def lasso_model_walk(X, Y, B=None, nr_steps=64, start=None, step=.9, tol=None, return_lams=False):
Y, mean = center(Y, axis=1)
Bs,lams = lasso_walk(X,Y, B, nr_steps, start, step, tol, return_lams=True)
models = [lasso_model(B, mean) for B in Bs]
if return_lams:
return models, lams
return models
def lasso_model_walk(X, Y, B=None, nr_steps=64, start=None, step=.9, tol=None, return_lams=False):
Y, mean = center(Y, axis=1)
Bs,lams = lasso_walk(X,Y, B, nr_steps, start, step, tol, return_lams=True)
models = [lasso_model(B, mean) for B in Bs]
if return_lams:
return models, lams
return models
def retrain(self, features, labels, lam, **kwargs):
features, mean = center(features)
betas = lasso(features,
labels,
self.betas.copy(),
lam=lam,
**_dict_subset(kwargs, ['tol', 'max_iter']))
return lasso_model(betas, mean)
def evaluate(lam):
predicted = []
for i in xrange(0,len(labels),3):
idx = np.ones(len(labels), bool)
idx[i:i+3] = 0
Y, Ymean = center(labels[idx].T, axis=1)
B,err = best_B(i,lam)
if err > 0:
B = lasso(features[idx].T, Y, B, lam=lam, tol=1e-3)
model = lasso_model(B, Ymean)
predicted.append(model.apply(features[i].T))
try:
predicted.append(model.apply(features[i+1]))
predicted.append(model.apply(features[i+2]))
except IndexError:
pass
models[i][lam] = B
predicted = np.array(predicted)
cur = 0.0
for p,ell in zip(predicted.T, labels.T):
corr,ps = spearnan_compare(p, ell)
cur += corr
print('evaluate({0}) = {1:.2%}'.format(lam, cur/labels.shape[1]))
return - cur / labels.shape[1]
def train(self, features, labels, betas=None, **kwargs):
labels, mean = center(labels, axis=1)
betas = lasso(features, labels, betas, lam=self.lam)
return lasso_model(betas, mean)
def train(self, features, labels, betas=None, **kwargs):
labels, mean = center(labels, axis=1)
betas = lasso(features, labels, betas, lam=self.lam)
return lasso_model(betas, mean)
def retrain(self, features, labels, lam, **kwargs):
features, mean = center(features)
betas = lasso(features, labels, self.betas.copy(), lam=lam, **_dict_subset(kwargs, ['tol', 'max_iter']))
return lasso_model(betas, mean)
def train(self, features, labels):
from milk.unsupervised import center
labels, mean = center(labels, axis=self.axis)
return self.base.train(features, labels)