def cv_train(self, X, Y, regMin=1e-30, regMax=1e+30, K=100):
patience = 100
regMinStart = regMin
regMaxStart = regMax
BETAs = []
time_start = time.time()
time_diffs = []
minFactor = 1
maxFactor = 1
if self.realDataFlag:
minFactor = 0.75
maxFactor = 1.25
for clf in [Lasso()]: #, MCP(), SCAD()]:
betaM = None
regMin = regMinStart
regMax = regMaxStart
iteration = 0
while regMin < regMax and iteration < patience:
iteration += 1
reg = np.exp((np.log(regMin) + np.log(regMax)) / 2.0)
# print("Iter:{}\tlambda:{}".format(iteration, lmbd), end="\t")
clf.setLambda(reg)
clf.setLearningRate(self.learningRate)
clf.fit(X, Y)
beta = clf.getBeta()
k = len(np.where(beta != 0)[0])
if k < minFactor * K: # Regularizer too strong
regMax = reg
if betaM is None:
betaM = beta
elif k > maxFactor * K: # Regularizer too weak
regMin = reg
betaM = beta
else:
betaM = beta
break
BETAs.append(
np.abs(betaM)
) # make sure this is absolute value for evaluation ROC curve
time_diffs.append(time.time() - time_start)
time_start = time.time()
return BETAs, time_diffs
def train_lasso(self, X, y): #, mu):
lasso = Lasso()
lasso.fit(X, y)
return lasso.getBeta()
