def test_tabnet_combiner(encoder_outputs):
# clean out unneeded encoder outputs
encoder_outputs = {}
encoder_outputs['feature_1'] = {
'encoder_output': tf.random.normal([128, 1], dtype=tf.float32)
}
encoder_outputs['feature_2'] = {
'encoder_output': tf.random.normal([128, 1], dtype=tf.float32)
}
input_features_def = [{
'name': 'feature_1',
'type': 'numerical'
}, {
'name': 'feature_2',
'type': 'numerical'
}]
# setup combiner to test
combiner = TabNetCombiner(build_inputs(input_features_def),
size=2,
output_size=2,
num_steps=3,
num_total_blocks=4,
num_shared_blocks=2,
dropout=0.1)
# concatenate encoder outputs
results = combiner(encoder_outputs)
# required key present
assert 'combiner_output' in results
assert 'attention_masks' in results
def test_tied_micro_level(input_feature_options):
# build input feature config
input_feature_configs = []
input_feature_configs.append({
"name": "input_feature_1",
"type": input_feature_options.feature_type
})
if input_feature_options.feature_options is not None:
input_feature_configs[0].update(input_feature_options.feature_options)
input_feature_configs.append({
"name": "input_feature_2",
"type": input_feature_options.feature_type
})
if input_feature_options.feature_options is not None:
input_feature_configs[1].update(input_feature_options.feature_options)
# add tied option to the second feature
if input_feature_options.tie_features:
input_feature_configs[1]["tied"] = "input_feature_1"
input_features = build_inputs(input_feature_configs)
if input_feature_options.tie_features:
# should be same encoder
assert input_features["input_feature_1"].encoder_obj is input_features[
"input_feature_2"].encoder_obj
else:
# no tied parameter, encoders should be different
assert input_features[
"input_feature_1"].encoder_obj is not input_features[
"input_feature_2"].encoder_obj
def test_tied_micro_level(input_feature_options):
# build input feature config
input_feature_configs = []
input_feature_configs.append({
'name': 'input_feature_1',
'type': input_feature_options.feature_type
})
if input_feature_options.feature_options is not None:
input_feature_configs[0].update(input_feature_options.feature_options)
input_feature_configs.append({
'name': 'input_feature_2',
'type': input_feature_options.feature_type
})
if input_feature_options.feature_options is not None:
input_feature_configs[1].update(input_feature_options.feature_options)
# add tied option to the second feature
if input_feature_options.tie_features:
input_feature_configs[1]['tied'] = 'input_feature_1'
input_features = build_inputs(input_feature_configs)
if input_feature_options.tie_features:
# should be same encoder
assert input_features['input_feature_1'].encoder_obj is \
input_features['input_feature_2'].encoder_obj
else:
# no tied parameter, encoders should be different
assert input_features['input_feature_1'].encoder_obj is not \
input_features['input_feature_2'].encoder_obj