def ordinary_logistic(data):
X = data.get_gene_exp_matrix()
y = data.get_labels()
logReg = LogisticRegression(C=1000000,tol=0.01,dual=True)#large C is less regularization, according to their docs (what is C?)
logis_eval = kFoldCrossValid(X,y,logReg)
eval_sum = reduce(lambda x,y: [a+b for a,b in zip(x,y)],logis_eval)
eval_avg = map(lambda x: x/len(logis_eval),eval_sum)
print(eval_avg)
def main():
data = TCGAData()
gene_exp = data.get_gene_exp_matrix()
labels = data.get_labels()
gnb = GaussianNB()
accuracy = kFoldCrossValid(gene_exp,labels,gnb)
print(accuracy)
def svmfn(featureSelectionMethod = 'none'):
data = TCGAData()
gene_exp = data.get_gene_exp_matrix()
labels = data.get_labels()
names = data.get_gene_names()
clf = svm.SVC(gamma=0.001,C=100.) #these are the values in some random example, idk what C is
accuracy = kFoldCrossValid(gene_exp,labels,clf,k=4,names=names,selection=featureSelectionMethod)
print(accuracy)
def with_l1_penalty(data,C_list):#data should be TCGAData object
"""Adapted from sklearn website,
# Authors: Alexandre Gramfort <*****@*****.**>
# Mathieu Blondel <*****@*****.**>
# Andreas Mueller <*****@*****.**>
# License: BSD Style.
"""
X = data.get_gene_exp_matrix()
y = data.get_labels()
evaluations = []
sparsityList = []
for C in C_list:
l1_logReg = LogisticRegression(C=C, penalty='l1', tol=0.01)
sparsities = kFoldGetSparsity(data,l1_logReg)
sparsity = sum(sparsities)/len(sparsities)
sparsityList.append(sparsity)
print "C=%.4f" % C
print "Sparsity with L1 penalty: %.2f%%" % sparsity
#print "Sparsity with L2 penalty: %.2f%%" % sparsity_l2_LR
#print "score with L2 penalty: %.4f" % l2_logReg.score(X, y)
l1_eval = kFoldCrossValid(X,y,l1_logReg)
l1_eval_sum = reduce(lambda x,y: [a+b for a,b in zip(x,y)],l1_eval)
l1_eval_avg = map(lambda x: x/len(l1_eval),l1_eval_sum)
evaluations.append(l1_eval_avg)
#l2_accuracy = kFoldCrossValid(X,y,l2_logReg)
#print(l2_accuracy)
C_list = np.array(C_list)
print(C_list)
precisions = [100*evaluations[i][1] for i in range(0,len(C_list))]
print(precisions)
recalls = [100*evaluations[i][2] for i in range(0,len(C_list))]
print(recalls)
fig = pl.figure()
precis_recall = fig.add_subplot(211)
sparse = fig.add_subplot(212)
precis_recall.set_title('Precision/Recall with Varying C')
precis_recall.plot(C_list,precisions,color="red")
precis_recall.plot(C_list,recalls,color="blue")
precis_recall.set_xlabel("C")
precis_recall.set_ylabel("Precision (red)/Recall (blue)")
sparse.set_title('Sparsity with Varying C')
sparse.plot(C_list,sparsityList,color="black")
sparse.set_xlabel("C")
sparse.set_ylabel("Sparsity")
pl.show()
