def populate_defaults(input_feature):
set_default_values(input_feature, {TIED: None, "encoder": "parallel_cnn", "level": "word"})
encoder_class = get_encoder_cls(input_feature["type"], input_feature["encoder"])
if hasattr(encoder_class, "default_params"):
set_default_values(input_feature, encoder_class.default_params)
def populate_defaults(output_feature):
# If Loss is not defined, set an empty dictionary
set_default_value(output_feature, LOSS, {})
set_default_values(
output_feature[LOSS],
{
"robust_lambda": 0,
"confidence_penalty": 0,
"positive_class_weight": 1,
"weight": 1,
},
)
set_default_value(output_feature[LOSS], "robust_lambda", 0)
set_default_value(output_feature[LOSS], "confidence_penalty", 0)
set_default_value(output_feature[LOSS], "positive_class_weight", 1)
set_default_value(output_feature[LOSS], "weight", 1)
set_default_values(
output_feature,
{
"threshold": 0.5,
"dependencies": [],
"reduce_input": SUM,
"reduce_dependencies": SUM,
},
)
def populate_defaults(output_feature):
# If Loss is not defined, set an empty dictionary
set_default_value(output_feature, LOSS, {})
set_default_values(
output_feature[LOSS],
{
'robust_lambda': 0,
'confidence_penalty': 0,
'positive_class_weight': 1,
'weight': 1
}
)
set_default_value(output_feature[LOSS], 'robust_lambda', 0)
set_default_value(output_feature[LOSS], 'confidence_penalty', 0)
set_default_value(output_feature[LOSS], 'positive_class_weight', 1)
set_default_value(output_feature[LOSS], 'weight', 1)
set_default_values(
output_feature,
{
'threshold': 0.5,
'dependencies': [],
'reduce_input': SUM,
'reduce_dependencies': SUM
}
)
def populate_defaults(input_feature):
set_default_values(
input_feature,
{
TIED: None,
"encoder": "parallel_cnn",
},
)
def populate_defaults(input_feature):
set_default_values(
input_feature,
{
TIED: None,
'encoder': 'parallel_cnn',
}
)
def populate_defaults(input_feature):
set_default_values(
input_feature,
{
TIED: None,
'preprocessing': {}
}
)
def populate_defaults(input_feature):
set_default_values(input_feature, {
TIED: None,
'encoder': 'parallel_cnn',
'level': 'word'
})
encoder_class = get_from_registry(input_feature['encoder'],
TextInputFeature.encoder_registry)
if hasattr(encoder_class, 'default_params'):
set_default_values(input_feature, encoder_class.default_params)
def update_hyperopt_params_with_defaults(hyperopt_params):
set_default_value(hyperopt_params, STRATEGY, {})
set_default_value(hyperopt_params, EXECUTOR, {})
set_default_value(hyperopt_params, "split", VALIDATION)
set_default_value(hyperopt_params, "output_feature", COMBINED)
set_default_value(hyperopt_params, "metric", LOSS)
set_default_value(hyperopt_params, "goal", MINIMIZE)
set_default_values(hyperopt_params[STRATEGY], {TYPE: "random"})
strategy = get_from_registry(hyperopt_params[STRATEGY][TYPE],
sampler_registry)
strategy_defaults = {
k: v
for k, v in strategy.__dict__.items()
if k in get_class_attributes(strategy)
}
set_default_values(
hyperopt_params[STRATEGY],
strategy_defaults,
)
set_default_values(hyperopt_params[EXECUTOR], {TYPE: "serial"})
executor = get_from_registry(hyperopt_params[EXECUTOR][TYPE],
executor_registry)
executor_defaults = {
k: v
for k, v in executor.__dict__.items()
if k in get_class_attributes(executor)
}
set_default_values(
hyperopt_params[EXECUTOR],
executor_defaults,
)
def populate_defaults(output_feature):
set_default_value(output_feature, LOSS, {TYPE: "mean_squared_error", "weight": 1})
set_default_value(output_feature[LOSS], TYPE, "mean_squared_error")
set_default_value(output_feature[LOSS], "weight", 1)
set_default_values(
output_feature,
{
"clip": None,
"dependencies": [],
"reduce_input": SUM,
"reduce_dependencies": SUM,
},
)
def populate_defaults(output_feature):
set_default_value(output_feature, LOSS, {
TYPE: 'mean_squared_error',
'weight': 1
})
set_default_value(output_feature[LOSS], TYPE, 'mean_squared_error')
set_default_value(output_feature[LOSS], 'weight', 1)
set_default_values(
output_feature, {
'clip': None,
'dependencies': [],
'reduce_input': SUM,
'reduce_dependencies': SUM
})
def update_hyperopt_params_with_defaults(hyperopt_params):
from ludwig.hyperopt.execution import executor_registry
set_default_value(hyperopt_params, EXECUTOR, {})
set_default_value(hyperopt_params, "split", VALIDATION)
set_default_value(hyperopt_params, "output_feature", COMBINED)
set_default_value(hyperopt_params, "metric", LOSS)
set_default_value(hyperopt_params, "goal", MINIMIZE)
set_default_values(hyperopt_params[EXECUTOR], {TYPE: "ray"})
executor = get_from_registry(hyperopt_params[EXECUTOR][TYPE],
executor_registry)
executor_defaults = {
k: v
for k, v in executor.__dict__.items()
if k in get_class_attributes(executor)
}
set_default_values(
hyperopt_params[EXECUTOR],
executor_defaults,
)
def populate_defaults(output_feature):
# If Loss is not defined, set an empty dictionary
set_default_value(output_feature, LOSS, {})
# Populate the default values for LOSS if they aren't defined already
set_default_values(
output_feature[LOSS], {
TYPE: 'softmax_cross_entropy',
'labels_smoothing': 0,
'class_weights': 1,
'robust_lambda': 0,
'confidence_penalty': 0,
'class_similarities_temperature': 0,
'weight': 1
})
if output_feature[LOSS][TYPE] == 'sampled_softmax_cross_entropy':
set_default_values(
output_feature[LOSS], {
'sampler': 'log_uniform',
'unique': False,
'negative_samples': 25,
'distortion': 0.75
})
set_default_values(
output_feature, {
'top_k': 3,
'dependencies': [],
'reduce_input': SUM,
'reduce_dependencies': SUM
})
def populate_defaults(output_feature):
# If Loss is not defined, set an empty dictionary
set_default_value(output_feature, LOSS, {})
# Populate the default values for LOSS if they aren't defined already
set_default_values(
output_feature[LOSS],
{
TYPE: "softmax_cross_entropy",
"labels_smoothing": 0,
"class_weights": 1,
"robust_lambda": 0,
"confidence_penalty": 0,
"class_similarities_temperature": 0,
"weight": 1,
},
)
if output_feature[LOSS][TYPE] == "sampled_softmax_cross_entropy":
set_default_values(
output_feature[LOSS],
{"sampler": "log_uniform", "unique": False, "negative_samples": 25, "distortion": 0.75},
)
set_default_values(
output_feature, {"top_k": 3, "dependencies": [], "reduce_input": SUM, "reduce_dependencies": SUM}
)
def populate_defaults(output_feature):
# If Loss is not defined, set an empty dictionary
set_default_value(output_feature, LOSS, {})
# Populate the default values for LOSS if they aren't defined already
set_default_values(
output_feature[LOSS],
{
TYPE: "softmax_cross_entropy",
"class_weights": 1,
"robust_lambda": 0,
"confidence_penalty": 0,
"class_similarities_temperature": 0,
"weight": 1,
},
)
set_default_values(
output_feature, {
"top_k": 3,
"dependencies": [],
"reduce_input": SUM,
"reduce_dependencies": SUM
})