def split(
self,
df: DataFrame,
backend: Backend,
random_seed: float = default_random_seed
) -> Tuple[DataFrame, DataFrame, DataFrame]:
df[self.column] = df[self.column].astype(np.int8)
dfs = split_dataset_ttv(df, self.column)
train, test, val = tuple(
df.drop(columns=self.column) if df is not None else None
for df in dfs)
return train, val, test
def obtain_df_splits(data_csv):
"""Split input data csv file in to train, validation and test dataframes.
:param data_csv: Input data CSV file.
:return test_df, train_df, val_df: Train, validation and test dataframe
splits
"""
data_df = read_csv(data_csv)
# Obtain data split array mapping data rows to split type
# 0-train, 1-validation, 2-test
data_df[SPLIT] = get_split(data_df)
train_split, test_split, val_split = split_dataset_ttv(data_df, SPLIT)
# Splits are python dictionaries not dataframes- they need to be converted.
test_df = pd.DataFrame(test_split)
train_df = pd.DataFrame(train_split)
val_df = pd.DataFrame(val_split)
return test_df, train_df, val_df
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(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(),
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),
<<<<<<< HEAD
# TODO(shreya): Reintroduce sequence and text after sequence output feature.
=======
# TODO(#1333): Reintroduce sequence and text after sequence output feature.
>>>>>>> upstream/master
# 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.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)
# 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)