def CrossValidateSMOTE(data, labels, clf, folds=10, runSMOTE=True):
from unbalanced_dataset import SMOTE
from sklearn.metrics import confusion_matrix as confmat
from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score
columns = []
if type(data) is not np.ndarray:
data = data.as_matrix()
if type(labels) is not np.ndarray:
labels = labels.as_matrix().ravel()
skf = StratifiedKFold(labels,n_folds=folds, shuffle=False)
sets = [{'train':train, 'test':test} for train, test in skf]
acc = []
fmeasure = []
recall = []
precision = []
cm = np.array([0, 0, 0, 0]).reshape(2,2)
for fold in sets:
data_train = data[fold['train']]
labels_train = labels[fold['train']]
bugs = sum(labels_train)
ratio = float(len(labels_train)-bugs)/bugs
data_test = data[fold['test']]
labels_test = labels[fold['test']]
if runSMOTE:
smote = SMOTE(ratio=ratio, verbose=False, kind='borderline1')
data_train, labels_train = smote.fit_transform(data_train,labels_train)
clf.fit(data_train, labels_train)
hypot = clf.predict(data_test)
acc.append(accuracy_score(hypot, labels_test))
fmeasure.append(f1_score(hypot, labels_test))
recall.append(recall_score(hypot, labels_test))
precision.append(precision_score(hypot, labels_test))
cm += confmat(labels_test, hypot)
return acc, fmeasure, recall, precision, cm
def doNetRun(DoPretrain, actType, numHidden, numNodes, dropOut, NumReps,
AdjustWforDropout, L1, L2,
LearningRate, Momentum, Algorithm, maxUpdate, BatchSize, Patience,
MinImprovement, ValidateEveryN):
'''Performs *numRep* neural network runs, saving the best run so far
(global var)
:params: NN hyper-parameters.
:returns: run parameters + performance measures (DataFrame).'''
layerDef = dict(size=numNodes, activation=actType)
netDef = []
netDef.append(numFeatures)
for _ in range(numHidden):
netDef.append(layerDef)
netDef.append(numCats)
frame = inspect.currentframe()
args, _, _, values = inspect.getargvalues(frame)
saveCols = []
for arg in args:
print arg, ': ', values[arg], ',',
saveCols.append(arg)
print
saveCols = saveCols + \
['TrLoss', 'VldLoss', 'TstLoss', 'TestAcc', 'Time', 'BestEpoch']
hist = pd.DataFrame(columns=saveCols)
for _ in range(NumReps):
global kountRuns
kountRuns = kountRuns + 1
# use new seed for each run
ii32 = np.iinfo(np.int32)
seed = random.randint(0, ii32.max)
print 'seed: ', seed
# although numpy.random.seed should be uint32, Theanets only checks for
# int
# and fails if given a uint32 stored as a long in Python
net = thts.Classifier(layers=netDef, rng=seed)
t0 = time.clock()
Epoch = 0
if DoPretrain:
print('Train phase I:')
net.train(train, valid,
patience=Patience,
learning_rate=LearningRate,
momentum=Momentum,
min_improvement=MinImprovement,
validate_every=ValidateEveryN,
max_updates=maxUpdate,
input_dropout=dropOut,
hidden_dropout=dropOut,
algo='layerwise',
weight_l1=L1, # L1 norm sparsity
weight_l2=L2, # L2 norm weight decay
batch_size=BatchSize)
print('Train phase II:')
Epoch = 0
lastLoss = np.Inf
lastEpoch = 0
for tr, vl in net.itertrain(train, valid,
patience=Patience,
learning_rate=LearningRate,
momentum=Momentum,
min_improvement=MinImprovement,
validate_every=ValidateEveryN,
max_updates=maxUpdate,
input_dropout=dropOut,
hidden_dropout=dropOut,
algo=Algorithm,
weight_l1=L1, # L1 norm sparsity
weight_l2=L2, # L2 norm weight decay
batch_size=BatchSize):
Epoch = Epoch + 1
vloss = vl['loss']
if (lastLoss - vloss) >= MinImprovement:
lastLoss = vloss
lastEpoch = Epoch
flg = ' *' + str(lastEpoch)
else:
flg = ''
print Epoch, 'trLoss: %.4f' % tr['loss'], ' vlLoss: %.4f' % vloss, \
' vlacc: %.4f' % vl['acc'], flg
t1 = time.clock() - t0
print 'Time: ', t1, ' Epochs:', Epoch
if AdjustWforDropout:
fact = 1.0 - dropOut
for ll in net.layers:
if (ll.name != 'in'):
w = net.find(ll.name, 'w')
w.set_value(w.get_value() * fact)
X, y = train
trnLoss = log_loss(y, net.predict_proba(X))
X, y = valid
vldLoss = log_loss(y, net.predict_proba(X))
X, y = test
ypp = net.predict_proba(X)
yp = net.predict(X)
acc = net.score(X, y)
tstLoss = log_loss(y, ypp)
print Epoch, 'trLoss: %.4f' % trnLoss, ' vlLoss: %.4f' % vldLoss, \
' vlacc: %.4f' % acc
print 'Best Epoch: ', lastEpoch
cf = confmat(y, yp)
print 'Test-set confusion matrix:'
print cf
global bestLoss
global bestParams
dta = dict()
for arg in args:
dta[arg] = values[arg]
dta['TrLoss'] = trnLoss
dta['VldLoss'] = vldLoss
dta['TstLoss'] = tstLoss
dta['TestAcc'] = acc
dta['Time'] = t1
dta['BestEpoch'] = lastEpoch
nr = pd.DataFrame([dta])
if (tstLoss <= bestLoss):
bestLoss = tstLoss
net.save('bestModel')
bestParams = nr
hist = hist.append(nr, ignore_index=True)
# re-order columns...
hist = hist[saveCols]
return hist
def fit(self,
dataloaders,
criterion,
optimizer,
scheduler,
num_epochs=(0, 500)):
#pdb.set_trace()
epoch_stats = Stats(phases=['train', 'val'])
dataset_sizes = {
x: len(dataloaders[x].dataset)
for x in ['train', 'val']
}
# class_names = dataloaders['train'].dataset.classes
#best_model_W = copy.deepcopy(self.model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs[0], num_epochs[1]):
for phase in ['train', 'val']:
if phase == 'train':
scheduler.step()
self.model.train() # Set model to training mode
else:
self.model.eval() # Set model to evaluate mode
running_loss_epoch = 0.0
running_corrects_epoch = 0.0
running_loss = 0.0
# Iterate over data.
y_preds = torch.LongTensor().to(self.processor)
y_trues = torch.LongTensor().to(self.processor)
for i, (inputs, labels) in enumerate(dataloaders[phase]):
inputs = inputs.to(self.processor)
labels = labels.to(self.processor)
# zero the parameter gradients
optimizer.zero_grad()
# forward
with torch.set_grad_enabled(phase == 'train'):
outputs = self.model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 99: # print every 50 minibatches
print('[%d, %d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
running_loss_epoch += loss.item() * inputs.size(0)
running_corrects_epoch += torch.sum(preds == labels).item()
#pdb.set_trace()
y_preds = torch.cat((y_preds, preds), dim=0)
y_trues = torch.cat((y_trues, labels), dim=0)
epoch_loss = running_loss_epoch / dataset_sizes[phase]
epoch_acc = running_corrects_epoch / dataset_sizes[phase]
print(f'{self.model_name} at {self.model_dir}')
print('Epoch #{} {} Loss: {:.3f}'.format(
epoch + 1, phase, epoch_loss))
print('Epoch #{} {} Accuracy: {:.3f}'.format(
epoch + 1, phase, epoch_acc))
print(confmat(y_trues.cpu().numpy(), y_preds.cpu().numpy()))
# keeping track of the epoch progress
epoch_stats(phase, epoch_loss, epoch_acc)
epoch_stats.write2file(self.model_dir)
# deep copy the best model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
self.save_model()
# write model to file at every 50 epochs.
if epoch % 50 == 49:
self.save_model(epoch + 1)
