def rerun_dfa(chrom,xdata,mask,groups,names,DFs):
"""Run DFA in min app"""
#extract vars from xdata
slice = meancent(_slice(xdata,chrom))
#split in to training and test
tr_slice,cv_slice,ts_slice,tr_grp,cv_grp,ts_grp,tr_nm,cv_nm,ts_nm=_split(slice,groups,mask,names)
#get indexes
idx = scipy.arange(xdata.shape[0])[:,nA]
tr_idx = scipy.take(idx,_index(mask,0),0)
cv_idx = scipy.take(idx,_index(mask,1),0)
ts_idx = scipy.take(idx,_index(mask,2),0)
#model DFA on training samples
u,v,eigs,dummy = cva(tr_slice,tr_grp,DFs)
#project xval and test samples
projUcv = scipy.dot(cv_slice,v)
projUt = scipy.dot(ts_slice,v)
uout = scipy.zeros((xdata.shape[0],DFs),'d')
_put(uout,scipy.reshape(tr_idx,(len(tr_idx),)).tolist(),u)
_put(uout,scipy.reshape(cv_idx,(len(cv_idx),)).tolist(),projUcv)
_put(uout,scipy.reshape(ts_idx,(len(ts_idx),)).tolist(),projUt)
return uout,v,eigs
def rerun_dfa(chrom, xdata, mask, groups, names, DFs):
"""Run DFA in min app"""
#extract vars from xdata
slice = meancent(_slice(xdata, chrom))
#split in to training and test
tr_slice, cv_slice, ts_slice, tr_grp, cv_grp, ts_grp, tr_nm, cv_nm, ts_nm = _split(
slice, groups, mask, names)
#get indexes
idx = scipy.arange(xdata.shape[0])[:, nA]
tr_idx = scipy.take(idx, _index(mask, 0), 0)
cv_idx = scipy.take(idx, _index(mask, 1), 0)
ts_idx = scipy.take(idx, _index(mask, 2), 0)
#model DFA on training samples
u, v, eigs, dummy = cva(tr_slice, tr_grp, DFs)
#project xval and test samples
projUcv = scipy.dot(cv_slice, v)
projUt = scipy.dot(ts_slice, v)
uout = scipy.zeros((xdata.shape[0], DFs), 'd')
_put(uout, scipy.reshape(tr_idx, (len(tr_idx), )).tolist(), u)
_put(uout, scipy.reshape(cv_idx, (len(cv_idx), )).tolist(), projUcv)
_put(uout, scipy.reshape(ts_idx, (len(ts_idx), )).tolist(), projUt)
return uout, v, eigs
def call_dfa(chrom,xdata,DFs,mask,data):
"""Runs DFA on subset of variables from "xdata" as
defined by "chrom" and returns a vector of fitness
scores to be fed back into the GA
"""
Y = []
for x in range(len(chrom)):
if _remdup(chrom[x]) == 0:
#extract vars from xdata
slice = meancent(_slice(xdata,chrom[x]))
collate = 0
for nF in range(mask.shape[1]):
#split in to training and test
tr_slice,cv_slice,ts_slice,tr_grp,cv_grp,ts_grp,tr_nm,cv_nm,ts_nm=_split(slice,
data['class'][:,0],mask[:,nF].tolist(),data['label'])
try:
u,v,eigs,dummy = cva(tr_slice,tr_grp,DFs)
projU = scipy.dot(cv_slice,v)
u = scipy.concatenate((u,projU),0)
group2 = scipy.concatenate((tr_grp,cv_grp),0)
B,W = _BW(u,group2)
L,A = scipy.linalg.eig(B,W)
order = _flip(scipy.argsort(scipy.reshape(L.real,(len(L),))))
Ls = _flip(scipy.sort(L.real))
eigval = Ls[0:DFs]
collate += sum(eigval)
except:
continue
if collate != 0:
Y.append(float(mask.shape[1])/collate)
else:
Y.append(10.0**5)
else:
Y.append(10.0**5)
return scipy.array(Y)[:,nA]
def call_dfa(chrom, xdata, DFs, mask, data):
"""Runs DFA on subset of variables from "xdata" as
defined by "chrom" and returns a vector of fitness
scores to be fed back into the GA
"""
Y = []
for x in range(len(chrom)):
if _remdup(chrom[x]) == 0:
#extract vars from xdata
slice = meancent(_slice(xdata, chrom[x]))
collate = 0
for nF in range(mask.shape[1]):
#split in to training and test
tr_slice, cv_slice, ts_slice, tr_grp, cv_grp, ts_grp, tr_nm, cv_nm, ts_nm = _split(
slice, data['class'][:, 0], mask[:, nF].tolist(),
data['label'])
try:
u, v, eigs, dummy = cva(tr_slice, tr_grp, DFs)
projU = scipy.dot(cv_slice, v)
u = scipy.concatenate((u, projU), 0)
group2 = scipy.concatenate((tr_grp, cv_grp), 0)
B, W = _BW(u, group2)
L, A = scipy.linalg.eig(B, W)
order = _flip(
scipy.argsort(scipy.reshape(L.real, (len(L), ))))
Ls = _flip(scipy.sort(L.real))
eigval = Ls[0:DFs]
collate += sum(eigval)
except:
continue
if collate != 0:
Y.append(float(mask.shape[1]) / collate)
else:
Y.append(10.0**5)
else:
Y.append(10.0**5)
return scipy.array(Y)[:, nA]
