def test_missing_values_drop_rows(csv_filename, tmpdir):
data_csv_path = os.path.join(tmpdir, csv_filename)
kwargs = {PREPROCESSING: {"missing_value_strategy": DROP_ROW}}
input_features = [
number_feature(),
binary_feature(),
category_feature(vocab_size=3),
]
output_features = [
binary_feature(**kwargs),
number_feature(**kwargs),
category_feature(vocab_size=3, **kwargs),
sequence_feature(vocab_size=3, **kwargs),
text_feature(vocab_size=3, **kwargs),
set_feature(vocab_size=3, **kwargs),
vector_feature(),
]
backend = LocalTestBackend()
config = {
"input_features": input_features,
"output_features": output_features,
TRAINER: {
"epochs": 2
}
}
training_data_csv_path = generate_data(input_features, output_features,
data_csv_path)
df = pd.read_csv(training_data_csv_path)
# set 10% of values to NaN
nan_percent = 0.1
ix = [(row, col) for row in range(df.shape[0])
for col in range(df.shape[1])]
for row, col in random.sample(ix, int(round(nan_percent * len(ix)))):
df.iat[row, col] = np.nan
# run preprocessing
ludwig_model = LudwigModel(config, backend=backend)
ludwig_model.preprocess(dataset=df)
def __init__(self, csv_filename):
self.csv_file = csv_filename
self.model = None
self.input_features = [
text_feature(vocab_size=10, min_len=1, representation='sparse'),
category_feature(vocab_size=10)
]
self.output_features = [
category_feature(vocab_size=2, reduce_input='sum')]
encoder = 'parallel_cnn'
data_csv = generate_data(
self.input_features,
self.output_features,
self.csv_file
)
self.input_features[0]['encoder'] = encoder
self.setup_model()
test_df, train_df, val_df = obtain_df_splits(data_csv)
self.train_stats = self.model.train(
data_train_df=train_df,
data_validation_df=val_df
)
self.test_stats_full = self.model.test(
data_df=test_df
)
self.output_feature_name = self.output_features[0]['name']
# probabilities need to be list of lists containing each row data
# from the probability columns
# ref: https://uber.github.io/ludwig/api/#test - Return
num_probs = self.output_features[0]['vocab_size']
self.probability = self.test_stats_full[0].iloc[:, 1:(num_probs+2)].values
self.ground_truth_metadata = self.model.train_set_metadata
target_predictions = test_df[self.output_feature_name]
self.ground_truth = np.asarray([
self.ground_truth_metadata[self.output_feature_name]['str2idx'][test_row]
for test_row in target_predictions
])
self.prediction_raw = self.test_stats_full[0].iloc[:, 0].tolist()
self.prediction = np.asarray([
self.ground_truth_metadata[self.output_feature_name]['str2idx'][pred_row]
for pred_row in self.prediction_raw])
def test_experiment_image_inputs(csv_filename):
# Image Inputs
image_dest_folder = os.path.join(os.getcwd(), 'generated_images')
# Resnet encoder
input_features = [
image_feature(
folder=image_dest_folder,
encoder='resnet',
preprocessing={
'in_memory': True,
'height': 8,
'width': 8,
'num_channels': 3
},
fc_size=16,
num_filters=8
),
text_feature(encoder='embed', min_len=1),
numerical_feature(normalization='zscore')
]
output_features = [
category_feature(vocab_size=2, reduce_input='sum'),
numerical_feature()
]
rel_path = generate_data(input_features, output_features, csv_filename)
run_experiment(input_features, output_features, data_csv=rel_path)
# Stacked CNN encoder
input_features[0]['encoder'] = 'stacked_cnn'
rel_path = generate_data(input_features, output_features, csv_filename)
run_experiment(input_features, output_features, data_csv=rel_path)
# Stacked CNN encoder, in_memory = False
input_features[0]['preprocessing']['in_memory'] = False
rel_path = generate_data(input_features, output_features, csv_filename)
run_experiment(input_features, output_features, data_csv=rel_path)
# Delete the temporary data created
shutil.rmtree(image_dest_folder)
def test_server_integration(csv_filename):
# Image Inputs
image_dest_folder = os.path.join(os.getcwd(), 'generated_images')
# Resnet encoder
input_features = [
image_feature(folder=image_dest_folder,
encoder='resnet',
preprocessing={
'in_memory': True,
'height': 8,
'width': 8,
'num_channels': 3
},
fc_size=16,
num_filters=8),
text_feature(encoder='embed', min_len=1),
numerical_feature(normalization='zscore')
]
output_features = [
categorical_feature(vocab_size=2, reduce_input='sum'),
numerical_feature()
]
rel_path = generate_data(input_features, output_features, csv_filename)
model = train_model(input_features, output_features, data_csv=rel_path)
app = server(model)
client = TestClient(app)
response = client.post('/predict')
assert response.json() == ALL_FEATURES_PRESENT_ERROR
data_df = read_csv(rel_path)
data, files = convert_to_form(data_df.T.to_dict()[0])
response = client.post('/predict', data=data, files=files)
response_keys = sorted(list(response.json().keys()))
assert response_keys == sorted(output_keys_for(output_features))
shutil.rmtree(model.exp_dir_name, ignore_errors=True)
shutil.rmtree(image_dest_folder)
def test_experiment_infer_image_metadata(csv_filename: str):
# Image Inputs
image_dest_folder = os.path.join(os.getcwd(), "generated_images")
# Resnet encoder
input_features = [
image_feature(folder=image_dest_folder, encoder="stacked_cnn", fc_size=16, num_filters=8),
text_feature(encoder="embed", min_len=1),
numerical_feature(normalization="zscore"),
]
output_features = [category_feature(vocab_size=2, reduce_input="sum"), numerical_feature()]
rel_path = generate_data(input_features, output_features, csv_filename)
# remove image preprocessing section to force inferring image meta data
input_features[0].pop("preprocessing")
run_experiment(input_features, output_features, dataset=rel_path)
# Delete the temporary data created
shutil.rmtree(image_dest_folder)
def test_visual_question_answering(csv_filename):
image_dest_folder = os.path.join(os.getcwd(), 'generated_images')
input_features = [
image_feature(folder=image_dest_folder,
encoder='resnet',
preprocessing={
'in_memory': True,
'height': 8,
'width': 8,
'num_channels': 3
},
fc_size=8,
num_filters=8),
text_feature(encoder='embed', min_len=1, level='word'),
]
output_features = [sequence_feature(decoder='generator', cell_type='lstm')]
rel_path = generate_data(input_features, output_features, csv_filename)
run_experiment(input_features, output_features, data_csv=rel_path)
# Delete the temporary data created
shutil.rmtree(image_dest_folder)
def test_experiment_image_inputs(image_params: ImageParams, csv_filename: str):
# Image Inputs
image_dest_folder = os.path.join(os.getcwd(), "generated_images")
# Resnet encoder
input_features = [
image_feature(
folder=image_dest_folder,
encoder="resnet",
preprocessing={
"in_memory": True,
"height": 12,
"width": 12,
"num_channels": 3,
"num_processes": 5
},
fc_size=16,
num_filters=8,
),
text_feature(encoder="embed", min_len=1),
numerical_feature(normalization="zscore"),
]
output_features = [
category_feature(vocab_size=2, reduce_input="sum"),
numerical_feature()
]
input_features[0]["encoder"] = image_params.image_encoder
input_features[0]["preprocessing"][
"in_memory"] = image_params.in_memory_flag
rel_path = generate_data(input_features, output_features, csv_filename)
run_experiment(
input_features,
output_features,
dataset=rel_path,
skip_save_processed_input=image_params.skip_save_processed_input,
)
# Delete the temporary data created
shutil.rmtree(image_dest_folder)
def test_hyperopt_run_hyperopt(csv_filename):
with ray_start_4_cpus():
input_features = [
text_feature(name="utterance", cell_type="lstm", reduce_output="sum"),
category_feature(vocab_size=2, reduce_input="sum"),
]
output_features = [category_feature(vocab_size=2, reduce_input="sum")]
rel_path = generate_data(input_features, output_features, csv_filename)
config = {
"input_features": input_features,
"output_features": output_features,
"combiner": {"type": "concat", "num_fc_layers": 2},
"training": {"epochs": 2, "learning_rate": 0.001},
}
output_feature_name = output_features[0]["name"]
hyperopt_configs = {
"parameters": {
"training.learning_rate": {
"space": "loguniform",
"lower": 0.001,
"upper": 0.1,
},
output_feature_name + ".fc_size": {"space": "randint", "lower": 32, "upper": 256},
output_feature_name + ".num_fc_layers": {"space": "randint", "lower": 2, "upper": 6},
},
"goal": "minimize",
"output_feature": output_feature_name,
"validation_metrics": "loss",
"executor": {"type": "ray"},
"sampler": {"type": "ray", "num_samples": 2},
}
# add hyperopt parameter space to the config
config["hyperopt"] = hyperopt_configs
run_hyperopt(config, rel_path)
def test_experiment_image_inputs(image_parms: ImageParms, csv_filename: str):
# Image Inputs
image_dest_folder = os.path.join(os.getcwd(), 'generated_images')
# Resnet encoder
input_features = [
image_feature(
folder=image_dest_folder,
encoder='resnet',
preprocessing={
'in_memory': True,
'height': 12,
'width': 12,
'num_channels': 3,
'num_processes': 5
},
fc_size=16,
num_filters=8
),
text_feature(encoder='embed', min_len=1),
numerical_feature(normalization='zscore')
]
output_features = [
category_feature(vocab_size=2, reduce_input='sum'),
numerical_feature()
]
input_features[0]['encoder'] = image_parms.image_encoder
input_features[0]['preprocessing'][
'in_memory'] = image_parms.in_memory_flag
rel_path = generate_data(input_features, output_features, csv_filename)
run_experiment(
input_features,
output_features,
dataset=rel_path,
skip_save_processed_input=image_parms.skip_save_processed_input
)
# Delete the temporary data created
shutil.rmtree(image_dest_folder)
def test_experiment_image_inputs(image_params: ImageParams, tmpdir):
# Image Inputs
image_dest_folder = os.path.join(tmpdir, "generated_images")
# Resnet encoder
input_features = [
image_feature(
folder=image_dest_folder,
encoder="resnet",
preprocessing={
"in_memory": True,
"height": 12,
"width": 12,
"num_channels": 3,
"num_processes": 5
},
output_size=16,
num_filters=8,
),
text_feature(encoder="embed", min_len=1),
number_feature(normalization="zscore"),
]
output_features = [
category_feature(vocab_size=2, reduce_input="sum"),
number_feature()
]
input_features[0]["encoder"] = image_params.image_encoder
input_features[0]["preprocessing"][
"in_memory"] = image_params.in_memory_flag
rel_path = generate_data(input_features, output_features,
os.path.join(tmpdir, "dataset.csv"))
run_experiment(
input_features,
output_features,
dataset=rel_path,
skip_save_processed_input=image_params.skip_save_processed_input,
)
def test_visual_question_answering(tmpdir):
image_dest_folder = os.path.join(tmpdir, "generated_images")
input_features = [
image_feature(
folder=image_dest_folder,
encoder="resnet",
preprocessing={
"in_memory": True,
"height": 8,
"width": 8,
"num_channels": 3,
"num_processes": 5
},
output_size=8,
num_filters=8,
),
text_feature(encoder="embed", min_len=1),
]
output_features = [sequence_feature(decoder="generator", cell_type="lstm")]
rel_path = generate_data(input_features, output_features,
os.path.join(tmpdir, "dataset.csv"))
run_experiment(input_features, output_features, dataset=rel_path)
def test_sequence_tagger_text(
csv_filename
):
# Define input and output features
input_features = [
text_feature(
max_len=10,
encoder='rnn',
reduce_output=None
)
]
output_features = [
sequence_feature(
max_len=10,
decoder='tagger',
reduce_input=None
)
]
# Generate test data
rel_path = generate_data(input_features, output_features, csv_filename)
# run the experiment
run_experiment(input_features, output_features, dataset=rel_path)
def run_test_gbm_output_not_supported(tmpdir, backend_config):
"""Test that an error is raised when the output feature is not supported by the model."""
input_features = [number_feature(), category_feature(reduce_output="sum")]
output_features = [text_feature()]
csv_filename = os.path.join(tmpdir, "training.csv")
dataset_filename = generate_data(input_features,
output_features,
csv_filename,
num_examples=100)
config = {
MODEL_TYPE: "gbm",
"input_features": input_features,
"output_features": output_features
}
model = LudwigModel(config, backend=backend_config)
with pytest.raises(
ValueError,
match=
"Model type GBM only supports numerical, categorical, or binary output features"
):
model.train(dataset=dataset_filename, output_directory=tmpdir)
def test_visual_question_answering(csv_filename):
image_dest_folder = os.path.join(os.getcwd(), "generated_images")
input_features = [
image_feature(
folder=image_dest_folder,
encoder="resnet",
preprocessing={
"in_memory": True,
"height": 8,
"width": 8,
"num_channels": 3,
"num_processes": 5
},
fc_size=8,
num_filters=8,
),
text_feature(encoder="embed", min_len=1, level="word"),
]
output_features = [sequence_feature(decoder="generator", cell_type="lstm")]
rel_path = generate_data(input_features, output_features, csv_filename)
run_experiment(input_features, output_features, dataset=rel_path)
# Delete the temporary data created
shutil.rmtree(image_dest_folder)
def test_experiment_multiple_seq_seq(csv_filename):
# Multiple inputs, Multiple outputs
input_features = [
text_feature(vocab_size=100, min_len=1, encoder='stacked_cnn'),
numerical_feature(),
categorical_feature(vocab_size=10, embedding_size=5),
set_feature(),
sequence_feature(vocab_size=10, max_len=10, encoder='embed')
]
output_features = [
categorical_feature(vocab_size=2, reduce_input='sum'),
sequence_feature(vocab_size=10, max_len=5),
numerical_feature()
]
rel_path = generate_data(input_features, output_features, csv_filename)
run_experiment(input_features, output_features, data_csv=rel_path)
# Use generator as decoder
output_features = [
categorical_feature(vocab_size=2, reduce_input='sum'),
sequence_feature(vocab_size=10, max_len=5, decoder='generator'),
numerical_feature()
]
rel_path = generate_data(input_features, output_features, csv_filename)
run_experiment(input_features, output_features, data_csv=rel_path)
# Generator decoder and reduce_input = None
output_features = [
categorical_feature(vocab_size=2, reduce_input='sum'),
sequence_feature(max_len=5, decoder='generator', reduce_input=None),
numerical_feature()
]
rel_path = generate_data(input_features, output_features, csv_filename)
run_experiment(input_features, output_features, data_csv=rel_path)
sequence_feature(min_len=5,
max_len=10,
encoder='rnn',
cell_type='lstm',
reduce_output=None)
],
# output feature
[sequence_feature(max_len=10, decoder='tagger', reduce_input=None)]),
FeaturesToUse(
# input feature
[
numerical_feature(normalization='zscore'),
numerical_feature(normalization='zscore')
],
# output feature
[text_feature()]),
]
@pytest.mark.parametrize('features_to_use', FEATURES_TO_TEST)
def test_kfold_cv_cli(features_to_use: FeaturesToUse):
# k-fold cross validation cli
num_folds = 3
# setup temporary directory to run test
with tempfile.TemporaryDirectory() as tmpdir:
training_data_fp = os.path.join(tmpdir, 'train.csv')
config_fp = os.path.join(tmpdir, 'config.yaml')
results_dir = os.path.join(tmpdir, 'results')
statistics_fp = os.path.join(results_dir,
def test_model_save_reload_api(csv_filename, tmp_path):
tf.random.set_seed(1234)
image_dest_folder = os.path.join(os.getcwd(), 'generated_images')
audio_dest_folder = os.path.join(os.getcwd(), 'generated_audio')
input_features = [
binary_feature(),
numerical_feature(),
category_feature(vocab_size=3),
sequence_feature(vocab_size=3),
text_feature(vocab_size=3, encoder='rnn', cell_type='lstm',
num_layers=2, bidirections=True),
vector_feature(),
image_feature(image_dest_folder),
audio_feature(audio_dest_folder, encoder='stacked_cnn'),
timeseries_feature(encoder='parallel_cnn'),
sequence_feature(vocab_size=3, encoder='stacked_parallel_cnn'),
date_feature(),
h3_feature(),
set_feature(vocab_size=3),
bag_feature(vocab_size=3),
]
output_features = [
binary_feature(),
numerical_feature(),
category_feature(vocab_size=3),
sequence_feature(vocab_size=3),
text_feature(vocab_size=3),
set_feature(vocab_size=3),
vector_feature(),
]
# Generate test data
data_csv_path = generate_data(input_features, output_features,
csv_filename)
#############
# Train model
#############
config = {
'input_features': input_features,
'output_features': output_features,
'training': {'epochs': 2}
}
data_df = read_csv(data_csv_path)
data_df[SPLIT] = get_split(data_df)
training_set, test_set, validation_set = split_dataset_ttv(
data_df,
SPLIT
)
training_set = pd.DataFrame(training_set)
validation_set = pd.DataFrame(validation_set)
test_set = pd.DataFrame(test_set)
# create sub-directory to store results
results_dir = tmp_path / 'results'
results_dir.mkdir()
# perform initial model training
backend = LocalTestBackend()
ludwig_model1 = LudwigModel(config, backend=backend)
_, _, output_dir = ludwig_model1.train(
training_set=training_set,
validation_set=validation_set,
test_set=test_set,
output_directory='results' # results_dir
)
preds_1, _ = ludwig_model1.predict(dataset=validation_set)
def check_model_equal(ludwig_model2):
# Compare model predictions
preds_2, _ = ludwig_model2.predict(dataset=validation_set)
assert set(preds_1.keys()) == set(preds_2.keys())
for key in preds_1:
assert preds_1[key].dtype == preds_2[key].dtype, key
assert np.all(a == b for a, b in zip(preds_1[key], preds_2[key])), key
# assert preds_2[key].dtype == preds_3[key].dtype, key
# assert list(preds_2[key]) == list(preds_3[key]), key
# Compare model weights
# this has to be done after predicts because of TF2 lazy restoration
for if_name in ludwig_model1.model.input_features:
if1 = ludwig_model1.model.input_features[if_name]
if2 = ludwig_model2.model.input_features[if_name]
for if1_w, if2_w in zip(if1.encoder_obj.weights,
if2.encoder_obj.weights):
assert np.allclose(if1_w.numpy(), if2_w.numpy())
c1 = ludwig_model1.model.combiner
c2 = ludwig_model2.model.combiner
for c1_w, c2_w in zip(c1.weights, c2.weights):
assert np.allclose(c1_w.numpy(), c2_w.numpy())
for of_name in ludwig_model1.model.output_features:
of1 = ludwig_model1.model.output_features[of_name]
of2 = ludwig_model2.model.output_features[of_name]
for of1_w, of2_w in zip(of1.decoder_obj.weights,
of2.decoder_obj.weights):
assert np.allclose(of1_w.numpy(), of2_w.numpy())
# Test saving and loading the model explicitly
with tempfile.TemporaryDirectory() as tmpdir:
ludwig_model1.save(tmpdir)
ludwig_model_loaded = LudwigModel.load(tmpdir, backend=backend)
check_model_equal(ludwig_model_loaded)
# Test loading the model from the experiment directory
ludwig_model_exp = LudwigModel.load(
os.path.join(output_dir, 'model'),
backend=backend
)
check_model_equal(ludwig_model_exp)
def test_model_save_reload_api(tmpdir, csv_filename, tmp_path):
torch.manual_seed(1)
random.seed(1)
np.random.seed(1)
image_dest_folder = os.path.join(os.getcwd(), "generated_images")
audio_dest_folder = os.path.join(os.getcwd(), "generated_audio")
input_features = [
binary_feature(),
number_feature(),
category_feature(vocab_size=3),
sequence_feature(vocab_size=3),
text_feature(vocab_size=3,
encoder="rnn",
cell_type="lstm",
num_layers=2,
bidirections=True),
vector_feature(),
image_feature(image_dest_folder),
audio_feature(audio_dest_folder, encoder="stacked_cnn"),
timeseries_feature(encoder="parallel_cnn"),
sequence_feature(vocab_size=3, encoder="stacked_parallel_cnn"),
date_feature(),
h3_feature(),
set_feature(vocab_size=3),
bag_feature(vocab_size=3),
]
output_features = [
binary_feature(),
number_feature(),
category_feature(vocab_size=3),
sequence_feature(vocab_size=3),
text_feature(vocab_size=3),
set_feature(vocab_size=3),
vector_feature(),
]
# Generate test data
data_csv_path = generate_data(input_features,
output_features,
csv_filename,
num_examples=50)
#############
# Train model
#############
config = {
"input_features": input_features,
"output_features": output_features,
TRAINER: {
"epochs": 2
}
}
data_df = read_csv(data_csv_path)
splitter = get_splitter("random")
training_set, validation_set, test_set = splitter.split(
data_df, LocalTestBackend())
# create sub-directory to store results
results_dir = tmp_path / "results"
results_dir.mkdir()
# perform initial model training
backend = LocalTestBackend()
ludwig_model1 = LudwigModel(config, backend=backend)
_, _, output_dir = ludwig_model1.train(
training_set=training_set,
validation_set=validation_set,
test_set=test_set,
output_directory="results", # results_dir
)
preds_1, _ = ludwig_model1.predict(dataset=validation_set)
def check_model_equal(ludwig_model2):
# Compare model predictions
preds_2, _ = ludwig_model2.predict(dataset=validation_set)
assert set(preds_1.keys()) == set(preds_2.keys())
for key in preds_1:
assert preds_1[key].dtype == preds_2[key].dtype, key
assert np.all(a == b
for a, b in zip(preds_1[key], preds_2[key])), key
# assert preds_2[key].dtype == preds_3[key].dtype, key
# assert list(preds_2[key]) == list(preds_3[key]), key
# Compare model weights
for if_name in ludwig_model1.model.input_features:
if1 = ludwig_model1.model.input_features[if_name]
if2 = ludwig_model2.model.input_features[if_name]
for if1_w, if2_w in zip(if1.encoder_obj.parameters(),
if2.encoder_obj.parameters()):
assert torch.allclose(if1_w, if2_w)
c1 = ludwig_model1.model.combiner
c2 = ludwig_model2.model.combiner
for c1_w, c2_w in zip(c1.parameters(), c2.parameters()):
assert torch.allclose(c1_w, c2_w)
for of_name in ludwig_model1.model.output_features:
of1 = ludwig_model1.model.output_features[of_name]
of2 = ludwig_model2.model.output_features[of_name]
for of1_w, of2_w in zip(of1.decoder_obj.parameters(),
of2.decoder_obj.parameters()):
assert torch.allclose(of1_w, of2_w)
ludwig_model1.save(tmpdir)
ludwig_model_loaded = LudwigModel.load(tmpdir, backend=backend)
check_model_equal(ludwig_model_loaded)
# Test loading the model from the experiment directory
ludwig_model_exp = LudwigModel.load(os.path.join(output_dir, "model"),
backend=backend)
check_model_equal(ludwig_model_exp)
def run_hyperopt_executor(sampler,
executor,
csv_filename,
validate_output_feature=False,
validation_metric=None):
input_features = [
text_feature(name="utterance", cell_type="lstm", reduce_output="sum"),
category_feature(vocab_size=2, reduce_input="sum")
]
output_features = [category_feature(vocab_size=2, reduce_input="sum")]
rel_path = generate_data(input_features, output_features, csv_filename)
config = {
"input_features": input_features,
"output_features": output_features,
"combiner": {
"type": "concat",
"num_fc_layers": 2
},
"training": {
"epochs": 2,
"learning_rate": 0.001
},
"hyperopt": {
**HYPEROPT_CONFIG,
"executor": executor,
"sampler": sampler,
},
}
config = merge_with_defaults(config)
hyperopt_config = config["hyperopt"]
if validate_output_feature:
hyperopt_config['output_feature'] = output_features[0]['name']
if validation_metric:
hyperopt_config['validation_metric'] = validation_metric
update_hyperopt_params_with_defaults(hyperopt_config)
parameters = hyperopt_config["parameters"]
if sampler.get("search_alg", {}).get("type", "") == 'bohb':
# bohb does not support grid_search search space
del parameters['utterance.cell_type']
split = hyperopt_config["split"]
output_feature = hyperopt_config["output_feature"]
metric = hyperopt_config["metric"]
goal = hyperopt_config["goal"]
hyperopt_sampler = get_build_hyperopt_sampler(sampler["type"])(goal,
parameters,
**sampler)
hyperopt_executor = get_build_hyperopt_executor(executor["type"])(
hyperopt_sampler, output_feature, metric, split, **executor)
hyperopt_executor.execute(config, dataset=rel_path)
def test_hyperopt_run_hyperopt(csv_filename, samplers):
input_features = [
text_feature(name="utterance", cell_type="lstm", reduce_output="sum"),
category_feature(vocab_size=2, reduce_input="sum")
]
output_features = [category_feature(vocab_size=2, reduce_input="sum")]
rel_path = generate_data(input_features, output_features, csv_filename)
config = {
"input_features": input_features,
"output_features": output_features,
"combiner": {
"type": "concat",
"num_fc_layers": 2
},
"training": {
"epochs": 2,
"learning_rate": 0.001
}
}
output_feature_name = output_features[0]['name']
hyperopt_configs = {
"parameters": {
"training.learning_rate": {
"type": "float",
"low": 0.0001,
"high": 0.01,
"space": "log",
"steps": 3,
},
output_feature_name + ".fc_layers": {
'type':
'category',
'values': [[{
'fc_size': 512
}, {
'fc_size': 256
}], [{
'fc_size': 512
}], [{
'fc_size': 256
}]]
},
output_feature_name + ".fc_size": {
"type": "int",
"low": 32,
"high": 256,
"steps": 5
},
output_feature_name + ".num_fc_layers": {
'type': 'int',
'low': 1,
'high': 5,
'space': 'linear',
'steps': 4
}
},
"goal": "minimize",
'output_feature': output_feature_name,
'validation_metrics': 'loss',
'executor': {
'type': 'serial'
},
'sampler': {
'type': samplers["type"],
'num_samples': 2
}
}
# add hyperopt parameter space to the config
config['hyperopt'] = hyperopt_configs
hyperopt_results = hyperopt(config,
dataset=rel_path,
output_directory='results_hyperopt')
# check for return results
assert isinstance(hyperopt_results, list)
# check for existence of the hyperopt statistics file
assert os.path.isfile(
os.path.join('results_hyperopt', 'hyperopt_statistics.json'))
if os.path.isfile(
os.path.join('results_hyperopt', 'hyperopt_statistics.json')):
os.remove(os.path.join('results_hyperopt', 'hyperopt_statistics.json'))
def test_torchscript(csv_filename, should_load_model):
#######
# Setup
#######
with tempfile.TemporaryDirectory() as tmpdir:
dir_path = tmpdir
data_csv_path = os.path.join(tmpdir, csv_filename)
image_dest_folder = os.path.join(tmpdir, "generated_images")
audio_dest_folder = os.path.join(tmpdir, "generated_audio")
# Single sequence input, single category output
input_features = [
binary_feature(),
numerical_feature(),
category_feature(vocab_size=3),
sequence_feature(vocab_size=3),
text_feature(vocab_size=3),
vector_feature(),
image_feature(image_dest_folder),
audio_feature(audio_dest_folder),
timeseries_feature(),
date_feature(),
date_feature(),
h3_feature(),
set_feature(vocab_size=3),
bag_feature(vocab_size=3),
]
output_features = [
category_feature(vocab_size=3),
binary_feature(),
numerical_feature(),
set_feature(vocab_size=3),
vector_feature()
# TODO(#1333): Re-enable.
# sequence_feature(vocab_size=3),
# text_feature(vocab_size=3),
]
predictions_column_name = "{}_predictions".format(output_features[0]["name"])
# Generate test data
data_csv_path = generate_data(input_features, output_features, data_csv_path)
#############
# Train model
#############
backend = LocalTestBackend()
config = {"input_features": input_features, "output_features": output_features, "training": {"epochs": 2}}
ludwig_model = LudwigModel(config, backend=backend)
ludwig_model.train(
dataset=data_csv_path,
skip_save_training_description=True,
skip_save_training_statistics=True,
skip_save_model=True,
skip_save_progress=True,
skip_save_log=True,
skip_save_processed_input=True,
)
###################
# save Ludwig model
###################
ludwigmodel_path = os.path.join(dir_path, "ludwigmodel")
shutil.rmtree(ludwigmodel_path, ignore_errors=True)
ludwig_model.save(ludwigmodel_path)
###################
# load Ludwig model
###################
if should_load_model:
ludwig_model = LudwigModel.load(ludwigmodel_path, backend=backend)
##############################
# collect weight tensors names
##############################
original_predictions_df, _ = ludwig_model.predict(dataset=data_csv_path)
original_weights = deepcopy(list(ludwig_model.model.parameters()))
#################
# save torchscript
#################
torchscript_path = os.path.join(dir_path, "torchscript")
shutil.rmtree(torchscript_path, ignore_errors=True)
ludwig_model.model.save_torchscript(torchscript_path)
###################################################
# load Ludwig model, obtain predictions and weights
###################################################
ludwig_model = LudwigModel.load(ludwigmodel_path, backend=backend)
loaded_prediction_df, _ = ludwig_model.predict(dataset=data_csv_path)
loaded_weights = deepcopy(list(ludwig_model.model.parameters()))
#####################################################
# restore torchscript, obtain predictions and weights
#####################################################
training_set_metadata_json_fp = os.path.join(ludwigmodel_path, TRAIN_SET_METADATA_FILE_NAME)
dataset, training_set_metadata = preprocess_for_prediction(
ludwig_model.config,
dataset=data_csv_path,
training_set_metadata=training_set_metadata_json_fp,
backend=backend,
)
restored_model = torch.jit.load(torchscript_path)
# Check the outputs for one of the features for correctness
# Here we choose the first output feature (categorical)
of_name = list(ludwig_model.model.output_features.keys())[0]
data_to_predict = {
name: torch.from_numpy(dataset.dataset[feature.proc_column])
for name, feature in ludwig_model.model.input_features.items()
}
# Get predictions from restored torchscript.
logits = restored_model(data_to_predict)
restored_predictions = torch.argmax(
output_feature_utils.get_output_feature_tensor(logits, of_name, "logits"), -1
)
restored_predictions = [training_set_metadata[of_name]["idx2str"][idx] for idx in restored_predictions]
restored_weights = deepcopy(list(restored_model.parameters()))
#########
# Cleanup
#########
shutil.rmtree(ludwigmodel_path, ignore_errors=True)
shutil.rmtree(torchscript_path, ignore_errors=True)
###############################################
# Check if weights and predictions are the same
###############################################
# Check to weight values match the original model.
assert utils.is_all_close(original_weights, loaded_weights)
assert utils.is_all_close(original_weights, restored_weights)
# Check that predictions are identical to the original model.
assert np.all(original_predictions_df[predictions_column_name] == loaded_prediction_df[predictions_column_name])
assert np.all(original_predictions_df[predictions_column_name] == restored_predictions)
def test_experiment_seq_seq_tagger(csv_filename, encoder):
input_features = [text_feature(reduce_output=None, encoder=encoder)]
output_features = [text_feature(decoder="tagger")]
rel_path = generate_data(input_features, output_features, csv_filename)
run_experiment(input_features, output_features, dataset=rel_path)
def test_server_integration(csv_filename):
# Image Inputs
image_dest_folder = os.path.join(os.getcwd(), 'generated_images')
# Resnet encoder
input_features = [
image_feature(folder=image_dest_folder,
preprocessing={
'in_memory': True,
'height': 8,
'width': 8,
'num_channels': 3
},
fc_size=16,
num_filters=8),
text_feature(encoder='embed', min_len=1),
numerical_feature(normalization='zscore')
]
output_features = [category_feature(vocab_size=2), numerical_feature()]
rel_path = generate_data(input_features, output_features, csv_filename)
model, output_dir = train_model(input_features,
output_features,
data_csv=rel_path)
app = server(model)
client = TestClient(app)
response = client.get('/')
assert response.status_code == 200
response = client.post('/predict')
assert response.json() == ALL_FEATURES_PRESENT_ERROR
data_df = read_csv(rel_path)
# One-off prediction
first_entry = data_df.T.to_dict()[0]
data, files = convert_to_form(first_entry)
server_response = client.post('/predict', data=data, files=files)
server_response = server_response.json()
server_response_keys = sorted(list(server_response.keys()))
assert server_response_keys == sorted(output_keys_for(output_features))
model_output, _ = model.predict(dataset=[first_entry], data_format=dict)
model_output = model_output.to_dict('records')[0]
assert model_output == server_response
# Batch prediction
assert len(data_df) > 1
files = convert_to_batch_form(data_df)
server_response = client.post('/batch_predict', files=files)
server_response = server_response.json()
server_response_keys = sorted(server_response['columns'])
assert server_response_keys == sorted(output_keys_for(output_features))
assert len(data_df) == len(server_response['data'])
model_output, _ = model.predict(dataset=data_df)
model_output = model_output.to_dict('split')
assert model_output == server_response
# Cleanup
shutil.rmtree(output_dir, ignore_errors=True)
shutil.rmtree(image_dest_folder, ignore_errors=True)
sequence_feature(
max_len=10,
decoder='tagger',
reduce_input=None
)
]
),
FeaturesToUse(
# input feature
[
numerical_feature(normalization='zscore'),
numerical_feature(normalization='zscore')
],
# output feature
[
text_feature()
]
),
]
@pytest.mark.parametrize('features_to_use', FEATURES_TO_TEST)
def test_kfold_cv_cli(features_to_use: FeaturesToUse):
# k-fold cross validation cli
num_folds = 3
# setup temporary directory to run test
with tempfile.TemporaryDirectory() as tmpdir:
training_data_fp = os.path.join(tmpdir, 'train.csv')
config_fp = os.path.join(tmpdir, 'config.yaml')
def test_savedmodel(csv_filename, should_load_model):
#######
# Setup
#######
with tempfile.TemporaryDirectory() as tmpdir:
dir_path = tmpdir
data_csv_path = os.path.join(tmpdir, csv_filename)
image_dest_folder = os.path.join(tmpdir, 'generated_images')
audio_dest_folder = os.path.join(tmpdir, 'generated_audio')
# Single sequence input, single category output
input_features = [
binary_feature(),
numerical_feature(),
category_feature(vocab_size=3),
sequence_feature(vocab_size=3),
text_feature(vocab_size=3),
vector_feature(),
image_feature(image_dest_folder),
audio_feature(audio_dest_folder),
timeseries_feature(),
date_feature(),
h3_feature(),
set_feature(vocab_size=3),
bag_feature(vocab_size=3),
]
output_features = [
category_feature(vocab_size=3),
binary_feature(),
numerical_feature(),
sequence_feature(vocab_size=3),
text_feature(vocab_size=3),
set_feature(vocab_size=3),
vector_feature()
]
predictions_column_name = '{}_predictions'.format(
output_features[0]['name'])
# Generate test data
data_csv_path = generate_data(input_features, output_features,
data_csv_path)
#############
# Train model
#############
backend = LocalTestBackend()
config = {
'input_features': input_features,
'output_features': output_features,
'training': {
'epochs': 2
}
}
ludwig_model = LudwigModel(config, backend=backend)
ludwig_model.train(
dataset=data_csv_path,
skip_save_training_description=True,
skip_save_training_statistics=True,
skip_save_model=True,
skip_save_progress=True,
skip_save_log=True,
skip_save_processed_input=True,
)
###################
# save Ludwig model
###################
ludwigmodel_path = os.path.join(dir_path, 'ludwigmodel')
shutil.rmtree(ludwigmodel_path, ignore_errors=True)
ludwig_model.save(ludwigmodel_path)
###################
# load Ludwig model
###################
if should_load_model:
ludwig_model = LudwigModel.load(ludwigmodel_path, backend=backend)
##############################
# collect weight tensors names
##############################
original_predictions_df, _ = ludwig_model.predict(
dataset=data_csv_path)
original_weights = deepcopy(ludwig_model.model.trainable_variables)
#################
# save savedmodel
#################
savedmodel_path = os.path.join(dir_path, 'savedmodel')
shutil.rmtree(savedmodel_path, ignore_errors=True)
ludwig_model.model.save_savedmodel(savedmodel_path)
###################################################
# load Ludwig model, obtain predictions and weights
###################################################
ludwig_model = LudwigModel.load(ludwigmodel_path, backend=backend)
loaded_prediction_df, _ = ludwig_model.predict(dataset=data_csv_path)
loaded_weights = deepcopy(ludwig_model.model.trainable_variables)
#################################################
# restore savedmodel, obtain predictions and weights
#################################################
training_set_metadata_json_fp = os.path.join(
ludwigmodel_path, TRAIN_SET_METADATA_FILE_NAME)
dataset, training_set_metadata = preprocess_for_prediction(
ludwig_model.config,
dataset=data_csv_path,
training_set_metadata=training_set_metadata_json_fp,
backend=backend,
)
restored_model = tf.saved_model.load(savedmodel_path)
# Check the outputs for one of the features for correctness
# Here we choose the first output feature (categorical)
of_name = list(ludwig_model.model.output_features.keys())[0]
data_to_predict = {
name: tf.convert_to_tensor(dataset.dataset[feature.proc_column],
dtype=feature.get_input_dtype())
for name, feature in ludwig_model.model.input_features.items()
}
logits = restored_model(data_to_predict, False, None)
restored_predictions = tf.argmax(logits[of_name]['logits'],
-1,
name='predictions_{}'.format(of_name))
restored_predictions = tf.map_fn(
lambda idx: training_set_metadata[of_name]['idx2str'][idx],
restored_predictions,
dtype=tf.string)
restored_weights = deepcopy(restored_model.trainable_variables)
#########
# Cleanup
#########
shutil.rmtree(ludwigmodel_path, ignore_errors=True)
shutil.rmtree(savedmodel_path, ignore_errors=True)
###############################################
# Check if weights and predictions are the same
###############################################
# check for same number of weights as original model
assert len(original_weights) == len(loaded_weights)
assert len(original_weights) == len(restored_weights)
# check to ensure weight valuess match the original model
loaded_weights_match = np.all([
np.all(
np.isclose(original_weights[i].numpy(),
loaded_weights[i].numpy()))
for i in range(len(original_weights))
])
original_weights = sorted(original_weights, key=lambda w: w.name)
restored_weights = sorted(restored_weights, key=lambda w: w.name)
restored_weights_match = np.all([
np.all(
np.isclose(original_weights[i].numpy(),
restored_weights[i].numpy()))
for i in range(len(original_weights))
])
assert loaded_weights_match and restored_weights_match
# Are predictions identical to original ones?
loaded_predictions_match = np.all(
original_predictions_df[predictions_column_name] ==
loaded_prediction_df[predictions_column_name])
restored_predictions_match = np.all(
original_predictions_df[predictions_column_name] ==
restored_predictions.numpy().astype('str'))
assert loaded_predictions_match and restored_predictions_match
def hyperopt_results():
"""
This function generates hyperopt results
"""
input_features = [
text_feature(name="utterance", cell_type="lstm", reduce_output="sum"),
category_feature(vocab_size=2, reduce_input="sum")]
output_features = [category_feature(vocab_size=2, reduce_input="sum")]
csv_filename = uuid.uuid4().hex[:10].upper() + '.csv'
rel_path = generate_data(input_features, output_features, csv_filename)
config = {
"input_features": input_features,
"output_features": output_features,
"combiner": {"type": "concat", "num_fc_layers": 2},
"training": {"epochs": 2, "learning_rate": 0.001}
}
output_feature_name = output_features[0]['name']
hyperopt_configs = {
"parameters": {
"training.learning_rate": {
"type": "float",
"low": 0.0001,
"high": 0.01,
"space": "log",
"steps": 3,
},
output_feature_name + ".fc_size": {
"type": "int",
"low": 32,
"high": 256,
"steps": 5
},
output_feature_name + ".num_fc_layers": {
'type': 'int',
'low': 1,
'high': 5,
'space': 'linear',
'steps': 4
}
},
"goal": "minimize",
'output_feature': output_feature_name,
'validation_metrics': 'loss',
'executor': {'type': 'serial'},
'sampler': {'type': 'random', 'num_samples': 2}
}
# add hyperopt parameter space to the config
config['hyperopt'] = hyperopt_configs
hyperopt(
config,
dataset=rel_path,
output_directory='results'
)
return os.path.abspath('results')
def test_confidence_thresholding_2thresholds_3d_vis_api(csv_filename):
"""Ensure pdf and png figures can be saved via visualization API call.
:param csv_filename: csv fixture from tests.fixtures.filenames.csv_filename
:return: None
"""
input_features = [
text_feature(vocab_size=10, min_len=1, encoder='stacked_cnn'),
numerical_feature(),
category_feature(vocab_size=10, embedding_size=5),
set_feature(),
sequence_feature(vocab_size=10, max_len=10, encoder='embed')
]
output_features = [
category_feature(vocab_size=2, reduce_input='sum'),
category_feature(vocab_size=2, reduce_input='sum')
]
encoder = 'parallel_cnn'
with TemporaryDirectory() as tmpvizdir:
# Generate test data
data_csv = generate_data(input_features, output_features,
os.path.join(tmpvizdir, csv_filename))
input_features[0]['encoder'] = encoder
model = run_api_experiment(input_features, output_features)
test_df, train_df, val_df = obtain_df_splits(data_csv)
_, _, output_dir = model.train(
training_set=train_df,
validation_set=val_df,
output_directory=os.path.join(tmpvizdir, 'results')
)
test_stats, predictions, _ = model.evaluate(
dataset=test_df,
collect_predictions=True,
output_directory=output_dir
)
output_feature_name1 = output_features[0]['name']
output_feature_name2 = output_features[1]['name']
# probabilities need to be list of lists containing each row data from the
# probability columns ref: https://ludwig-ai.github.io/ludwig-docs/api/#test - Return
probability1 = predictions.iloc[:, [2, 3, 4]].values
probability2 = predictions.iloc[:, [7, 8, 9]].values
ground_truth_metadata = model.training_set_metadata
target_predictions1 = test_df[output_feature_name1]
target_predictions2 = test_df[output_feature_name2]
ground_truth1 = np.asarray([
ground_truth_metadata[output_feature_name1]['str2idx'][prediction]
for prediction in target_predictions1
])
ground_truth2 = np.asarray([
ground_truth_metadata[output_feature_name2]['str2idx'][prediction]
for prediction in target_predictions2
])
viz_outputs = ('pdf', 'png')
for viz_output in viz_outputs:
vis_output_pattern_pdf = os.path.join(
output_dir, '*.{}'.format(viz_output)
)
visualize.confidence_thresholding_2thresholds_3d(
[probability1, probability2],
[ground_truth1, ground_truth2],
model.training_set_metadata,
[output_feature_name1, output_feature_name2],
labels_limit=0,
output_directory=output_dir,
file_format=viz_output
)
figure_cnt = glob.glob(vis_output_pattern_pdf)
assert 1 == len(figure_cnt)
def test_hyperopt_run_hyperopt(csv_filename, ray_start_4_cpus):
input_features = [
text_feature(name="utterance", cell_type="lstm", reduce_output="sum"),
category_feature(vocab_size=2, reduce_input="sum")
]
output_features = [category_feature(vocab_size=2, reduce_input="sum")]
rel_path = generate_data(input_features, output_features, csv_filename)
config = {
"input_features": input_features,
"output_features": output_features,
"combiner": {
"type": "concat",
"num_fc_layers": 2
},
"training": {
"epochs": 2,
"learning_rate": 0.001
}
}
output_feature_name = output_features[0]['name']
hyperopt_configs = {
"parameters": {
"training.learning_rate": {
"space": "loguniform",
"lower": 0.001,
"upper": 0.1,
},
output_feature_name + ".fc_size": {
"space": "randint",
"lower": 32,
"upper": 256
},
output_feature_name + ".num_fc_layers": {
"space": "randint",
"lower": 2,
"upper": 6
}
},
"goal": "minimize",
'output_feature': output_feature_name,
'validation_metrics': 'loss',
'executor': {
'type': 'ray'
},
'sampler': {
'type': 'ray',
'num_samples': 2
}
}
# add hyperopt parameter space to the config
config['hyperopt'] = hyperopt_configs
hyperopt_results = hyperopt(config,
dataset=rel_path,
output_directory='results_hyperopt')
# check for return results
assert isinstance(hyperopt_results, list)
# check for existence of the hyperopt statistics file
assert os.path.isfile(
os.path.join('results_hyperopt', 'hyperopt_statistics.json'))
def t_neuropod(csv_filename):
#######
# Setup
#######
dir_path = os.path.dirname(csv_filename)
image_dest_folder = os.path.join(os.getcwd(), 'generated_images')
audio_dest_folder = os.path.join(os.getcwd(), 'generated_audio')
input_features = [
binary_feature(),
numerical_feature(),
category_feature(vocab_size=3),
sequence_feature(vocab_size=3),
text_feature(vocab_size=3),
vector_feature(),
image_feature(image_dest_folder),
audio_feature(audio_dest_folder),
timeseries_feature(),
date_feature(),
h3_feature(),
set_feature(vocab_size=3),
bag_feature(vocab_size=3),
]
output_features = [
binary_feature(),
numerical_feature(),
category_feature(vocab_size=3),
sequence_feature(vocab_size=3),
text_feature(vocab_size=3),
set_feature(vocab_size=3),
vector_feature()
]
# Generate test data
data_csv_path = generate_data(input_features, output_features,
csv_filename)
#############
# Train model
#############
model_definition = {
'input_features': input_features,
'output_features': output_features,
'training': {
'epochs': 2
}
}
ludwig_model = LudwigModel(model_definition)
ludwig_model.train(
data_csv=data_csv_path,
skip_save_training_description=True,
skip_save_training_statistics=True,
skip_save_model=True,
skip_save_progress=True,
skip_save_log=True,
skip_save_processed_input=True,
)
original_predictions_df = ludwig_model.predict(data_csv=data_csv_path)
###################
# save Ludwig model
###################
ludwigmodel_path = os.path.join(dir_path, 'ludwigmodel')
shutil.rmtree(ludwigmodel_path, ignore_errors=True)
ludwig_model.save(ludwigmodel_path)
################
# build neuropod
################
neuropod_path = os.path.join(dir_path, 'neuropod')
export_neuropod(ludwigmodel_path, neuropod_path=neuropod_path)
########################
# predict using neuropod
########################
data_df = pd.read_csv(data_csv_path)
if_dict = {
input_feature['name']: np.expand_dims(
np.array([str(x) for x in data_df[input_feature['name']].tolist()],
dtype='str'), 1)
for input_feature in input_features
}
from neuropod.loader import load_neuropod
neuropod_model = load_neuropod(neuropod_path)
preds = neuropod_model.infer(if_dict)
for key in preds:
preds[key] = np.squeeze(preds[key])
#########
# cleanup
#########
# Delete the temporary data created
for path in [
ludwigmodel_path, neuropod_path, image_dest_folder,
audio_dest_folder
]:
if os.path.exists(path):
if os.path.isfile(path):
os.remove(path)
else:
shutil.rmtree(path, ignore_errors=True)
########
# checks
########
for output_feature in output_features:
output_feature_name = output_feature['name']
output_feature_type = output_feature['type']
if (output_feature_name + "_predictions" in preds
and output_feature_name + "_predictions"
in original_predictions_df):
neuropod_pred = preds[output_feature_name +
"_predictions"].tolist()
if output_feature_type == BINARY:
neuropod_pred = list(map(lambda x: str2bool(x), neuropod_pred))
if output_feature_type in {SEQUENCE, TEXT, SET}:
neuropod_pred = list(map(lambda x: x.split(), neuropod_pred))
original_pred = original_predictions_df[output_feature_name +
"_predictions"].tolist()
assert neuropod_pred == original_pred
if (output_feature_name + "_probability" in preds
and output_feature_name + "_probability"
in original_predictions_df):
neuropod_prob = preds[output_feature_name +
"_probability"].tolist()
if output_feature_type in {SEQUENCE, TEXT, SET}:
neuropod_prob = list(
map(lambda x: [float(n) for n in x.split()],
neuropod_prob))
if any(isinstance(el, list) for el in neuropod_prob):
neuropod_prob = np.array(
list(itertools.zip_longest(*neuropod_prob, fillvalue=0))).T
original_prob = original_predictions_df[output_feature_name +
"_probability"].tolist()
if any(isinstance(el, list) for el in original_prob):
original_prob = np.array(
list(itertools.zip_longest(*original_prob, fillvalue=0))).T
assert np.isclose(neuropod_prob, original_prob).all()
if (output_feature_name + "_probabilities" in preds
and output_feature_name + "_probabilities"
in original_predictions_df):
neuropod_prob = preds[output_feature_name +
"_probabilities"].tolist()
original_prob = original_predictions_df[output_feature_name +
"_probabilities"].tolist()
assert np.isclose(neuropod_prob, original_prob).all()
def test_hyperopt_run_hyperopt(csv_filename, samplers):
input_features = [
text_feature(name="utterance", cell_type="lstm", reduce_output="sum"),
category_feature(vocab_size=2, reduce_input="sum"),
]
output_features = [category_feature(vocab_size=2, reduce_input="sum")]
rel_path = generate_data(input_features, output_features, csv_filename)
config = {
"input_features": input_features,
"output_features": output_features,
"combiner": {
"type": "concat",
"num_fc_layers": 2
},
TRAINER: {
"epochs": 2,
"learning_rate": 0.001
},
}
output_feature_name = output_features[0]["name"]
hyperopt_configs = {
"parameters": {
"trainer.learning_rate": {
"type": "float",
"low": 0.0001,
"high": 0.01,
"space": "log",
"steps": 3,
},
output_feature_name + ".fc_layers": {
"type":
"category",
"values": [
[{
"output_size": 64
}, {
"output_size": 32
}],
[{
"output_size": 64
}],
[{
"output_size": 32
}],
],
},
output_feature_name + ".output_size": {
"type": "int",
"low": 16,
"high": 36,
"steps": 5
},
output_feature_name + ".num_fc_layers": {
"type": "int",
"low": 1,
"high": 5,
"space": "linear",
"steps": 4
},
},
"goal": "minimize",
"output_feature": output_feature_name,
"validation_metrics": "loss",
"executor": {
"type": "serial"
},
"sampler": {
"type": samplers["type"],
"num_samples": 2
},
}
# add hyperopt parameter space to the config
config["hyperopt"] = hyperopt_configs
hyperopt_results = hyperopt(config,
dataset=rel_path,
output_directory="results_hyperopt")
# check for return results
assert isinstance(hyperopt_results, HyperoptResults)
# check for existence of the hyperopt statistics file
assert os.path.isfile(
os.path.join("results_hyperopt", "hyperopt_statistics.json"))
if os.path.isfile(
os.path.join("results_hyperopt", "hyperopt_statistics.json")):
os.remove(os.path.join("results_hyperopt", "hyperopt_statistics.json"))
