def test_update_statistics_progress_within_statistics(self, m):
statistics = Statistics(progress_percentage=0.5)
statistics.add_stage(name='other_stage', key1='value1')
self.client.update_statistics(statistics)
self.assertEqual(m.call_count, 1)
url = '{}/organizations/{}/training/definitions/{}/jobs/{}/statistics'.format(
ABEJA_API_URL, ORGANIZATION_ID, TRAINING_JON_DEFINITION_NAME, TRAINING_JOB_ID)
expected_data = {
'statistics': {
'progress_percentage': 0.5,
'stages': {
'other_stage': {
'key1': 'value1'
}
}
}
}
m.assert_called_with(
'POST',
url,
params=None,
headers={
'User-Agent': 'abeja-platform-sdk/{}'.format(VERSION)},
timeout=30,
data=None,
json=expected_data)
def handler(context):
iris = datasets.load_iris()
cls = catboost.CatBoostClassifier(loss_function='MultiClass')
X = iris.data
y = iris.target
data_train, data_test, label_train, label_test = model_selection.train_test_split(
X, y)
cls.fit(data_train, label_train)
train_acc = cls.score(data_train, label_train)
test_acc = cls.score(data_test, label_test)
statistics = ABEJAStatistics(num_epochs=1, epoch=1)
statistics.add_stage(ABEJAStatistics.STAGE_TRAIN, train_acc, None)
statistics.add_stage(ABEJAStatistics.STAGE_VALIDATION, test_acc, None)
print(train_acc, test_acc)
try:
client.update_statistics(statistics)
except Exception:
pass
cls.save_model(os.path.join(ABEJA_TRAINING_RESULT_DIR, 'iris.mlmodel'),
format='coreml',
export_parameters={'prediction_type': 'probability'})
def handler(context):
iris = datasets.load_iris()
X = iris.data
y = iris.target
data_train, data_test, label_train, label_test = model_selection.train_test_split(
X, y)
clf = svm.SVC()
clf.fit(data_train, label_train)
train_acc = clf.score(data_train, label_train)
test_acc = clf.score(data_test, label_test)
statistics = ABEJAStatistics(num_epochs=1, epoch=1)
statistics.add_stage(ABEJAStatistics.STAGE_TRAIN, train_acc, None)
statistics.add_stage(ABEJAStatistics.STAGE_VALIDATION, test_acc, None)
print(train_acc, test_acc)
try:
client.update_statistics(statistics)
except Exception:
pass
joblib.dump(clf, os.path.join(ABEJA_TRAINING_RESULT_DIR, 'model.pkl'))
def handler(context):
data = fetch_movielens(min_rating=5.0)
model = LightFM(loss='warp')
epochs = 50
for epoch in range(1, epochs + 1):
print('Epoch: {}'.format(epoch))
model.fit_partial(data['train'], epochs=1, num_threads=1)
train_acc = precision_at_k(model, data['train'], k=5).mean()
test_acc = precision_at_k(model, data['test'], k=5).mean()
print("Train precision: {}".format(train_acc))
print("Test precision: {}".format(test_acc))
statistics = ABEJAStatistics(num_epochs=epochs, epoch=epoch)
statistics.add_stage(ABEJAStatistics.STAGE_TRAIN, float(train_acc),
None)
statistics.add_stage(ABEJAStatistics.STAGE_VALIDATION, float(test_acc),
None)
try:
client.update_statistics(statistics)
except Exception:
pass
np.save(os.path.join(ABEJA_TRAINING_RESULT_DIR, 'model.npy'),
model.__dict__)
def on_epoch_end(self, epoch, logs=None):
epochs = self.params['epochs']
statistics = ABEJAStatistics(num_epochs=epochs, epoch=epoch + 1)
statistics.add_stage(ABEJAStatistics.STAGE_TRAIN, logs['acc'], logs['loss'])
statistics.add_stage(ABEJAStatistics.STAGE_VALIDATION, logs['val_acc'], logs['val_loss'])
try:
self.client.update_statistics(statistics)
except Exception:
logger.warning('failed to update statistics.')
def __call__(self, epoch, train_loss, train_acc, val_loss, val_acc):
statistics = ABEJAStatistics(num_epochs=self._total_epochs,
epoch=epoch)
statistics.add_stage(ABEJAStatistics.STAGE_TRAIN, train_acc,
train_loss)
statistics.add_stage(ABEJAStatistics.STAGE_VALIDATION, val_acc,
val_loss)
try:
self.client.update_statistics(statistics)
except Exception:
logger.warning('failed to update statistics.')
def handler(context):
"""
the following csv file should be stored in the datalake channel.
---
sepal_lenght (cm), sepal_width (cm), petal_lenght (cm), petal_width (cm), target
float, float, float, float, int
---
"""
iris = datasets.load_iris()
file_path = load_latest_file_from_datalake(channel_id)
data = pd.read_csv(file_path, sep=',')
X = data[iris.feature_names].values.astype('float64')
Y = data['target'].values.astype('int64')
print('successfully load datalake channel file.')
# train test split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.7)
# define model
model = LogisticRegression(solver='lbfgs',
C=c,
multi_class='multinomial',
max_iter=epochs)
# train model
model.fit(X_train, Y_train)
# evaluate model
train_acc = accuracy_score(Y_train, model.predict(X_train))
train_loss = log_loss(Y_train, model.predict_proba(X_train))
valid_acc = accuracy_score(Y_test, model.predict(X_test))
valid_loss = log_loss(Y_test, model.predict_proba(X_test))
# update ABEJA statisctics
train_client = TrainClient()
statistics = ABEJAStatistics(num_epochs=epochs, epoch=epochs)
statistics.add_stage(name=ABEJAStatistics.STAGE_TRAIN,
accuracy=train_acc,
loss=train_loss)
statistics.add_stage(name=ABEJAStatistics.STAGE_VALIDATION,
accuracy=valid_acc,
loss=valid_loss)
train_client.update_statistics(statistics)
print('Train accuracy is {:.3f}.'.format(train_acc))
print('Train loss is {:.3f}.'.format(train_loss))
print('Valid accuracy is {:.3f}.'.format(valid_acc))
print('Valid loss is {:.3f}.'.format(valid_loss))
# save model
joblib.dump(model, os.path.join(ABEJA_TRAINING_RESULT_DIR, 'model.pkl'))
def _print(self, observation):
train_loss = None
train_acc = None
val_loss = None
val_acc = None
train_list = {}
val_list = {}
epoch = observation[self._obs_key]
statistics = ABEJAStatistics(num_epochs=self._total_epochs,
epoch=epoch)
for key, value in observation.items():
keys = key.split('/')
if len(keys) > 1 and keys[0] == 'main':
name = '/'.join(keys[1:])
if name == 'loss':
train_loss = value
elif name == 'accuracy':
train_acc = value
else:
train_list[name] = value
elif len(keys
) > 2 and keys[0] == 'validation' and keys[1] == 'main':
name = '/'.join(keys[2:])
if name == 'loss':
val_loss = value
elif name == 'accuracy':
val_acc = value
else:
val_list[name] = value
statistics.add_stage(ABEJAStatistics.STAGE_TRAIN, train_acc,
train_loss, **train_list)
statistics.add_stage(ABEJAStatistics.STAGE_VALIDATION, val_acc,
val_loss, **val_list)
try:
self.client.update_statistics(statistics)
except Exception:
logger.warning('failed to update statistics.')
def test_update_statistics(self, m):
statistics = Statistics(progress_percentage=0.5, epoch=1,
num_epochs=5, key1='value1')
statistics.add_stage(
name=Statistics.STAGE_TRAIN,
accuracy=0.9,
loss=0.05)
statistics.add_stage(name=Statistics.STAGE_VALIDATION,
accuracy=0.8, loss=0.1, key2=2)
self.client.update_statistics(statistics)
self.assertEqual(m.call_count, 1)
url = '{}/organizations/{}/training/definitions/{}/jobs/{}/statistics'.format(
ABEJA_API_URL, ORGANIZATION_ID, TRAINING_JON_DEFINITION_NAME, TRAINING_JOB_ID)
expected_data = {
'statistics': {
'num_epochs': 5,
'epoch': 1,
'progress_percentage': 0.5,
'stages': {
'train': {
'accuracy': 0.9,
'loss': 0.05
},
'validation': {
'accuracy': 0.8,
'loss': 0.1,
'key2': 2
}
},
'key1': 'value1'
}
}
m.assert_called_with(
'POST',
url,
params=None,
headers={
'User-Agent': 'abeja-platform-sdk/{}'.format(VERSION)},
timeout=30,
data=None,
json=expected_data)
def train_model(trainloader, validloader, model, optimizer, criterion):
"""returns trained model"""
# initialize tracker for minimum validation loss
valid_loss_min = 3.877533 # np.Inf
if os.path.exists(save_path):
model.load_state_dict(torch.load(save_path))
for epoch in range(1, n_epochs + 1):
# initialize variables to monitor training and validation loss and accuracy
train_loss = 0.0
train_total = 0
train_correct = 0
valid_loss = 0.0
valid_total = 0
valid_correct = 0
# train the model
model.train()
for data, target in trainloader:
data, target = data.to(device), target.to(device)
# clear the gradients of all optimized variables
optimizer.zero_grad()
# forward pass: compute predicted outputs by passing inputs to the model
output = model(data)
# calculate the batch loss
loss = criterion(output, target)
# backward pass: compute gradient of the loss with respect to model parameters
loss.backward()
# perform a single optimization step (parameter update)
optimizer.step()
# update training loss
train_loss += loss.item() * data.size(0)
# count number of correct labels
_, preds_tensor = torch.max(output, 1)
train_total += target.size(0)
train_correct += (preds_tensor == target).sum().item()
# validate the model
model.eval()
for data, target in validloader:
data, target = data.to(device), target.to(device)
# forward pass: compute predicted outputs by passing inputs to the model
output = model(data)
# calculate the batch loss
loss = criterion(output, target)
# update average validation loss
valid_loss += loss.item() * data.size(0)
# count number of correct labels
_, preds_tensor = torch.max(output, 1)
valid_total += target.size(0)
valid_correct += (preds_tensor == target).sum().item()
# calculate average losses
train_loss = train_loss / len(trainloader.dataset)
valid_loss = valid_loss / len(validloader.dataset)
# calculate accuracy
train_acc = train_correct / train_total
valid_acc = valid_correct / valid_total
# update ABEJA statisctics
train_client = TrainClient()
statistics = ABEJAStatistics(num_epochs=n_epochs, epoch=epoch)
statistics.add_stage(name=ABEJAStatistics.STAGE_TRAIN,
accuracy=train_acc,
loss=train_loss)
statistics.add_stage(name=ABEJAStatistics.STAGE_VALIDATION,
accuracy=valid_acc,
loss=valid_loss)
train_client.update_statistics(statistics)
# print training/validation statistics
print(
'Epoch: {} \tTrain loss: {:.6f} \tTrain acc: {:.6f} \tValid loss: {:.6f} \tValid acc: {:.6f}'.format(
epoch,
train_loss,
train_acc,
valid_loss,
valid_acc
))
# save model if validation loss has decreased
if valid_loss <= valid_loss_min:
print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model.'.format(
valid_loss_min,
valid_loss))
torch.save(model.state_dict(), save_path)
valid_loss_min = valid_loss
# return trained model
return model