def test_pipeline_predict(self, fit_dictionary_tabular):
"""This test makes sure that the pipeline is able to predict
given a random configuration"""
X = fit_dictionary_tabular['X_train'].copy()
pipeline = TabularClassificationPipeline(
dataset_properties=fit_dictionary_tabular['dataset_properties'])
cs = pipeline.get_hyperparameter_search_space()
config = cs.sample_configuration()
pipeline.set_hyperparameters(config)
with unittest.mock.patch.object(pipeline.named_steps['trainer'].choice, 'train_epoch') \
as patch_train:
patch_train.return_value = 1, {}
pipeline.fit(fit_dictionary_tabular)
# we expect the output to have the same batch size as the test input,
# and number of outputs per batch sample equal to the number of outputs
expected_output_shape = (
X.shape[0],
fit_dictionary_tabular["dataset_properties"]["output_shape"])
prediction = pipeline.predict(X)
assert isinstance(prediction, np.ndarray)
assert prediction.shape == expected_output_shape
def test_pipeline_fit(self, fit_dictionary_tabular):
"""This test makes sure that the pipeline is able to fit
given random combinations of hyperparameters across the pipeline"""
pipeline = TabularClassificationPipeline(
dataset_properties=fit_dictionary_tabular['dataset_properties'])
cs = pipeline.get_hyperparameter_search_space()
config = cs.sample_configuration()
pipeline.set_hyperparameters(config)
try:
pipeline.fit(fit_dictionary_tabular)
except Exception as e:
pytest.fail(f"Failed due to {e} for config={config}")
# To make sure we fitted the model, there should be a
# run summary object with accuracy
run_summary = pipeline.named_steps['trainer'].run_summary
assert run_summary is not None
# Make sure that performance was properly captured
assert run_summary.performance_tracker['train_loss'][1] > 0
assert run_summary.total_parameter_count > 0
assert 'accuracy' in run_summary.performance_tracker['train_metrics'][
1]
# Make sure a network was fit
assert isinstance(pipeline.named_steps['network'].get_network(),
torch.nn.Module)
def test_pipeline_fit_include(self, fit_dictionary_tabular, preprocessor):
"""
This test ensures that a tabular classification
pipeline can be fit with all preprocessors
in the include
"""
fit_dictionary_tabular['epochs'] = 1
pipeline = TabularClassificationPipeline(
dataset_properties=fit_dictionary_tabular['dataset_properties'],
include={'feature_preprocessor': [preprocessor]})
cs = pipeline.get_hyperparameter_search_space()
config = cs.sample_configuration()
pipeline.set_hyperparameters(config)
try:
pipeline.fit(fit_dictionary_tabular)
except Exception as e:
pytest.fail(f"For config {config} failed with {e}")
# To make sure we fitted the model, there should be a
# run summary object with accuracy
run_summary = pipeline.named_steps['trainer'].run_summary
assert run_summary is not None
assert preprocessor == pipeline.named_steps[
'feature_preprocessor'].choice.__class__.__name__
def test_pipeline_predict_proba(self, fit_dictionary_tabular):
"""This test makes sure that the pipeline is able to fit
given random combinations of hyperparameters across the pipeline
And then predict using predict probability
"""
X = fit_dictionary_tabular['X_train'].copy()
pipeline = TabularClassificationPipeline(
dataset_properties=fit_dictionary_tabular['dataset_properties'])
cs = pipeline.get_hyperparameter_search_space()
config = cs.sample_configuration()
pipeline.set_hyperparameters(config)
try:
with unittest.mock.patch.object(pipeline.named_steps['trainer'].choice, 'train_epoch') \
as patch_train:
patch_train.return_value = 1, {}
pipeline.fit(fit_dictionary_tabular)
except Exception as e:
pytest.fail(f"Failed on config={config} with {e}")
# we expect the output to have the same batch size as the test input,
# and number of outputs per batch sample equal to the number of classes ("num_classes" in dataset_properties)
expected_output_shape = (
X.shape[0],
fit_dictionary_tabular["dataset_properties"]["output_shape"])
prediction = pipeline.predict_proba(X)
assert isinstance(prediction, np.ndarray)
assert prediction.shape == expected_output_shape
def test_pipeline_fit(self):
"""This test makes sure that the pipeline is able to fit
given random combinations of hyperparameters across the pipeline"""
pipeline = TabularClassificationPipeline(
dataset_properties=self.dataset_properties)
cs = pipeline.get_hyperparameter_search_space()
config = cs.sample_configuration()
pipeline.set_hyperparameters(config)
pipeline.fit({
'num_features':
self.num_features,
'num_classes':
self.num_classes,
'numerical_columns':
list(range(self.num_features)),
'categorical_columns': [],
'categories': [],
'X_train':
self.X,
'y_train':
self.y,
'train_indices':
list(range(self.X.shape[0] // 2)),
'val_indices':
list(range(self.X.shape[0] // 2, self.X.shape[0])),
'is_small_preprocess':
False,
# Training configuration
'dataset_properties':
self.dataset_properties,
'job_id':
'example_tabular_classification_1',
'device':
'cpu',
'budget_type':
'epochs',
'epochs':
5,
'torch_num_threads':
1,
'early_stopping':
20,
'working_dir':
'/tmp',
'use_tensorboard_logger':
True,
'use_pynisher':
False,
'metrics_during_training':
True,
'split_id':
0,
'backend':
self.backend,
})
# To make sure we fitted the model, there should be a
# run summary object with accuracy
self.assertIsNotNone(pipeline.named_steps['trainer'].run_summary)
def test_pipeline_score(fit_dictionary_tabular_dummy):
"""This test makes sure that the pipeline is able to achieve a decent score on dummy data
given the default configuration"""
X = fit_dictionary_tabular_dummy['X_train'].copy()
y = fit_dictionary_tabular_dummy['y_train'].copy()
# increase number of epochs to test for performance
fit_dictionary_tabular_dummy['epochs'] = 50
pipeline = TabularClassificationPipeline(
dataset_properties=fit_dictionary_tabular_dummy['dataset_properties'])
cs = pipeline.get_hyperparameter_search_space()
config = cs.get_default_configuration()
pipeline.set_hyperparameters(config)
pipeline.fit(fit_dictionary_tabular_dummy)
# Ensure that the network is an instance of torch Module
assert isinstance(pipeline.named_steps['network'].get_network(),
torch.nn.Module)
accuracy = pipeline.score(X, y)
# we should be able to get a decent score on this dummy data
assert accuracy >= 0.8, f"Pipeline:{pipeline} Config:{config} FitDict: {fit_dictionary_tabular_dummy}"
def test_train_pipeline_with_runtime(fit_dictionary_tabular_dummy):
"""This test makes sure that the pipeline is able to achieve a decent score on dummy data
given the default configuration"""
# Convert the training to runtime
fit_dictionary_tabular_dummy.pop('epochs', None)
fit_dictionary_tabular_dummy['budget_type'] = 'runtime'
fit_dictionary_tabular_dummy['runtime'] = 5
fit_dictionary_tabular_dummy['early_stopping'] = -1
pipeline = TabularClassificationPipeline(
dataset_properties=fit_dictionary_tabular_dummy['dataset_properties'])
cs = pipeline.get_hyperparameter_search_space()
config = cs.get_default_configuration()
pipeline.set_hyperparameters(config)
pipeline.fit(fit_dictionary_tabular_dummy)
run_summary = pipeline.named_steps['trainer'].run_summary
budget_tracker = pipeline.named_steps['trainer'].budget_tracker
assert budget_tracker.budget_type == 'runtime'
assert budget_tracker.max_runtime == 5
assert budget_tracker.is_max_time_reached()
# There is no epoch limitation
assert not budget_tracker.is_max_epoch_reached(epoch=np.inf)
# More than 200 epochs would have pass in 5 seconds for this dataset
assert len(run_summary.performance_tracker['start_time']) > 100
def test_default_configuration(self):
"""Makes sure that when no config is set, we can trust the
default configuration from the space"""
pipeline = TabularClassificationPipeline(
dataset_properties=self.dataset_properties)
pipeline.fit({
'num_features':
self.num_features,
'num_classes':
self.num_classes,
'numerical_columns':
list(range(self.num_features)),
'categorical_columns': [],
'categories': [],
'X_train':
self.X,
'y_train':
self.y,
'train_indices':
list(range(self.X.shape[0] // 2)),
'val_indices':
list(range(self.X.shape[0] // 2, self.X.shape[0])),
'is_small_preprocess':
False,
# Training configuration
'dataset_properties':
self.dataset_properties,
'job_id':
'example_tabular_classification_1',
'device':
'cpu',
'budget_type':
'epochs',
'epochs':
5,
'torch_num_threads':
1,
'early_stopping':
20,
'working_dir':
'/tmp',
'use_tensorboard_logger':
True,
'use_pynisher':
False,
'metrics_during_training':
True,
'split_id':
0,
'backend':
self.backend,
})
def test_default_configuration(self, fit_dictionary_tabular,
is_small_preprocess):
"""Makes sure that when no config is set, we can trust the
default configuration from the space"""
fit_dictionary_tabular['is_small_preprocess'] = is_small_preprocess
pipeline = TabularClassificationPipeline(
dataset_properties=fit_dictionary_tabular['dataset_properties'])
with unittest.mock.patch.object(pipeline.named_steps['trainer'].choice, 'train_epoch') \
as patch_train:
patch_train.return_value = 1, {}
pipeline.fit(fit_dictionary_tabular)
def random_search_and_save(fit_dictionary: typing.Dict[str, typing.Any],
backend: Backend, num_models: int) -> None:
"""
A function to generate randomly fitted pipelines.
It inefficiently pass the data in the fit dictionary, as there is no datamanager yet.
It uses the backend to save the models and predictions for the ensemble selection
"""
# Ensemble selection will evaluate performance on the OOF predictions. Store the OOF
# Ground truth
datamanager = backend.load_datamanager()
X_train, y_train = datamanager.train_tensors
X_test, y_test = (None, None)
if datamanager.test_tensors is not None:
X_test, y_test = datamanager.test_tensors
targets = np.take(y_train, fit_dictionary['val_indices'], axis=0)
backend.save_targets_ensemble(targets)
for idx in range(num_models):
pipeline = TabularClassificationPipeline(
dataset_properties=fit_dictionary['dataset_properties'])
# Sample a random configuration
pipeline_cs = pipeline.get_hyperparameter_search_space()
config = pipeline_cs.sample_configuration()
pipeline.set_hyperparameters(config)
# Fit the sample configuration
pipeline.fit(fit_dictionary)
# Predict using the fit model
ensemble_predictions = pipeline.predict(
np.take(X_train, fit_dictionary['val_indices'], axis=0))
test_predictions = pipeline.predict(X_test)
backend.save_numrun_to_dir(
seed=fit_dictionary['seed'],
idx=idx,
budget=fit_dictionary['epochs'],
model=pipeline,
cv_model=None,
ensemble_predictions=ensemble_predictions,
valid_predictions=None,
test_predictions=test_predictions,
)
score = accuracy_score(y_test, np.argmax(test_predictions, axis=1))
print(f"Fitted a pipeline {idx} with score = {score}")
return
def test_remove_key_check_requirements(self, fit_dictionary_tabular):
"""Makes sure that when a key is removed from X, correct error is outputted"""
pipeline = TabularClassificationPipeline(
dataset_properties=fit_dictionary_tabular['dataset_properties'])
for key in [
'num_run', 'device', 'split_id', 'torch_num_threads',
'dataset_properties'
]:
fit_dictionary_tabular_copy = fit_dictionary_tabular.copy()
fit_dictionary_tabular_copy.pop(key)
with pytest.raises(
ValueError,
match=r"To fit .+?, expected fit dictionary to have"):
pipeline.fit(fit_dictionary_tabular_copy)
def test_tabular_preprocess(self):
dataset_properties = {
'numerical_columns': list(range(15)),
'categorical_columns': [],
'task_type': 'tabular_classification'
}
X = dict(
X_train=np.random.random((10, 15)),
y_train=np.random.random(10),
train_indices=[0, 1, 2, 3, 4, 5],
val_indices=[6, 7, 8, 9],
is_small_preprocess=True,
numerical_columns=list(range(15)),
categorical_columns=[],
num_features=15,
num_classes=2,
categories=[],
# Training configuration
job_id='test',
device='cpu',
budget_type='epochs',
epochs=10,
torch_num_threads=1,
early_stopping=20,
dataset_properties=dataset_properties,
split_id=0,
backend=self.backend,
)
pipeline = TabularClassificationPipeline(
dataset_properties=dataset_properties)
# Remove the trainer
pipeline.steps.pop()
pipeline = pipeline.fit(X)
X = pipeline.transform(X)
self.assertNotIn('preprocess_transforms', X.keys())
def test_pipeline_transform(self, fit_dictionary_tabular):
"""
In the context of autopytorch, transform expands a fit dictionary with
components that where previously fit. We can use this as a nice way to make sure
that fit properly work.
This code is added in light of components not properly added to the fit dicitonary
"""
pipeline = TabularClassificationPipeline(
dataset_properties=fit_dictionary_tabular['dataset_properties'])
cs = pipeline.get_hyperparameter_search_space()
config = cs.sample_configuration()
pipeline.set_hyperparameters(config)
with unittest.mock.patch.object(pipeline.named_steps['trainer'].choice, 'train_epoch') \
as patch_train:
patch_train.return_value = 1, {}
# We do not want to make the same early preprocessing operation to the fit dictionary
pipeline.fit(fit_dictionary_tabular.copy())
transformed_fit_dictionary_tabular = pipeline.transform(
fit_dictionary_tabular)
# First, we do not lose anyone! (We use a fancy subset containment check)
assert fit_dictionary_tabular.items(
) <= transformed_fit_dictionary_tabular.items()
# Then the pipeline should have added the following keys
expected_keys = {
'imputer', 'encoder', 'scaler', 'tabular_transformer',
'preprocess_transforms', 'network', 'optimizer', 'lr_scheduler',
'train_data_loader', 'val_data_loader', 'run_summary'
}
assert expected_keys.issubset(
set(transformed_fit_dictionary_tabular.keys()))
# Then we need to have transformations being created.
assert len(
get_preprocess_transforms(transformed_fit_dictionary_tabular)) > 0
# We expect the transformations to be in the pipeline at anytime for inference
assert 'preprocess_transforms' in transformed_fit_dictionary_tabular.keys(
)
def test_remove_key_check_requirements(self):
"""Makes sure that when a key is removed from X, correct error is outputted"""
pipeline = TabularClassificationPipeline(
dataset_properties=self.dataset_properties)
X = {
'num_features': self.num_features,
'num_classes': self.num_classes,
'numerical_columns': list(range(self.num_features)),
'categorical_columns': [],
'categories': [],
'X_train': self.X,
'y_train': self.y,
'train_indices': list(range(self.X.shape[0] // 2)),
'val_indices': list(range(self.X.shape[0] // 2, self.X.shape[0])),
'is_small_preprocess': False,
# Training configuration
'dataset_properties': self.dataset_properties,
'job_id': 'example_tabular_classification_1',
'device': 'cpu',
'budget_type': 'epochs',
'epochs': 5,
'torch_num_threads': 1,
'early_stopping': 20,
'working_dir': '/tmp',
'use_tensorboard_logger': True,
'use_pynisher': False,
'metrics_during_training': True,
'split_id': 0,
'backend': self.backend,
}
for key in X.keys():
# skip tests for data loader requirements as data loader has different check_requirements
if key == 'y_train' or 'val_indices':
continue
X_copy = X.copy()
X_copy.pop(key)
try:
pipeline.fit(X_copy)
except ValueError as msg:
self.assertRegex(
str(msg),
r"To fit .+?, expected fit dictionary to have .+? but got .*"
)
def test_tabular_no_preprocess(self):
dataset_properties = {
'numerical_columns': list(range(15)),
'categorical_columns': [],
'task_type': TASK_TYPES_TO_STRING[TABULAR_CLASSIFICATION],
'output_type': OUTPUT_TYPES_TO_STRING[MULTICLASS],
'is_small_preprocess': False,
'input_shape': (15, ),
'output_shape': 2,
'categories': [],
'issparse': False
}
X = dict(
X_train=np.random.random((10, 15)),
y_train=np.random.random(10),
train_indices=[0, 1, 2, 3, 4, 5],
val_indices=[6, 7, 8, 9],
dataset_properties=dataset_properties,
# Training configuration
num_run=16,
device='cpu',
budget_type='epochs',
epochs=10,
torch_num_threads=1,
early_stopping=20,
split_id=0,
backend=self.backend,
)
pipeline = TabularClassificationPipeline(
dataset_properties=dataset_properties)
# Remove the trainer
pipeline.steps.pop()
pipeline = pipeline.fit(X)
X = pipeline.transform(X)
self.assertIn('preprocess_transforms', X.keys())
self.assertIsInstance(X['preprocess_transforms'], list)
self.assertIsInstance(X['preprocess_transforms'][-1].preprocessor,
BaseEstimator)
'epochs': 100,
'runtime': 3600,
'torch_num_threads': 1,
'early_stopping': 20,
'use_tensorboard_logger': True,
'use_pynisher': False,
'metrics_during_training': True,
'backend': backend,
'split_id': 0,
}
# Configuration space
pipeline_cs = pipeline.get_hyperparameter_search_space()
print("Pipeline CS:\n", '_' * 40, f"\n{pipeline_cs}")
config = pipeline_cs.sample_configuration()
print("Pipeline Random Config:\n", '_' * 40, f"\n{config}")
pipeline.set_hyperparameters(config)
# Make sure the working directory exists. Something that backend will handle
os.makedirs('./tmp/example_tabular_classification_1', exist_ok=True)
# Fit the pipeline
print("Fitting the pipeline...")
pipeline.fit(fit_dictionary)
# Showcase some components of the pipeline
print(pipeline)
# Showcase performance of pipeline
print(pipeline.named_steps['trainer'].run_summary.repr_last_epoch())
def test_pipeline_fit(self, fit_dictionary_tabular, embedding, backbone,
head):
"""This test makes sure that the pipeline is able to fit
every combination of network embedding, backbone, head"""
# increase number of epochs to test for performance
fit_dictionary_tabular['epochs'] = 50
include = {
'network_backbone': [backbone],
'network_head': [head],
'network_embedding': [embedding]
}
if len(fit_dictionary_tabular['dataset_properties']
['categorical_columns']
) == 0 and embedding == 'LearnedEntityEmbedding':
pytest.skip(
"Learned Entity Embedding is not used with numerical only data"
)
pipeline = TabularClassificationPipeline(
dataset_properties=fit_dictionary_tabular['dataset_properties'],
include=include)
cs = pipeline.get_hyperparameter_search_space()
config = cs.get_default_configuration()
assert embedding == config.get('network_embedding:__choice__', None)
assert backbone == config.get('network_backbone:__choice__', None)
assert head == config.get('network_head:__choice__', None)
pipeline.set_hyperparameters(config)
# Early stop to the best configuration seen
fit_dictionary_tabular['early_stopping'] = 50
pipeline.fit(fit_dictionary_tabular)
# To make sure we fitted the model, there should be a
# run summary object with accuracy
run_summary = pipeline.named_steps['trainer'].run_summary
assert run_summary is not None
# Make sure that performance was properly captured
assert run_summary.performance_tracker['train_loss'][1] > 0
assert run_summary.total_parameter_count > 0
assert 'accuracy' in run_summary.performance_tracker['train_metrics'][
1]
# Make sure default pipeline achieves a good score for dummy datasets
epoch_where_best = int(
np.argmax([
run_summary.performance_tracker['val_metrics'][e]['accuracy']
for e in range(
1,
len(run_summary.performance_tracker['val_metrics']) + 1)
])) + 1 # Epochs start at 1
score = run_summary.performance_tracker['val_metrics'][
epoch_where_best]['accuracy']
assert score >= 0.8, run_summary.performance_tracker['val_metrics']
# Check that early stopping happened, if it did
# We should not stop before patience
assert run_summary.get_last_epoch(
) >= fit_dictionary_tabular['early_stopping']
# we should not be greater than max allowed epoch
assert run_summary.get_last_epoch() <= fit_dictionary_tabular['epochs']
# every trained epoch has a val metric
assert run_summary.get_last_epoch() == max(
list(run_summary.performance_tracker['train_metrics'].keys()))
epochs_since_best = run_summary.get_last_epoch(
) - run_summary.get_best_epoch()
if epochs_since_best >= fit_dictionary_tabular['early_stopping']:
assert run_summary.get_best_epoch() == epoch_where_best
# Make sure a network was fit
assert isinstance(pipeline.named_steps['network'].get_network(),
torch.nn.Module)
def test_constant_pipeline_iris(fit_dictionary_tabular):
search_space_updates = HyperparameterSearchSpaceUpdates()
search_space_updates.append(node_name='feature_preprocessor',
hyperparameter='__choice__',
value_range=['PolynomialFeatures'],
default_value='PolynomialFeatures')
search_space_updates.append(node_name='scaler',
hyperparameter='__choice__',
value_range=['StandardScaler'],
default_value='StandardScaler')
search_space_updates.append(
node_name='network_backbone',
hyperparameter='__choice__',
value_range=['MLPBackbone', 'ShapedMLPBackbone'],
default_value='MLPBackbone')
search_space_updates.append(node_name='network_backbone',
hyperparameter='MLPBackbone:num_groups',
value_range=[1, 1],
default_value=1)
search_space_updates.append(node_name='network_backbone',
hyperparameter='MLPBackbone:num_units',
value_range=[100],
default_value=100)
search_space_updates.append(node_name='trainer',
hyperparameter='__choice__',
value_range=['StandardTrainer'],
default_value='StandardTrainer')
search_space_updates.append(node_name='lr_scheduler',
hyperparameter='__choice__',
value_range=['NoScheduler'],
default_value='NoScheduler')
search_space_updates.append(node_name='optimizer',
hyperparameter='__choice__',
value_range=['AdamOptimizer'],
default_value='AdamOptimizer')
search_space_updates.append(node_name='optimizer',
hyperparameter='AdamOptimizer:lr',
value_range=[1e-2],
default_value=1e-2)
pipeline = TabularClassificationPipeline(
dataset_properties=fit_dictionary_tabular['dataset_properties'],
search_space_updates=search_space_updates)
fit_dictionary_tabular['additional_metrics'] = ['balanced_accuracy']
# increase number of epochs to test for performance
fit_dictionary_tabular['epochs'] = 50
try:
pipeline.fit(fit_dictionary_tabular)
except Exception as e:
pytest.fail(f"Failed due to {e}")
configuration = pipeline.configuration
assert 'PolynomialFeatures' == configuration.get(
'feature_preprocessor:__choice__')
assert 'StandardScaler' == configuration.get('scaler:__choice__')
assert 'MLPBackbone' == configuration.get('network_backbone:__choice__')
assert 'StandardTrainer' == configuration.get('trainer:__choice__')
assert 'NoScheduler' == configuration.get('lr_scheduler:__choice__')
assert 'AdamOptimizer' == configuration.get('optimizer:__choice__')
assert 1 == configuration.get('network_backbone:MLPBackbone:num_groups')
assert 100 == configuration.get('network_backbone:MLPBackbone:num_units_1')
assert 1e-2 == configuration.get('optimizer:AdamOptimizer:lr')
# To make sure we fitted the model, there should be a
# run summary object with accuracy
run_summary = pipeline.named_steps['trainer'].run_summary
assert run_summary is not None
# Make sure that performance was properly captured
assert run_summary.performance_tracker['train_loss'][1] > 0
assert run_summary.total_parameter_count > 0
assert 'balanced_accuracy' in run_summary.performance_tracker[
'train_metrics'][1]
# Make sure default pipeline achieves a good score for dummy datasets
epoch2loss = run_summary.performance_tracker['val_loss']
best_loss = min(list(epoch2loss.values()))
epoch_where_best = list(epoch2loss.keys())[list(
epoch2loss.values()).index(best_loss)]
val_score = run_summary.performance_tracker['val_metrics'][
epoch_where_best]['balanced_accuracy']
train_score = run_summary.performance_tracker['train_metrics'][
epoch_where_best]['balanced_accuracy']
assert val_score >= 0.8, run_summary.performance_tracker['val_metrics']
assert train_score >= 0.8, run_summary.performance_tracker['train_metrics']