def fitData(self, data, batch_size=100, n_epochs=10, print_every=10,
valdata=None):
'''
fit a model to x, y data by batch
print_every is 0 if do not wish to print
'''
time_start = time.time()
losses = []
best_valloss, best_valindex = np.inf, 0
vallosses = []
n = len(data.dataset)
cost = 0
for epoch in range(n_epochs):
for k, (x_batch, y_batch) in enumerate(data):
x_batch, y_batch = to_var(x_batch), to_var(y_batch)
y_hat, regret = self.step(x_batch, y_batch)
m = x_batch.size(0)
cost += 1 / (k+1) * (regret/m - cost)
if print_every != 0 and k % print_every == 0:
losses.append(cost)
# progress, time, avg loss, auc
to_print = ('%.2f%% (%s) %.4f %.4f' % ((epoch * n + (k+1) * m) /
(n_epochs * n) * 100,
timeSince(time_start),
cost,
model_auc(self.model,
data)))
if valdata is not None:
valloss = calc_loss(self.model, valdata, self.loss)
vallosses.append(valloss)
np.save('models/%s.valloss' % self.name, vallosses)
to_print += " %.4f" % model_auc(self.model, valdata)
if valloss <= best_valloss:
best_valloss = valloss
best_valindex = len(vallosses) - 1
torch.save(self.model, 'models/%s.pt' % self.name)
else:
torch.save(self.model, 'models/%s.pt' % self.name)
print(to_print)
np.save('models/%s.loss' % self.name, losses)
cost = 0
return losses, vallosses
def run(self, n_bootstrap=100):
#map_parallel(trainData, self.tasks, self.n_cpus)
for task in self.tasks:
trainData(*task)
# select a model to run: split on auc and sparsity
aucs = []
models = []
sparsities = []
for name, reg, alpha in self.hyperparams:
# load the model
model = torch.load('models/' + name + '.pt')
sp = modelSparsity(model, self.valdata)
models.append(model)
sparsities.append(sp)
for _ in range(n_bootstrap):
test = bootstrap(self.valdata)
local_aucs = []
for model in models:
# bootstrap for CI on auc
local_aucs.append(model_auc(model, test))
aucs.append(local_aucs)
aucs = np.array(aucs)
# only keep those with high auc
b = np.argmax(aucs.mean(0))
discardset = set([])
for a in range(len(models)):
diffs = ((aucs[:, a] - aucs[:, b]) >= 0).astype(np.int)
if diffs.sum() / diffs.shape[0] <= 0.05:
discardset.add(a)
# choose the one with largest sparsity
chosen, sp = max(filter(lambda x: x[0] not in discardset,
enumerate(sparsities)),
key=lambda x: x[1])
# retrian the chosen model
name, reg, alpha = self.hyperparams[chosen]
print('name', name)
trainData(name,
self.data,
reg,
alpha,
p.epochs,
p.lr,
p.batch_size,
p.log_interval,
test=True)
def select_on_auc(self, *args, **kwargs):
'''hyperparameter selection based on auc alone
return index within self.hyperparams that need to retrain
'''
print('hyperparam select using auc')
aucs = []
sparsities = []
for name, reg, alpha in self.hyperparams:
# load the model
model = torch.load('models/' + name + '.pt')
aucs.append(model_auc(model, self.valdata))
# choose the one with largest auc
chosen, auc = max(enumerate(aucs),
key=lambda x: x[1])
return chosen
def trainData(name, data, regularization=eye_loss, alpha=0.01, n_epochs=300,
learning_rate=1e-3, batch_size=4000, r=None, test=False):
'''
return validation auc, average precision, score1
if test is true, combine train and val and report on test performance
'''
m = data
if test:
name = 'test' + name
xtrain = np.vstack([m.xtrain, m.xval])
xval = m.xte
ytrain = np.hstack([m.ytrain, m.yval])
yval = m.yte
else:
xtrain = m.xtrain
xval = m.xval
ytrain = m.ytrain
yval = m.yval
# note: for cross validation, just split data into n fold and
# choose appropriate train_data and valdata from those folds
# not doing here for simplicity
d = m.r.size(0)
train_data = TensorDataset(*map(lambda x: x.data, prepareData(xtrain, ytrain)))
data = DataLoader(train_data, batch_size=batch_size, shuffle=True)
valdata = TensorDataset(*map(lambda x: x.data, prepareData(xval, yval)))
valdata = DataLoader(valdata, batch_size=4000, shuffle=True)
n_output = 2 # binary classification task
model = LR(d, n_output)
reg_parameters = model.i2o.weight
t = Trainer(model, lr=learning_rate, risk_factors=m.r, alpha=alpha,
regularization=regularization, reg_parameters=reg_parameters,
name=name)
losses, vallosses = t.fit(data, n_epochs=n_epochs, print_every=1, valdata=valdata)
# report statistics
val_auc = model_auc(model, valdata)
ap = calcAP(m.r.data.numpy(), (reg_parameters[1] - reg_parameters[0]).data.numpy())
t, s1 = sweepS1(model, valdata)
sp = sparsity((reg_parameters[1]-reg_parameters[0]).data.numpy())
joblib.dump((val_auc, ap, s1, sp), 'models/' + name + '.pkl')
return val_auc, ap, s1, sp
def select_on_auc_sp(self, n_bootstrap=100):
'''
return the index in self.hyperparams chosen
hyper parameter selection based on auc and sp
choose the hyper parameter that has no auc difference with top
but is the sparsest model
This is the criteria used in the learning credible models paper
'''
print('hyperparam select using auc and sparsity')
aucs = []
models = []
sparsities = []
for name, reg, alpha in self.hyperparams:
# load the model
model = torch.load('models/' + name + '.pt')
reg_parameters = model.i2o.weight
sp = sparsity((reg_parameters[1]-reg_parameters[0]).data.numpy())
models.append(model)
sparsities.append(sp)
for _ in range(n_bootstrap):
test = bootstrap(self.valdata)
local_aucs = []
for model in models:
# bootstrap for CI on auc
local_aucs.append(model_auc(model, test))
aucs.append(local_aucs)
aucs = np.array(aucs)
# only keep those with high auc
b = np.argmax(aucs.mean(0))
discardset = set([])
for a in range(len(models)):
diffs = ((aucs[:,a] - aucs[:,b]) >= 0).astype(int)
if diffs.sum() / diffs.shape[0] <= 0.05:
discardset.add(a)
# choose the one with largest sparsity
chosen, sp = max(filter(lambda x: x[0] not in discardset,
enumerate(sparsities)),
key=lambda x: x[1])
return chosen
def select_on_auc_ap(self, n_bootstrap=100):
'''
return the index in self.hyperparams chosen
hyper parameter selection based on auc and ap
choose the hyper parameter that has no auc difference with top
but is the model with highest average precision (align with expert)
'''
print('hyperparam select using auc and ap')
aucs = []
models = []
aps = []
for name, reg, alpha in self.hyperparams:
# load the model
model = torch.load('models/' + name + '.pt')
reg_parameters = model.i2o.weight
ap = calcAP(self.data.r.data.numpy(),
(reg_parameters[1] - reg_parameters[0]).data.numpy())
models.append(model)
aps.append(ap)
for _ in range(n_bootstrap):
test = bootstrap(self.valdata)
local_aucs = []
for model in models:
# bootstrap for CI on auc
local_aucs.append(model_auc(model, test))
aucs.append(local_aucs)
aucs = np.array(aucs)
# only keep those with high auc
b = np.argmax(aucs.mean(0))
discardset = set([])
for a in range(len(models)):
diffs = ((aucs[:,a] - aucs[:,b]) >= 0).astype(int)
if diffs.sum() / diffs.shape[0] <= 0.05:
discardset.add(a)
# choose the one with largest average precision
chosen, ap = max(filter(lambda x: x[0] not in discardset,
enumerate(aps)),
key=lambda x: x[1])
return chosen
