def test_config_bad_preprocessing_param():
config = {
"input_features": [
sequence_feature(reduce_output="sum", encoder="fake"),
image_feature(
"/tmp/destination_folder",
preprocessing={
"in_memory": True,
"height": 12,
"width": 12,
"num_channels": 3,
"tokenizer": "space",
},
),
],
"output_features":
[category_feature(vocab_size=2, reduce_input="sum")],
"combiner": {
"type": "concat",
"output_size": 14
},
}
with pytest.raises(ValidationError, match=r"^'fake' is not one of .*"):
validate_config(config)
def test_config_fill_values():
vector_fill_values = ['1.0 0.0 1.04 10.49', '1 2 3 4 5' '0' '1.0' '']
binary_fill_values = ['yes', 'No', '1', 'TRUE', 1]
for vector_fill_value, binary_fill_value in zip(vector_fill_values,
binary_fill_values):
config = {
'input_features': [
vector_feature(
preprocessing={'fill_value': vector_fill_value}),
],
'output_features':
[binary_feature(preprocessing={'fill_value': binary_fill_value})],
}
validate_config(config)
bad_vector_fill_values = ['one two three', '1,2,3', 0]
bad_binary_fill_values = ['one', 2, 'maybe']
for vector_fill_value, binary_fill_value in zip(
vector_fill_values[:3] + bad_vector_fill_values,
bad_binary_fill_values + binary_fill_values[:3]):
config = {
'input_features': [
vector_feature(
preprocessing={'fill_value': vector_fill_value}),
],
'output_features':
[binary_feature(preprocessing={'fill_value': binary_fill_value})],
}
with pytest.raises(ValidationError):
validate_config(config)
def test_config_bad_preprocessing_param():
config = {
'input_features': [
sequence_feature(reduce_output='sum', encoder='fake'),
image_feature(
'/tmp/destination_folder',
preprocessing={
'in_memory': True,
'height': 12,
'width': 12,
'num_channels': 3,
'tokenizer': 'space',
},
),
],
'output_features':
[category_feature(vocab_size=2, reduce_input='sum')],
'combiner': {
'type': 'concat',
'fc_size': 14
},
}
with pytest.raises(ValidationError, match=r"^'fake' is not one of .*"):
validate_config(config)
def test_config_fill_values():
vector_fill_values = ["1.0 0.0 1.04 10.49", "1 2 3 4 5" "0" "1.0" ""]
binary_fill_values = ["yes", "No", "1", "TRUE", 1]
for vector_fill_value, binary_fill_value in zip(vector_fill_values,
binary_fill_values):
config = {
"input_features": [
vector_feature(
preprocessing={"fill_value": vector_fill_value}),
],
"output_features":
[binary_feature(preprocessing={"fill_value": binary_fill_value})],
}
validate_config(config)
bad_vector_fill_values = ["one two three", "1,2,3", 0]
bad_binary_fill_values = ["one", 2, "maybe"]
for vector_fill_value, binary_fill_value in zip(
vector_fill_values[:3] + bad_vector_fill_values,
bad_binary_fill_values + binary_fill_values[:3]):
config = {
"input_features": [
vector_feature(
preprocessing={"fill_value": vector_fill_value}),
],
"output_features":
[binary_feature(preprocessing={"fill_value": binary_fill_value})],
}
with pytest.raises(ValidationError):
validate_config(config)
def test_validate_with_preprocessing_defaults():
config = {
"input_features": [
audio_feature("/tmp/destination_folder", preprocessing=AudioFeatureMixin.preprocessing_defaults),
bag_feature(preprocessing=BagFeatureMixin.preprocessing_defaults),
binary_feature(preprocessing=BinaryFeatureMixin.preprocessing_defaults),
category_feature(preprocessing=CategoryFeatureMixin.preprocessing_defaults),
date_feature(preprocessing=DateFeatureMixin.preprocessing_defaults),
h3_feature(preprocessing=H3FeatureMixin.preprocessing_defaults),
image_feature("/tmp/destination_folder", preprocessing=ImageFeatureMixin.preprocessing_defaults),
numerical_feature(preprocessing=NumericalFeatureMixin.preprocessing_defaults),
sequence_feature(preprocessing=SequenceFeatureMixin.preprocessing_defaults),
set_feature(preprocessing=SetFeatureMixin.preprocessing_defaults),
text_feature(preprocessing=TextFeatureMixin.preprocessing_defaults),
timeseries_feature(preprocessing=TimeseriesFeatureMixin.preprocessing_defaults),
vector_feature(preprocessing=VectorFeatureMixin.preprocessing_defaults),
],
"output_features": [{"name": "target", "type": "category"}],
"training": {
"decay": True,
"learning_rate": 0.001,
"validation_field": "target",
"validation_metric": "accuracy",
},
}
validate_config(config)
config = merge_with_defaults(config)
validate_config(config)
def test_config_bad_encoder_name():
config = {
"input_features": [sequence_feature(reduce_output="sum", encoder="fake")],
"output_features": [category_feature(vocab_size=2, reduce_input="sum")],
"combiner": {"type": "concat", "fc_size": 14},
}
with pytest.raises(ValidationError, match=r"^'fake' is not one of .*"):
validate_config(config)
def test_config_bad_encoder_name():
config = {
'input_features': [sequence_feature(reduce_output='sum', encoder='fake')],
'output_features': [category_feature(vocab_size=2, reduce_input='sum')],
'combiner': {'type': 'concat', 'fc_size': 14},
}
with pytest.raises(ValidationError, match=r"^'fake' is not one of .*"):
validate_config(config)
def test_config_encoders():
for encoder in ENCODERS:
config = {
'input_features': [
sequence_feature(reduce_output='sum', encoder=encoder),
image_feature('/tmp/destination_folder'),
],
'output_features': [category_feature(vocab_size=2, reduce_input='sum')],
'combiner': {'type': 'concat', 'fc_size': 14},
}
validate_config(config)
def test_config_encoders():
for encoder in ENCODERS:
config = {
"input_features": [
sequence_feature(reduce_output="sum", encoder=encoder),
image_feature("/tmp/destination_folder"),
],
"output_features": [category_feature(vocab_size=2, reduce_input="sum")],
"combiner": {"type": "concat", "fc_size": 14},
}
validate_config(config)
def test_config_bad_feature_type():
config = {
"input_features": [{
"name": "foo",
"type": "fake"
}],
"output_features":
[category_feature(vocab_size=2, reduce_input="sum")],
"combiner": {
"type": "concat",
"output_size": 14
},
}
with pytest.raises(ValidationError, match=r"^'fake' is not one of .*"):
validate_config(config)
def test_config_tabnet():
config = {
"input_features": [
category_feature(vocab_size=2, reduce_input="sum"),
numerical_feature(),
],
"output_features": [binary_feature(weight_regularization=None)],
"combiner": {
"type": "tabnet",
"size": 24,
"output_size": 26,
"sparsity": 0.000001,
"bn_virtual_divider": 32,
"bn_momentum": 0.6,
"num_steps": 5,
"relaxation_factor": 1.5,
"use_keras_batch_norm": False,
"bn_virtual_bs": 512,
},
"training": {
"batch_size": 16384,
"eval_batch_size": 500000,
"epochs": 1000,
"early_stop": 20,
"learning_rate": 0.02,
"optimizer": {
"type": "adam"
},
"decay": True,
"decay_steps": 20000,
"decay_rate": 0.9,
"staircase": True,
"regularization_lambda": 1,
"regularization_type": "l2",
"validation_field": "label",
},
}
validate_config(config)
def test_config_tabnet():
config = {
'input_features': [
category_feature(vocab_size=2, reduce_input='sum'),
numerical_feature(),
],
'output_features': [binary_feature(weight_regularization=None)],
'combiner': {
'type': 'tabnet',
'size': 24,
'output_size': 26,
'sparsity': 0.000001,
'bn_virtual_divider': 32,
'bn_momentum': 0.6,
'num_steps': 5,
'relaxation_factor': 1.5,
'use_keras_batch_norm': False,
'bn_virtual_bs': 512,
},
'training': {
'batch_size': 16384,
'eval_batch_size': 500000,
'epochs': 1000,
'early_stop': 20,
'learning_rate': 0.02,
'optimizer': {
'type': 'adam'
},
'decay': True,
'decay_steps': 20000,
'decay_rate': 0.9,
'staircase': True,
'regularization_lambda': 1,
'validation_field': 'label',
}
}
validate_config(config)
def test_config_features():
all_input_features = [
audio_feature("/tmp/destination_folder"),
bag_feature(),
binary_feature(),
category_feature(),
date_feature(),
h3_feature(),
image_feature("/tmp/destination_folder"),
number_feature(),
sequence_feature(),
set_feature(),
text_feature(),
timeseries_feature(),
vector_feature(),
]
all_output_features = [
binary_feature(),
category_feature(),
number_feature(),
sequence_feature(),
set_feature(),
text_feature(),
vector_feature(),
]
# validate config with all features
config = {
"input_features": all_input_features,
"output_features": all_output_features,
}
validate_config(config)
# make sure all defaults provided also registers as valid
config = merge_with_defaults(config)
validate_config(config)
# test various invalid output features
input_only_features = [
feature for feature in all_input_features
if feature["type"] not in output_type_registry.keys()
]
for input_feature in input_only_features:
config = {
"input_features": all_input_features,
"output_features": all_output_features + [input_feature],
}
dtype = input_feature["type"]
with pytest.raises(ValidationError,
match=rf"^'{dtype}' is not one of .*"):
validate_config(config)
def test_config_features():
all_input_features = [
audio_feature('/tmp/destination_folder'),
bag_feature(),
binary_feature(),
category_feature(),
date_feature(),
h3_feature(),
image_feature('/tmp/destination_folder'),
numerical_feature(),
sequence_feature(),
set_feature(),
text_feature(),
timeseries_feature(),
vector_feature(),
]
all_output_features = [
binary_feature(),
category_feature(),
numerical_feature(),
sequence_feature(),
set_feature(),
text_feature(),
vector_feature(),
]
# validate config with all features
config = {
'input_features': all_input_features,
'output_features': all_output_features,
}
validate_config(config)
# make sure all defaults provided also registers as valid
config = merge_with_defaults(config)
validate_config(config)
# test various invalid output features
input_only_features = [
feature for feature in all_input_features
if feature['type'] not in OUTPUT_FEATURE_TYPES
]
for input_feature in input_only_features:
config = {
'input_features': all_input_features,
'output_features': all_output_features + [input_feature],
}
dtype = input_feature['type']
with pytest.raises(ValidationError,
match=rf"^'{dtype}' is not one of .*"):
validate_config(config)
def test_config_bad_combiner():
config = {
'input_features': [
category_feature(vocab_size=2, reduce_input='sum'),
numerical_feature(),
],
'output_features': [binary_feature(weight_regularization=None)],
'combiner': {
'type': 'tabnet'
}
}
# config is valid at this point
validate_config(config)
# bad combiner
config['combiner']['type'] = 'fake'
with pytest.raises(ValidationError, match=r"^'fake' is not one of .*"):
validate_config(config)
# bad combiner format (list instead of dict)
config['combiner'] = [{'type': 'tabnet'}]
with pytest.raises(ValidationError):
validate_config(config)
def test_config_bad_combiner_types_enums():
config = {
"input_features": [
category_feature(vocab_size=2, reduce_input="sum"),
number_feature(),
],
"output_features": [binary_feature(weight_regularization=None)],
"combiner": {
"type": "concat",
"weights_initializer": "zeros"
},
}
# config is valid at this point
validate_config(config)
# Test weights initializer:
config["combiner"]["weights_initializer"] = {"test": "fail"}
with pytest.raises(ValidationError, match=r"{'test': 'fail'} is not of*"):
validate_config(config)
config["combiner"]["weights_initializer"] = "fail"
with pytest.raises(ValidationError, match=r"'fail' is not of*"):
validate_config(config)
config["combiner"]["weights_initializer"] = {}
with pytest.raises(ValidationError, match=r"Failed validating 'type'"):
validate_config(config)
config["combiner"]["weights_initializer"] = {"type": "fail"}
with pytest.raises(ValidationError, match=r"'fail' is not one of*"):
validate_config(config)
config["combiner"]["weights_initializer"] = {"type": "normal", "stddev": 0}
validate_config(config)
# Test bias initializer:
del config["combiner"]["weights_initializer"]
config["combiner"]["bias_initializer"] = "kaiming_uniform"
validate_config(config)
config["combiner"]["bias_initializer"] = "fail"
with pytest.raises(ValidationError, match=r"'fail' is not of*"):
validate_config(config)
config["combiner"]["bias_initializer"] = {}
with pytest.raises(ValidationError, match=r"Failed validating 'type'"):
validate_config(config)
config["combiner"]["bias_initializer"] = {"type": "fail"}
with pytest.raises(ValidationError, match=r"'fail' is not one of*"):
validate_config(config)
config["combiner"]["bias_initializer"] = {"type": "zeros", "stddev": 0}
validate_config(config)
# Test norm:
del config["combiner"]["bias_initializer"]
config["combiner"]["norm"] = "batch"
validate_config(config)
config["combiner"]["norm"] = "fail"
with pytest.raises(ValidationError, match=r"'fail' is not one of*"):
validate_config(config)
# Test activation:
del config["combiner"]["norm"]
config["combiner"]["activation"] = "relu"
validate_config(config)
config["combiner"]["activation"] = 123
with pytest.raises(ValidationError, match=r"123 is not of type*"):
validate_config(config)
# Test reduce_output:
del config["combiner"]["activation"]
config2 = {**config}
config2["combiner"]["type"] = "tabtransformer"
config2["combiner"]["reduce_output"] = "sum"
validate_config(config)
config2["combiner"]["reduce_output"] = "fail"
with pytest.raises(ValidationError, match=r"'fail' is not one of*"):
validate_config(config2)
# Test reduce_output = None:
config2["combiner"]["reduce_output"] = None
validate_config(config2)
def test_config_bad_combiner():
config = {
"input_features": [
category_feature(vocab_size=2, reduce_input="sum"),
number_feature(),
],
"output_features": [binary_feature(weight_regularization=None)],
"combiner": {
"type": "tabnet",
},
}
# config is valid at this point
validate_config(config)
# combiner without type
del config["combiner"]["type"]
with pytest.raises(ValidationError, match=r"^'type' is a required .*"):
validate_config(config)
# bad combiner type
config["combiner"]["type"] = "fake"
with pytest.raises(ValidationError, match=r"^'fake' is not one of .*"):
validate_config(config)
# bad combiner format (list instead of dict)
config["combiner"] = [{"type": "tabnet"}]
with pytest.raises(ValidationError,
match=r"^\[\{'type': 'tabnet'\}\] is not of .*"):
validate_config(config)
# bad combiner parameter types
config["combiner"] = {
"type": "tabtransformer",
"num_layers": 10,
"dropout": False,
}
with pytest.raises(ValidationError, match=r"^False is not of type.*"):
validate_config(config)
# bad combiner parameter range
config["combiner"] = {
"type": "transformer",
"dropout": -1,
}
with pytest.raises(ValidationError, match=r"less than the minimum.*"):
validate_config(config)
