def rec_feature_elim(data,num_features=17700):
X = data.get_gene_exp_matrix()
y = data.get_labels()
svc = SVC(kernel="linear", C=1)
rfe = RFE(estimator=svc, n_features_to_select=num_features, step=1)
selector = rfe.fit(X, y)
mask = map(lambda x: 1 if x is True else 0,selector.support_)
print_genes_nonzero_coeff(data,mask)
def kFoldGetSparsity(data,logregAlgo,k=4):
print "--------------------------------------------"
X = data.get_gene_exp_matrix()
Y = data.get_labels()
kf = cross_validation.KFold(len(X), k=k,shuffle=True)
sparsity = []
for train_index, test_index in kf:
X_train, X_test = [X[i] for i in train_index], [X[i] for i in test_index]
y_train, y_test = [Y[i] for i in train_index], [Y[i] for i in test_index]
logregAlgo.fit(X_train,y_train)
coeffs = logregAlgo.coef_.ravel()
print print_genes_nonzero_coeff(data,coeffs)
sparsity.append(np.mean(coeffs==0)*100)
return(sparsity)
def rec_feature_elim_with_KFold(data):
"""Recursive feature elimination
FIXME: How to pick a kernel?
WARNING: ridiculously slow?
"""
X = data.get_gene_exp_matrix()
y = data.get_labels()
# Create the RFE object and compute a cross-validated score.
svc = SVC(kernel="linear")
rfecv = RFECV(estimator=svc, step=1, cv=StratifiedKFold(y, 2),loss_func=zero_one)
selector = rfecv.fit(X, y)
mask = map(lambda x: 1 if x is True else 0,selector.support_)
print_genes_nonzero_coeff(data,mask)
print "Optimal number of features : %d" % rfecv.n_features_
