :
setup = {'p': p, 'cvOut': cvOut, 'n_foldsOut': n_foldsOut,
'repOut': repOut, 'cvIn': cvIn, 'n_foldsIn': n_foldsIn,
'repIn': repIn, 'n_tree': n_tree, 'kern': kern,
'probOccur': probOccur, 'direction': direction, 'nbest': nbest}
# Run the MF model for each combinaition:
da, prob = [], []
for k in range(0, len(groupinfo)): # list(groupinfo['idx']):
if display:
print('=> Group : ' + groupinfo['feature'].iloc[k], end='\r')
daComb, probComb, MFstr = MFcv(
x[groupinfo['idx'].iloc[k], :], y, Id, clfOutMod, clfIn=clfIn,
display=display, **setup)
da.append(daComb), prob.append(probComb)
# Get final info on the classifier used :
setup['clfIn'], _ = classifier_string(
clfIn, n_tree=n_tree, n_knn=n_knn, kern=kern)
setup['clfOut'], _ = classifier_string(
clfOut, n_tree=n_tree, n_knn=n_knn, kern=kern)
# Complete the pandas Dataframe about group decoding
groupinfo['da'], groupinfo['occurrence'] = [
sum(k) / len(k) for k in da], prob
return da, prob, MFstr, groupinfo, setup
