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Updating search space #156

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merged 4 commits into from
Apr 6, 2021
Merged

Updating search space #156

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3ff8b48
Updating search space
ArlindKadra Mar 27, 2021
5c3d6a4
fix typo
ArlindKadra Mar 27, 2021
d6a6289
Bug fix
ArlindKadra Mar 27, 2021
98c1654
Fixing buggy implementation of predict when using gpu
ArlindKadra Mar 31, 2021
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2 changes: 1 addition & 1 deletion autoPyTorch/datasets/resampling_strategy.py
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Expand Up @@ -97,7 +97,7 @@ def holdout_validation(val_share: float, indices: np.ndarray, **kwargs: Any) ->

def stratified_holdout_validation(val_share: float, indices: np.ndarray, **kwargs: Any) \
-> Tuple[np.ndarray, np.ndarray]:
train, val = train_test_split(indices, test_size=val_share, shuffle=False, stratify=kwargs["stratify"])
train, val = train_test_split(indices, test_size=val_share, shuffle=True, stratify=kwargs["stratify"])
return train, val


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5 changes: 3 additions & 2 deletions autoPyTorch/pipeline/components/setup/network/base_network.py
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Expand Up @@ -125,6 +125,7 @@ def predict(self, loader: torch.utils.data.DataLoader) -> torch.Tensor:
return Y_snapshot_preds_tensor.mean(dim=0).cpu().numpy()

def _predict(self, network: torch.nn.Module, loader: torch.utils.data.DataLoader) -> torch.Tensor:
network.to(self.device)
network.float()
network.eval()
# Batch prediction
Expand All @@ -133,10 +134,10 @@ def _predict(self, network: torch.nn.Module, loader: torch.utils.data.DataLoader
for i, (X_batch, Y_batch) in enumerate(loader):
# Predict on batch
X_batch = X_batch.float().to(self.device)
Y_batch_pred = network(X_batch).detach().cpu()
Y_batch_pred = network(X_batch)
if self.final_activation is not None:
Y_batch_pred = self.final_activation(Y_batch_pred)
Y_batch_preds.append(Y_batch_pred)
Y_batch_preds.append(Y_batch_pred.detach().cpu())

return torch.cat(Y_batch_preds, 0)

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5 changes: 3 additions & 2 deletions autoPyTorch/pipeline/components/setup/network_backbone/MLPBackbone.py
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Expand Up @@ -73,7 +73,7 @@ def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
activation: Tuple[Tuple, str] = (tuple(_activations.keys()),
list(_activations.keys())[0]),
use_dropout: Tuple[Tuple, bool] = ((True, False), False),
num_units: Tuple[Tuple, int] = ((10, 1024), 200),
num_units: Tuple[Tuple, int, bool] = ((10, 1024), 200, True),
dropout: Tuple[Tuple, float] = ((0, 0.8), 0.5)
) -> ConfigurationSpace:

Expand Down Expand Up @@ -102,7 +102,8 @@ def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
n_units_hp = UniformIntegerHyperparameter("num_units_%d" % i,
lower=num_units[0][0],
upper=num_units[0][1],
default_value=num_units[1])
default_value=num_units[1],
log=num_units[2],)
cs.add_hyperparameter(n_units_hp)

if i > min_mlp_layers:
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5 changes: 3 additions & 2 deletions autoPyTorch/pipeline/components/setup/network_backbone/ResNetBackbone.py
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Expand Up @@ -97,7 +97,7 @@ def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
use_batch_norm: Tuple[Tuple, bool] = ((True, False), True),
use_dropout: Tuple[Tuple, bool] = ((True, False), False),
use_skip_connection: Tuple[Tuple, bool] = ((True, False), True),
num_units: Tuple[Tuple, int] = ((10, 1024), 200),
num_units: Tuple[Tuple, int, bool] = ((10, 1024), 200, True),
activation: Tuple[Tuple, str] = (tuple(_activations.keys()),
list(_activations.keys())[0]),
blocks_per_group: Tuple[Tuple, int] = ((1, 4), 2),
Expand Down Expand Up @@ -162,7 +162,8 @@ def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
"num_units_%d" % i,
lower=num_units[0][0],
upper=num_units[0][1],
default_value=num_units[1]
default_value=num_units[1],
log=num_units[2],
)
blocks_per_group = UniformIntegerHyperparameter(
"blocks_per_group_%d" % i,
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9 changes: 6 additions & 3 deletions autoPyTorch/pipeline/components/setup/network_backbone/ShapedMLPBackbone.py
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Expand Up @@ -79,8 +79,8 @@ def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
num_groups: Tuple[Tuple, int] = ((1, 15), 5),
max_dropout: Tuple[Tuple, float] = ((0, 1), 0.5),
use_dropout: Tuple[Tuple, bool] = ((True, False), False),
max_units: Tuple[Tuple, int] = ((10, 1024), 200),
output_dim: Tuple[Tuple, int] = ((10, 1024), 200),
max_units: Tuple[Tuple, int, bool] = ((10, 1024), 200, True),
output_dim: Tuple[Tuple, int, bool] = ((10, 1024), 200, True),
mlp_shape: Tuple[Tuple, str] = (('funnel', 'long_funnel',
'diamond', 'hexagon',
'brick', 'triangle', 'stairs'), 'funnel'),
Expand All @@ -104,18 +104,21 @@ def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
default_value=activation[1]
)
(min_num_units, max_num_units), default_units = max_units[:2]
log_nr_max_units = max_units[2]
max_units = UniformIntegerHyperparameter(
"max_units",
lower=min_num_units,
upper=max_num_units,
default_value=default_units,
log=log_nr_max_units,
)

output_dim = UniformIntegerHyperparameter(
"output_dim",
lower=output_dim[0][0],
upper=output_dim[0][1],
default_value=output_dim[1]
default_value=output_dim[1],
log=output_dim[2],
)

cs.add_hyperparameters([num_groups, activation, mlp_shape, max_units, output_dim])
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11 changes: 7 additions & 4 deletions autoPyTorch/pipeline/components/setup/network_backbone/ShapedResNetBackbone.py
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Expand Up @@ -85,7 +85,7 @@ def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
use_batch_norm: Tuple[Tuple, bool] = ((True, False), True),
use_dropout: Tuple[Tuple, bool] = ((True, False), False),
use_skip_connection: Tuple[Tuple, bool] = ((True, False), True),
max_units: Tuple[Tuple, int] = ((10, 1024), 200),
max_units: Tuple[Tuple, int, bool] = ((10, 1024), 200, True),
blocks_per_group: Tuple[Tuple, int] = ((1, 4), 2),
max_dropout: Tuple[Tuple, float] = ((0, 0.8), 0.5),
multi_branch_choice: Tuple[Tuple, str] = (('shake-drop', 'shake-shake',
Expand All @@ -96,7 +96,7 @@ def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
'brick', 'triangle', 'stairs'), 'funnel'),
activation: Tuple[Tuple, str] = (
tuple(_activations.keys()), list(_activations.keys())[0]),
output_dim: Tuple[Tuple, int] = ((10, 1024), 200),
output_dim: Tuple[Tuple, int, bool] = ((10, 1024), 200, True),
) -> ConfigurationSpace:
cs = ConfigurationSpace()

Expand Down Expand Up @@ -124,11 +124,13 @@ def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
default_value=activation[1]
)
(min_num_units, max_num_units), default_units = max_units[:2]
log_num_units = max_units[2]
output_dim = UniformIntegerHyperparameter(
"output_dim",
lower=output_dim[0][0],
upper=output_dim[0][1],
default_value=output_dim[1]
default_value=output_dim[1],
log=output_dim[2],
)

cs.add_hyperparameters([num_groups, blocks_per_group, activation, output_dim])
Expand Down Expand Up @@ -161,7 +163,8 @@ def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
"max_units",
lower=min_num_units,
upper=max_num_units,
default_value=default_units
default_value=default_units,
log=log_num_units,
)
cs.add_hyperparameters([max_units])

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4 changes: 2 additions & 2 deletions autoPyTorch/pipeline/components/setup/optimizer/AdamOptimizer.py
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Expand Up @@ -83,7 +83,7 @@ def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
beta1: Tuple[Tuple, float] = ((0.85, 0.999), 0.9),
beta2: Tuple[Tuple, float] = ((0.9, 0.9999), 0.9),
use_weight_decay: Tuple[Tuple, bool] = ((True, False), True),
weight_decay: Tuple[Tuple, float] = ((0.0, 0.1), 0.0)
weight_decay: Tuple[Tuple, float, bool] = ((1E-7, 0.1), 1E-4, True)
) -> ConfigurationSpace:

cs = ConfigurationSpace()
Expand All @@ -105,7 +105,7 @@ def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
)

weight_decay = UniformFloatHyperparameter('weight_decay', lower=weight_decay[0][0], upper=weight_decay[0][1],
default_value=weight_decay[1])
default_value=weight_decay[1], log=weight_decay[2])

cs.add_hyperparameters([lr, beta1, beta2, use_wd, weight_decay])

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4 changes: 2 additions & 2 deletions autoPyTorch/pipeline/components/setup/optimizer/AdamWOptimizer.py
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Expand Up @@ -83,7 +83,7 @@ def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
beta1: Tuple[Tuple, float] = ((0.85, 0.999), 0.9),
beta2: Tuple[Tuple, float] = ((0.9, 0.9999), 0.9),
use_weight_decay: Tuple[Tuple, bool] = ((True, False), True),
weight_decay: Tuple[Tuple, float] = ((0.0, 0.1), 0.0)
weight_decay: Tuple[Tuple, float, bool] = ((1E-7, 0.1), 1E-4, True)
) -> ConfigurationSpace:

cs = ConfigurationSpace()
Expand All @@ -105,7 +105,7 @@ def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
)

weight_decay = UniformFloatHyperparameter('weight_decay', lower=weight_decay[0][0], upper=weight_decay[0][1],
default_value=weight_decay[1])
default_value=weight_decay[1], log=weight_decay[2])

cs.add_hyperparameters([lr, beta1, beta2, weight_decay, use_wd])

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4 changes: 2 additions & 2 deletions autoPyTorch/pipeline/components/setup/optimizer/RMSpropOptimizer.py
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Expand Up @@ -85,7 +85,7 @@ def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
lr: Tuple[Tuple, float, bool] = ((1e-5, 1e-1), 1e-2, True),
alpha: Tuple[Tuple, float] = ((0.1, 0.99), 0.99),
use_weight_decay: Tuple[Tuple, bool] = ((True, False), True),
weight_decay: Tuple[Tuple, float] = ((0.0, 0.1), 0.0),
weight_decay: Tuple[Tuple, float, bool] = ((1E-7, 0.1), 1E-4, True),
momentum: Tuple[Tuple, float] = ((0.0, 0.99), 0.0),
) -> ConfigurationSpace:

Expand All @@ -105,7 +105,7 @@ def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
)

weight_decay = UniformFloatHyperparameter('weight_decay', lower=weight_decay[0][0], upper=weight_decay[0][1],
default_value=weight_decay[1])
default_value=weight_decay[1], log=weight_decay[2])

momentum = UniformFloatHyperparameter('momentum', lower=momentum[0][0], upper=momentum[0][1],
default_value=momentum[1])
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4 changes: 2 additions & 2 deletions autoPyTorch/pipeline/components/setup/optimizer/SGDOptimizer.py
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Expand Up @@ -77,7 +77,7 @@ def get_properties(dataset_properties: Optional[Dict[str, Any]] = None) -> Dict[
def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
lr: Tuple[Tuple, float, bool] = ((1e-5, 1e-1), 1e-2, True),
use_weight_decay: Tuple[Tuple, bool] = ((True, False), True),
weight_decay: Tuple[Tuple, float] = ((0.0, 0.1), 0.0),
weight_decay: Tuple[Tuple, float, bool] = ((1E-7, 0.1), 1E-4, True),
momentum: Tuple[Tuple, float] = ((0.0, 0.99), 0.0),
) -> ConfigurationSpace:

Expand All @@ -94,7 +94,7 @@ def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
)

weight_decay = UniformFloatHyperparameter('weight_decay', lower=weight_decay[0][0], upper=weight_decay[0][1],
default_value=weight_decay[1])
default_value=weight_decay[1], log=weight_decay[2])

momentum = UniformFloatHyperparameter('momentum', lower=momentum[0][0], upper=momentum[0][1],
default_value=momentum[1])
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9 changes: 7 additions & 2 deletions autoPyTorch/pipeline/components/training/data_loader/base_data_loader.py
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Expand Up @@ -249,10 +249,15 @@ def get_torchvision_datasets(self) -> Dict[str, torchvision.datasets.VisionDatas

@staticmethod
def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
batch_size: Tuple[Tuple, int] = ((32, 320), 64)
batch_size: Tuple[Tuple, int, bool] = ((32, 320), 64, True)
) -> ConfigurationSpace:
batch_size = UniformIntegerHyperparameter(
"batch_size", batch_size[0][0], batch_size[0][1], default_value=batch_size[1])
"batch_size",
batch_size[0][0],
batch_size[0][1],
default_value=batch_size[1],
log=batch_size[2],
)
cs = ConfigurationSpace()
cs.add_hyperparameters([batch_size])
return cs
Expand Down