def test_experiment_dataset_formats(data_format):
# primary focus of this test is to determine if exceptions are
# raised for different data set formats and in_memory setting
input_features = [
numerical_feature(),
category_feature()
]
output_features = [
category_feature(),
numerical_feature()
]
config = {
'input_features': input_features,
'output_features': output_features,
'combiner': {
'type': 'concat',
'fc_size': 14
},
'preprocessing': {},
'training': {'epochs': 2}
}
# create temporary name for train and test data sets
csv_filename = 'train_' + uuid.uuid4().hex[:10].upper() + '.csv'
# setup training data format to test
raw_data = generate_data(input_features, output_features,
csv_filename)
training_set_metadata = None
if data_format == 'hdf5':
# hdf5 format
training_set, _, _, training_set_metadata = preprocess_for_training(
config,
dataset=raw_data
)
dataset_to_use = training_set.data_hdf5_fp
else:
dataset_to_use = create_data_set_to_use(data_format, raw_data)
# define Ludwig model
model = LudwigModel(
config=config
)
model.train(
dataset=dataset_to_use,
training_set_metadata=training_set_metadata,
random_seed=default_random_seed
)
# # run functions with the specified data format
model.evaluate(dataset=dataset_to_use)
model.predict(dataset=dataset_to_use)
# Delete the temporary data created
delete_temporary_data(csv_filename)
def test_experiment_image_dataset(train_format, train_in_memory, test_format,
test_in_memory):
# primary focus of this test is to determine if exceptions are
# raised for different data set formats and in_memory setting
# Image Inputs
image_dest_folder = os.path.join(os.getcwd(), 'generated_images')
input_features = [
image_feature(folder=image_dest_folder,
encoder='stacked_cnn',
preprocessing={
'in_memory': True,
'height': 12,
'width': 12,
'num_channels': 3,
'num_processes': 5
},
fc_size=16,
num_filters=8),
]
output_features = [
category_feature(vocab_size=2, reduce_input='sum'),
]
config = {
'input_features': input_features,
'output_features': output_features,
'combiner': {
'type': 'concat',
'fc_size': 14
},
'preprocessing': {},
'training': {
'epochs': 2
}
}
# create temporary name for train and test data sets
train_csv_filename = 'train_' + uuid.uuid4().hex[:10].upper() + '.csv'
test_csv_filename = 'test_' + uuid.uuid4().hex[:10].upper() + '.csv'
# setup training data format to test
train_data = generate_data(input_features, output_features,
train_csv_filename)
config['input_features'][0]['preprocessing']['in_memory'] \
= train_in_memory
training_set_metadata = None
if train_format == 'hdf5':
# hdf5 format
train_set, _, _, training_set_metadata = preprocess_for_training(
config, dataset=train_data)
train_dataset_to_use = train_set.data_hdf5_fp
else:
train_dataset_to_use = create_data_set_to_use(train_format, train_data)
# define Ludwig model
model = LudwigModel(config=config, )
model.train(dataset=train_dataset_to_use,
training_set_metadata=training_set_metadata)
model.config['input_features'][0]['preprocessing']['in_memory'] \
= test_in_memory
# setup test data format to test
test_data = generate_data(input_features, output_features,
test_csv_filename)
if test_format == 'hdf5':
# hdf5 format
# create hdf5 data set
_, test_set, _, training_set_metadata_for_test = preprocess_for_training(
model.config, dataset=test_data)
test_dataset_to_use = test_set.data_hdf5_fp
else:
test_dataset_to_use = create_data_set_to_use(test_format, test_data)
# run functions with the specified data format
model.evaluate(dataset=test_dataset_to_use)
model.predict(dataset=test_dataset_to_use)
# Delete the temporary data created
shutil.rmtree(image_dest_folder)
delete_temporary_data(train_csv_filename)
delete_temporary_data(test_csv_filename)
def test_experiment_image_dataset(train_format, train_in_memory, test_format, test_in_memory):
# primary focus of this test is to determine if exceptions are
# raised for different data set formats and in_memory setting
# Image Inputs
image_dest_folder = os.path.join(os.getcwd(), "generated_images")
input_features = [
image_feature(
folder=image_dest_folder,
encoder="stacked_cnn",
preprocessing={"in_memory": True, "height": 12, "width": 12, "num_channels": 3, "num_processes": 5},
fc_size=16,
num_filters=8,
),
]
output_features = [
category_feature(vocab_size=2, reduce_input="sum"),
]
config = {
"input_features": input_features,
"output_features": output_features,
"combiner": {"type": "concat", "fc_size": 14},
"preprocessing": {},
"training": {"epochs": 2},
}
# create temporary name for train and test data sets
train_csv_filename = "train_" + uuid.uuid4().hex[:10].upper() + ".csv"
test_csv_filename = "test_" + uuid.uuid4().hex[:10].upper() + ".csv"
# setup training data format to test
train_data = generate_data(input_features, output_features, train_csv_filename)
config["input_features"][0]["preprocessing"]["in_memory"] = train_in_memory
training_set_metadata = None
backend = LocalTestBackend()
if train_format == "hdf5":
# hdf5 format
train_set, _, _, training_set_metadata = preprocess_for_training(
config,
dataset=train_data,
backend=backend,
)
train_dataset_to_use = train_set.data_hdf5_fp
else:
train_dataset_to_use = create_data_set_to_use(train_format, train_data)
# define Ludwig model
model = LudwigModel(
config=config,
backend=backend,
)
model.train(dataset=train_dataset_to_use, training_set_metadata=training_set_metadata)
model.config["input_features"][0]["preprocessing"]["in_memory"] = test_in_memory
# setup test data format to test
test_data = generate_data(input_features, output_features, test_csv_filename)
if test_format == "hdf5":
# hdf5 format
# create hdf5 data set
_, test_set, _, training_set_metadata_for_test = preprocess_for_training(
model.config,
dataset=test_data,
backend=backend,
)
test_dataset_to_use = test_set.data_hdf5_fp
else:
test_dataset_to_use = create_data_set_to_use(test_format, test_data)
# run functions with the specified data format
model.evaluate(dataset=test_dataset_to_use)
model.predict(dataset=test_dataset_to_use)
# Delete the temporary data created
shutil.rmtree(image_dest_folder)
delete_temporary_data(train_csv_filename)
delete_temporary_data(test_csv_filename)
