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_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",
)
