def test_all_hyperparameters(sagemaker_session):
lr = LinearLearner(sagemaker_session=sagemaker_session,
binary_classifier_model_selection_criteria='accuracy',
target_recall=0.5, target_precision=0.6,
positive_example_weight_mult=0.1, epochs=1, use_bias=True, num_models=5,
num_calibration_samples=6, init_method='uniform', init_scale=0.1, init_sigma=0.001,
init_bias=0, optimizer='sgd', loss='logistic', wd=0.4, l1=0.04, momentum=0.1,
learning_rate=0.001, beta_1=0.2, beta_2=0.03, bias_lr_mult=5.5, bias_wd_mult=6.6,
use_lr_scheduler=False, lr_scheduler_step=2, lr_scheduler_factor=0.03,
lr_scheduler_minimum_lr=0.001, normalize_data=False, normalize_label=True,
unbias_data=True, unbias_label=False, num_point_for_scaler=3, margin=1.0,
quantile=0.5, loss_insensitivity=0.1, huber_delta=0.1, early_stopping_patience=3,
early_stopping_tolerance=0.001, num_classes=1, accuracy_top_k=3, f_beta=1.0,
balance_multiclass_weights=False, **ALL_REQ_ARGS)
assert lr.hyperparameters() == dict(
predictor_type='binary_classifier', binary_classifier_model_selection_criteria='accuracy',
target_recall='0.5', target_precision='0.6', positive_example_weight_mult='0.1', epochs='1',
use_bias='True', num_models='5', num_calibration_samples='6', init_method='uniform',
init_scale='0.1', init_sigma='0.001', init_bias='0.0', optimizer='sgd', loss='logistic',
wd='0.4', l1='0.04', momentum='0.1', learning_rate='0.001', beta_1='0.2', beta_2='0.03',
bias_lr_mult='5.5', bias_wd_mult='6.6', use_lr_scheduler='False', lr_scheduler_step='2',
lr_scheduler_factor='0.03', lr_scheduler_minimum_lr='0.001', normalize_data='False',
normalize_label='True', unbias_data='True', unbias_label='False', num_point_for_scaler='3', margin='1.0',
quantile='0.5', loss_insensitivity='0.1', huber_delta='0.1', early_stopping_patience='3',
early_stopping_tolerance='0.001', num_classes='1', accuracy_top_k='3', f_beta='1.0',
balance_multiclass_weights='False',
)
def test_model_image(sagemaker_session):
lr = LinearLearner(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM, channel='train')
lr.fit(data)
model = lr.create_model()
assert model.image == registry(REGION, 'linear-learner') + '/linear-learner:1'
def test_predictor_type(sagemaker_session):
lr = LinearLearner(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM, channel='train')
lr.fit(data)
model = lr.create_model()
predictor = model.deploy(1, TRAIN_INSTANCE_TYPE)
assert isinstance(predictor, LinearLearnerPredictor)
def test_prepare_for_training_calculate_batch_size_1(sagemaker_session):
lr = LinearLearner(base_job_name='lr', sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM, channel='train')
lr._prepare_for_training(data)
assert lr.mini_batch_size == 1
def test_prepare_for_training_multiple_channel(sagemaker_session):
lr = LinearLearner(base_job_name='lr', sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX),
num_records=10000,
feature_dim=FEATURE_DIM,
channel='train')
lr._prepare_for_training([data, data])
assert lr.mini_batch_size == DEFAULT_MINI_BATCH_SIZE
def test_prepare_for_training_multiple_channel_no_train(sagemaker_session):
lr = LinearLearner(base_job_name='lr', sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX),
num_records=10000,
feature_dim=FEATURE_DIM,
channel='mock')
with pytest.raises(ValueError) as ex:
lr._prepare_for_training([data, data])
assert 'Must provide train channel.' in str(ex)
def test_model_image(sagemaker_session):
lr = LinearLearner(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet(
"s3://{}/{}".format(BUCKET_NAME, PREFIX),
num_records=1,
feature_dim=FEATURE_DIM,
channel="train",
)
lr.fit(data)
model = lr.create_model()
assert image_uris.retrieve("linear-learner", REGION) == model.image_uri
def test_prepare_for_training_calculate_batch_size_2(sagemaker_session):
lr = LinearLearner(base_job_name="lr", sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet(
"s3://{}/{}".format(BUCKET_NAME, PREFIX),
num_records=10000,
feature_dim=FEATURE_DIM,
channel="train",
)
lr._prepare_for_training(data)
assert lr.mini_batch_size == DEFAULT_MINI_BATCH_SIZE
def test_call_fit_pass_batch_size(base_fit, sagemaker_session):
lr = LinearLearner(base_job_name='lr', sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX),
num_records=10000,
feature_dim=FEATURE_DIM,
channel='train')
lr.fit(data, 10)
base_fit.assert_called_once()
assert len(base_fit.call_args[0]) == 2
assert base_fit.call_args[0][0] == data
assert base_fit.call_args[0][1] == 10
def test_init_required_positional(sagemaker_session):
lr = LinearLearner(ROLE, TRAIN_INSTANCE_COUNT, TRAIN_INSTANCE_TYPE, PREDICTOR_TYPE,
sagemaker_session=sagemaker_session)
assert lr.role == ROLE
assert lr.train_instance_count == TRAIN_INSTANCE_COUNT
assert lr.train_instance_type == TRAIN_INSTANCE_TYPE
assert lr.predictor_type == PREDICTOR_TYPE
def test_num_classes_is_required_for_multiclass_classifier(sagemaker_session):
with pytest.raises(ValueError) as excinfo:
test_params = ALL_REQ_ARGS.copy()
test_params["predictor_type"] = 'multiclass_classifier'
LinearLearner(sagemaker_session=sagemaker_session, **test_params)
assert "For predictor_type 'multiclass_classifier', 'num_classes' should be set to a value greater than 2." in str(
excinfo.value)
def test_init_required_named(sagemaker_session):
lr = LinearLearner(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
assert lr.role == ALL_REQ_ARGS["role"]
assert lr.instance_count == ALL_REQ_ARGS["instance_count"]
assert lr.instance_type == ALL_REQ_ARGS["instance_type"]
assert lr.predictor_type == ALL_REQ_ARGS["predictor_type"]
def test_call_fit_calculate_batch_size_2(base_fit, sagemaker_session):
lr = LinearLearner(base_job_name="lr",
sagemaker_session=sagemaker_session,
**REQ_ARGS)
data = RecordSet("s3://{}/{}".format(BUCKET_NAME, PREFIX),
num_records=10000,
feature_dim=FEATURE_DIM,
channel='train')
lr.fit(data)
base_fit.assert_called_once()
assert len(base_fit.call_args[0]) == 2
assert base_fit.call_args[0][0] == data
assert base_fit.call_args[0][1] == DEFAULT_MINI_BATCH_SIZE
def test_init_required_named(sagemaker_session):
lr = LinearLearner(sagemaker_session=sagemaker_session, **REQ_ARGS)
assert lr.role == REQ_ARGS['role']
assert lr.train_instance_count == REQ_ARGS['train_instance_count']
assert lr.train_instance_type == REQ_ARGS['train_instance_type']
assert lr.predictor_type == DEFAULT_PREDICTOR_TYPE
def test_init_required_named(sagemaker_session):
lr = LinearLearner(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
assert lr.role == ALL_REQ_ARGS['role']
assert lr.train_instance_count == ALL_REQ_ARGS['train_instance_count']
assert lr.train_instance_type == ALL_REQ_ARGS['train_instance_type']
assert lr.predictor_type == ALL_REQ_ARGS['predictor_type']
def test_linear_learner_multiclass(sagemaker_session, cpu_instance_type,
training_set):
job_name = unique_name_from_base("linear-learner")
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
training_set = training_set[0], training_set[1].astype(
np.dtype("float32"))
ll = LinearLearner(
"SageMakerRole",
1,
cpu_instance_type,
predictor_type="multiclass_classifier",
num_classes=10,
sagemaker_session=sagemaker_session,
)
ll.epochs = 1
ll.fit(ll.record_set(training_set[0][:200], training_set[1][:200]),
job_name=job_name)
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
predictor = ll.deploy(1, cpu_instance_type, endpoint_name=job_name)
result = predictor.predict(training_set[0][0:100])
assert len(result) == 100
for record in result:
assert record.label["predicted_label"] is not None
assert record.label["score"] is not None
def test_linear_learner_multiclass(sagemaker_session):
job_name = unique_name_from_base('linear-learner')
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
pickle_args = {} if sys.version_info.major == 2 else {
'encoding': 'latin1'
}
# Load the data into memory as numpy arrays
with gzip.open(data_path, 'rb') as f:
train_set, _, _ = pickle.load(f, **pickle_args)
train_set = train_set[0], train_set[1].astype(np.dtype('float32'))
ll = LinearLearner('SageMakerRole',
1,
'ml.c4.2xlarge',
predictor_type='multiclass_classifier',
num_classes=10,
sagemaker_session=sagemaker_session)
ll.epochs = 1
ll.fit(ll.record_set(train_set[0][:200], train_set[1][:200]),
job_name=job_name)
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
predictor = ll.deploy(1, 'ml.c4.xlarge', endpoint_name=job_name)
result = predictor.predict(train_set[0][0:100])
assert len(result) == 100
for record in result:
assert record.label["predicted_label"] is not None
assert record.label["score"] is not None
def test_linear_learner_multiclass(sagemaker_session):
with timeout(minutes=15):
data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
pickle_args = {} if sys.version_info.major == 2 else {'encoding': 'latin1'}
# Load the data into memory as numpy arrays
with gzip.open(data_path, 'rb') as f:
train_set, _, _ = pickle.load(f, **pickle_args)
train_set = train_set[0], train_set[1].astype(np.dtype('float32'))
ll = LinearLearner('SageMakerRole', 1, 'ml.c4.2xlarge', base_job_name='test-linear-learner',
predictor_type='multiclass_classifier', num_classes=10, sagemaker_session=sagemaker_session)
ll.epochs = 1
ll.fit(ll.record_set(train_set[0][:200], train_set[1][:200]))
endpoint_name = name_from_base('linear-learner')
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
predictor = ll.deploy(1, 'ml.c4.xlarge', endpoint_name=endpoint_name)
result = predictor.predict(train_set[0][0:100])
assert len(result) == 100
for record in result:
assert record.label["predicted_label"] is not None
assert record.label["score"] is not None
def test_predictor_custom_serialization(sagemaker_session):
lr = LinearLearner(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet(
"s3://{}/{}".format(BUCKET_NAME, PREFIX),
num_records=1,
feature_dim=FEATURE_DIM,
channel="train",
)
lr.fit(data)
model = lr.create_model()
custom_serializer = Mock()
custom_deserializer = Mock()
predictor = model.deploy(
1,
INSTANCE_TYPE,
serializer=custom_serializer,
deserializer=custom_deserializer,
)
assert isinstance(predictor, LinearLearnerPredictor)
assert predictor.serializer is custom_serializer
assert predictor.deserializer is custom_deserializer
def test_linear_learner_multiclass(sagemaker_session, cpu_instance_type):
job_name = unique_name_from_base("linear-learner")
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
data_path = os.path.join(DATA_DIR, "one_p_mnist", "mnist.pkl.gz")
pickle_args = {} if sys.version_info.major == 2 else {
"encoding": "latin1"
}
# Load the data into memory as numpy arrays
with gzip.open(data_path, "rb") as f:
train_set, _, _ = pickle.load(f, **pickle_args)
train_set = train_set[0], train_set[1].astype(np.dtype("float32"))
ll = LinearLearner(
"SageMakerRole",
1,
cpu_instance_type,
predictor_type="multiclass_classifier",
num_classes=10,
sagemaker_session=sagemaker_session,
)
ll.epochs = 1
ll.fit(ll.record_set(train_set[0][:200], train_set[1][:200]),
job_name=job_name)
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
predictor = ll.deploy(1, cpu_instance_type, endpoint_name=job_name)
result = predictor.predict(train_set[0][0:100])
assert len(result) == 100
for record in result:
assert record.label["predicted_label"] is not None
assert record.label["score"] is not None
def test_image(sagemaker_session):
lr = LinearLearner(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
assert lr.train_image() == registry(REGION, 'linear-learner') + '/linear-learner:1'
def test_optional_hyper_parameters_value(sagemaker_session,
optional_hyper_parameters, value):
with pytest.raises(ValueError):
test_params = ALL_REQ_ARGS.copy()
test_params.update({optional_hyper_parameters: value})
LinearLearner(sagemaker_session=sagemaker_session, **test_params)
def test_iterable_hyper_parameters_type(sagemaker_session,
iterable_hyper_parameters, value):
with pytest.raises(TypeError):
test_params = ALL_REQ_ARGS.copy()
test_params.update({iterable_hyper_parameters: value})
LinearLearner(sagemaker_session=sagemaker_session, **test_params)
def test_num_classes_can_be_string_for_multiclass_classifier(
sagemaker_session):
test_params = ALL_REQ_ARGS.copy()
test_params["predictor_type"] = "multiclass_classifier"
test_params["num_classes"] = "3"
LinearLearner(sagemaker_session=sagemaker_session, **test_params)
def test_required_hyper_parameters_value(sagemaker_session,
required_hyper_parameters, value):
with pytest.raises(ValueError):
test_params = ALL_REQ_ARGS.copy()
test_params[required_hyper_parameters] = value
LinearLearner(sagemaker_session=sagemaker_session, **test_params)
def test_image(sagemaker_session):
lr = LinearLearner(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
assert image_uris.retrieve("linear-learner",
REGION) == lr.training_image_uri()
def test_image(sagemaker_session):
lr = LinearLearner(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
assert lr.train_image() == registry(REGION,
'linear-learner') + '/linear-learner:1'
def test_linear_learner(sagemaker_session):
with timeout(minutes=15):
data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
pickle_args = {} if sys.version_info.major == 2 else {
'encoding': 'latin1'
}
# Load the data into memory as numpy arrays
with gzip.open(data_path, 'rb') as f:
train_set, _, _ = pickle.load(f, **pickle_args)
train_set[1][:100] = 1
train_set[1][100:200] = 0
train_set = train_set[0], train_set[1].astype(np.dtype('float32'))
ll = LinearLearner('SageMakerRole',
1,
'ml.c4.2xlarge',
base_job_name='test-linear-learner',
predictor_type='binary_classifier',
sagemaker_session=sagemaker_session)
ll.binary_classifier_model_selection_criteria = 'accuracy'
ll.target_recall = 0.5
ll.target_precision = 0.5
ll.positive_example_weight_mult = 0.1
ll.epochs = 1
ll.use_bias = True
ll.num_models = 1
ll.num_calibration_samples = 1
ll.init_method = 'uniform'
ll.init_scale = 0.5
ll.init_sigma = 0.2
ll.init_bias = 5
ll.optimizer = 'adam'
ll.loss = 'logistic'
ll.wd = 0.5
ll.l1 = 0.5
ll.momentum = 0.5
ll.learning_rate = 0.1
ll.beta_1 = 0.1
ll.beta_2 = 0.1
ll.use_lr_scheduler = True
ll.lr_scheduler_step = 2
ll.lr_scheduler_factor = 0.5
ll.lr_scheduler_minimum_lr = 0.1
ll.normalize_data = False
ll.normalize_label = False
ll.unbias_data = True
ll.unbias_label = False
ll.num_point_for_scaler = 10000
ll.margin = 1.0
ll.quantile = 0.5
ll.loss_insensitivity = 0.1
ll.huber_delta = 0.1
ll.early_stopping_tolerance = 0.0001
ll.early_stopping_patience = 3
ll.fit(ll.record_set(train_set[0][:200], train_set[1][:200]))
endpoint_name = name_from_base('linear-learner')
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
predictor = ll.deploy(1, 'ml.c4.xlarge', endpoint_name=endpoint_name)
result = predictor.predict(train_set[0][0:100])
assert len(result) == 100
for record in result:
assert record.label["predicted_label"] is not None
assert record.label["score"] is not None
def test_linear_learner(sagemaker_session, cpu_instance_type):
job_name = unique_name_from_base("linear-learner")
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
data_path = os.path.join(DATA_DIR, "one_p_mnist", "mnist.pkl.gz")
pickle_args = {} if sys.version_info.major == 2 else {
"encoding": "latin1"
}
# Load the data into memory as numpy arrays
with gzip.open(data_path, "rb") as f:
train_set, _, _ = pickle.load(f, **pickle_args)
train_set[1][:100] = 1
train_set[1][100:200] = 0
train_set = train_set[0], train_set[1].astype(np.dtype("float32"))
ll = LinearLearner(
"SageMakerRole",
1,
cpu_instance_type,
predictor_type="binary_classifier",
sagemaker_session=sagemaker_session,
)
ll.binary_classifier_model_selection_criteria = "accuracy"
ll.target_recall = 0.5
ll.target_precision = 0.5
ll.positive_example_weight_mult = 0.1
ll.epochs = 1
ll.use_bias = True
ll.num_models = 1
ll.num_calibration_samples = 1
ll.init_method = "uniform"
ll.init_scale = 0.5
ll.init_sigma = 0.2
ll.init_bias = 5
ll.optimizer = "adam"
ll.loss = "logistic"
ll.wd = 0.5
ll.l1 = 0.5
ll.momentum = 0.5
ll.learning_rate = 0.1
ll.beta_1 = 0.1
ll.beta_2 = 0.1
ll.use_lr_scheduler = True
ll.lr_scheduler_step = 2
ll.lr_scheduler_factor = 0.5
ll.lr_scheduler_minimum_lr = 0.1
ll.normalize_data = False
ll.normalize_label = False
ll.unbias_data = True
ll.unbias_label = False
ll.num_point_for_scaler = 10000
ll.margin = 1.0
ll.quantile = 0.5
ll.loss_insensitivity = 0.1
ll.huber_delta = 0.1
ll.early_stopping_tolerance = 0.0001
ll.early_stopping_patience = 3
ll.fit(ll.record_set(train_set[0][:200], train_set[1][:200]),
job_name=job_name)
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
predictor = ll.deploy(1, cpu_instance_type, endpoint_name=job_name)
result = predictor.predict(train_set[0][0:100])
assert len(result) == 100
for record in result:
assert record.label["predicted_label"] is not None
assert record.label["score"] is not None
def test_async_linear_learner(sagemaker_session, cpu_instance_type,
training_set):
job_name = unique_name_from_base("linear-learner")
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
training_set[1][:100] = 1
training_set[1][100:200] = 0
training_set = training_set[0], training_set[1].astype(
np.dtype("float32"))
ll = LinearLearner(
"SageMakerRole",
1,
cpu_instance_type,
predictor_type="binary_classifier",
sagemaker_session=sagemaker_session,
)
ll.binary_classifier_model_selection_criteria = "accuracy"
ll.target_recall = 0.5
ll.target_precision = 0.5
ll.positive_example_weight_mult = 0.1
ll.epochs = 1
ll.use_bias = True
ll.num_models = 1
ll.num_calibration_samples = 1
ll.init_method = "uniform"
ll.init_scale = 0.5
ll.init_sigma = 0.2
ll.init_bias = 5
ll.optimizer = "adam"
ll.loss = "logistic"
ll.wd = 0.5
ll.l1 = 0.5
ll.momentum = 0.5
ll.learning_rate = 0.1
ll.beta_1 = 0.1
ll.beta_2 = 0.1
ll.use_lr_scheduler = True
ll.lr_scheduler_step = 2
ll.lr_scheduler_factor = 0.5
ll.lr_scheduler_minimum_lr = 0.1
ll.normalize_data = False
ll.normalize_label = False
ll.unbias_data = True
ll.unbias_label = False
ll.num_point_for_scaler = 10000
ll.margin = 1.0
ll.quantile = 0.5
ll.loss_insensitivity = 0.1
ll.huber_delta = 0.1
ll.early_stopping_tolerance = 0.0001
ll.early_stopping_patience = 3
ll.fit(
ll.record_set(training_set[0][:200], training_set[1][:200]),
wait=False,
job_name=job_name,
)
print("Waiting to re-attach to the training job: %s" % job_name)
time.sleep(20)
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
estimator = LinearLearner.attach(training_job_name=job_name,
sagemaker_session=sagemaker_session)
model = LinearLearnerModel(estimator.model_data,
role="SageMakerRole",
sagemaker_session=sagemaker_session)
predictor = model.deploy(1, cpu_instance_type, endpoint_name=job_name)
result = predictor.predict(training_set[0][0:100])
assert len(result) == 100
for record in result:
assert record.label["predicted_label"] is not None
assert record.label["score"] is not None
def test_all_hyperparameters(sagemaker_session):
lr = LinearLearner(
sagemaker_session=sagemaker_session,
binary_classifier_model_selection_criteria="accuracy",
target_recall=0.5,
target_precision=0.6,
positive_example_weight_mult=0.1,
epochs=1,
use_bias=True,
num_models=5,
num_calibration_samples=6,
init_method="uniform",
init_scale=0.1,
init_sigma=0.001,
init_bias=0,
optimizer="sgd",
loss="logistic",
wd=0.4,
l1=0.04,
momentum=0.1,
learning_rate=0.001,
beta_1=0.2,
beta_2=0.03,
bias_lr_mult=5.5,
bias_wd_mult=6.6,
use_lr_scheduler=False,
lr_scheduler_step=2,
lr_scheduler_factor=0.03,
lr_scheduler_minimum_lr=0.001,
normalize_data=False,
normalize_label=True,
unbias_data=True,
unbias_label=False,
num_point_for_scaler=3,
margin=1.0,
quantile=0.5,
loss_insensitivity=0.1,
huber_delta=0.1,
early_stopping_patience=3,
early_stopping_tolerance=0.001,
num_classes=1,
accuracy_top_k=3,
f_beta=1.0,
balance_multiclass_weights=False,
**ALL_REQ_ARGS,
)
assert lr.hyperparameters() == dict(
predictor_type="binary_classifier",
binary_classifier_model_selection_criteria="accuracy",
target_recall="0.5",
target_precision="0.6",
positive_example_weight_mult="0.1",
epochs="1",
use_bias="True",
num_models="5",
num_calibration_samples="6",
init_method="uniform",
init_scale="0.1",
init_sigma="0.001",
init_bias="0.0",
optimizer="sgd",
loss="logistic",
wd="0.4",
l1="0.04",
momentum="0.1",
learning_rate="0.001",
beta_1="0.2",
beta_2="0.03",
bias_lr_mult="5.5",
bias_wd_mult="6.6",
use_lr_scheduler="False",
lr_scheduler_step="2",
lr_scheduler_factor="0.03",
lr_scheduler_minimum_lr="0.001",
normalize_data="False",
normalize_label="True",
unbias_data="True",
unbias_label="False",
num_point_for_scaler="3",
margin="1.0",
quantile="0.5",
loss_insensitivity="0.1",
huber_delta="0.1",
early_stopping_patience="3",
early_stopping_tolerance="0.001",
num_classes="1",
accuracy_top_k="3",
f_beta="1.0",
balance_multiclass_weights="False",
)
def test_linear_learner(sagemaker_session):
with timeout(minutes=15):
data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
pickle_args = {} if sys.version_info.major == 2 else {'encoding': 'latin1'}
# Load the data into memory as numpy arrays
with gzip.open(data_path, 'rb') as f:
train_set, _, _ = pickle.load(f, **pickle_args)
train_set[1][:100] = 1
train_set[1][100:200] = 0
train_set = train_set[0], train_set[1].astype(np.dtype('float32'))
ll = LinearLearner('SageMakerRole', 1, 'ml.c4.2xlarge', base_job_name='test-linear-learner',
predictor_type='binary_classifier', sagemaker_session=sagemaker_session)
ll.binary_classifier_model_selection_criteria = 'accuracy'
ll.target_recall = 0.5
ll.target_precision = 0.5
ll.positive_example_weight_mult = 0.1
ll.epochs = 1
ll.use_bias = True
ll.num_models = 1
ll.num_calibration_samples = 1
ll.init_method = 'uniform'
ll.init_scale = 0.5
ll.init_sigma = 0.2
ll.init_bias = 5
ll.optimizer = 'adam'
ll.loss = 'logistic'
ll.wd = 0.5
ll.l1 = 0.5
ll.momentum = 0.5
ll.learning_rate = 0.1
ll.beta_1 = 0.1
ll.beta_2 = 0.1
ll.use_lr_scheduler = True
ll.lr_scheduler_step = 2
ll.lr_scheduler_factor = 0.5
ll.lr_scheduler_minimum_lr = 0.1
ll.normalize_data = False
ll.normalize_label = False
ll.unbias_data = True
ll.unbias_label = False
ll.num_point_for_scaler = 10000
ll.margin = 1.0
ll.quantile = 0.5
ll.loss_insensitivity = 0.1
ll.huber_delta = 0.1
ll.early_stopping_tolerance = 0.0001
ll.early_stopping_patience = 3
ll.fit(ll.record_set(train_set[0][:200], train_set[1][:200]))
endpoint_name = name_from_base('linear-learner')
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
predictor = ll.deploy(1, 'ml.c4.xlarge', endpoint_name=endpoint_name)
result = predictor.predict(train_set[0][0:100])
assert len(result) == 100
for record in result:
assert record.label["predicted_label"] is not None
assert record.label["score"] is not None
def model_name(sagemaker_session, cpu_instance_type, training_set):
job_name = utils.unique_name_from_base("clarify-xgb")
with timeout.timeout(minutes=integ.TRAINING_DEFAULT_TIMEOUT_MINUTES):
ll = LinearLearner(
"SageMakerRole",
1,
cpu_instance_type,
predictor_type="binary_classifier",
sagemaker_session=sagemaker_session,
disable_profiler=True,
)
ll.binary_classifier_model_selection_criteria = "accuracy"
ll.early_stopping_tolerance = 0.0001
ll.early_stopping_patience = 3
ll.num_models = 1
ll.epochs = 1
ll.num_calibration_samples = 1
features, label = training_set
ll.fit(
ll.record_set(features.astype(np.float32), label.reshape(-1).astype(np.float32)),
job_name=job_name,
)
with timeout.timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
ll.deploy(1, cpu_instance_type, endpoint_name=job_name, model_name=job_name, wait=True)
yield job_name
