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
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_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, 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(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]), 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, 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_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):
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):
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,
"ml.c4.2xlarge",
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, "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