Configurations

class deeptab.configs.DefaultMambularConfig(lr=0.0001, lr_patience=10, weight_decay=1e-06, lr_factor=0.1, use_embeddings=False, embedding_activation=torch.nn.Identity, embedding_type='linear', embedding_bias=False, layer_norm_after_embedding=False, d_model=64, plr_lite=False, n_frequencies=48, frequencies_init_scale=0.01, embedding_projection=True, batch_norm=False, layer_norm=False, layer_norm_eps=1e-05, activation=torch.nn.SiLU, cat_encoding='int', n_layers=4, d_conv=4, dilation=1, expand_factor=2, bias=False, dropout=0.0, dt_rank='auto', d_state=128, dt_scale=1.0, dt_init='random', dt_max=0.1, dt_min=0.0001, dt_init_floor=0.0001, norm='RMSNorm', conv_bias=False, AD_weight_decay=True, BC_layer_norm=False, shuffle_embeddings=False, head_layer_sizes=<factory>, head_dropout=0.5, head_skip_layers=False, head_activation=torch.nn.SELU, head_use_batch_norm=False, pooling_method='avg', bidirectional=False, use_learnable_interaction=False, use_cls=False, use_pscan=False, mamba_version='mamba-torch')

Configuration class for the Default Mambular model with predefined hyperparameters.

Parameters:

• d_model (int) – Dimensionality of the model.

• n_layers (int) – Number of layers in the model.

• expand_factor (int) – Expansion factor for the feed-forward layers.

• bias (bool) – Whether to use bias in the linear layers.

• dropout (float) – Dropout rate for regularization.

• d_conv (int) – Size of convolution over columns.

• dilation (int) – Dilation factor for the convolution.

• dt_rank (str) – Rank of the decision tree used in the model.

• d_state (int) – Dimensionality of the state in recurrent layers.

• dt_scale (float) – Scaling factor for decision tree parameters.

• dt_init (str) – Initialization method for decision tree parameters.

• dt_max (float) – Maximum value for decision tree initialization.

• dt_min (float) – Minimum value for decision tree initialization.

• dt_init_floor (float) – Floor value for decision tree initialization.

• norm (str) – Type of normalization used (‘RMSNorm’, etc.).

• activation (Callable) – Activation function for the model.

• shuffle_embeddings (bool) – Whether to shuffle embeddings before being passed to Mamba layers.

• head_layer_sizes (list) – Sizes of the layers in the model’s head.

• head_dropout (float) – Dropout rate for the head layers.

• head_skip_layers (bool) – Whether to skip layers in the head.

• head_activation (Callable) – Activation function for the head layers.

• head_use_batch_norm (bool) – Whether to use batch normalization in the head layers.

• pooling_method (str) – Pooling method to use (‘avg’, ‘max’, etc.).

• bidirectional (bool) – Whether to process data bidirectionally.

• use_learnable_interaction (bool) – Whether to use learnable feature interactions before passing through Mamba blocks.

• use_cls (bool) – Whether to append a CLS token to the input sequences.

• use_pscan (bool) – Whether to use PSCAN for the state-space model.

• mamba_version (str) – Version of the Mamba model to use (‘mamba-torch’, ‘mamba1’, ‘mamba2’).

• conv_bias (bool) – Whether to use a bias in the 1D convolution before each mamba block

• AD_weight_decay (bool) – Whether to use weight decay als for the A and D matrices in Mamba

• BC_layer_norm (bool) – Whether to use layer norm on the B and C matrices

AD_weight_decay: bool = True

BC_layer_norm: bool = False

batch_norm: bool = False

bias: bool = False

bidirectional: bool = False

cat_encoding: str = 'int'

conv_bias: bool = False

d_conv: int = 4

d_model: int = 64

d_state: int = 128

dilation: int = 1

dropout: float = 0.0

dt_init: str = 'random'

dt_init_floor: float = 0.0001

dt_max: float = 0.1

dt_min: float = 0.0001

dt_rank: str = 'auto'

dt_scale: float = 1.0

embedding_bias: bool = False

embedding_projection: bool = True

embedding_type: str = 'linear'

expand_factor: int = 2

frequencies_init_scale: float = 0.01

head_dropout: float = 0.5

head_layer_sizes: list

head_skip_layers: bool = False

head_use_batch_norm: bool = False

layer_norm: bool = False

layer_norm_after_embedding: bool = False

layer_norm_eps: float = 1e-05

lr: float = 0.0001

lr_factor: float = 0.1

lr_patience: int = 10

mamba_version: str = 'mamba-torch'

n_frequencies: int = 48

n_layers: int = 4

norm: str = 'RMSNorm'

plr_lite: bool = False

pooling_method: str = 'avg'

shuffle_embeddings: bool = False

use_cls: bool = False

use_embeddings: bool = False

use_learnable_interaction: bool = False

use_pscan: bool = False

weight_decay: float = 1e-06

class deeptab.configs.DefaultFTTransformerConfig(lr=0.0001, lr_patience=10, weight_decay=1e-06, lr_factor=0.1, use_embeddings=False, embedding_activation=torch.nn.Identity, embedding_type='linear', embedding_bias=False, layer_norm_after_embedding=False, d_model=128, plr_lite=False, n_frequencies=48, frequencies_init_scale=0.01, embedding_projection=True, batch_norm=False, layer_norm=False, layer_norm_eps=1e-05, activation=torch.nn.SELU, cat_encoding='int', n_layers=4, n_heads=8, attn_dropout=0.2, ff_dropout=0.1, norm='LayerNorm', transformer_activation=torch.nn.Module, transformer_dim_feedforward=256, norm_first=False, bias=True, head_layer_sizes=<factory>, head_dropout=0.5, head_skip_layers=False, head_activation=torch.nn.SELU, head_use_batch_norm=False, pooling_method='avg', use_cls=False)

Configuration class for the FT Transformer model with predefined hyperparameters.

Parameters:

• d_model (int) – Dimensionality of the transformer model.

• n_layers (int) – Number of transformer layers.

• n_heads (int) – Number of attention heads in the transformer.

• attn_dropout (float) – Dropout rate for the attention mechanism.

• ff_dropout (float) – Dropout rate for the feed-forward layers.

• norm (str) – Type of normalization to be used (‘LayerNorm’, ‘RMSNorm’, etc.).

• activation (Callable) – Activation function for the transformer layers.

• transformer_activation (Callable) – Activation function for the transformer feed-forward layers.

• transformer_dim_feedforward (int) – Dimensionality of the feed-forward layers in the transformer.

• layer_norm_eps (float) – Epsilon value for layer normalization to improve numerical stability.

• norm_first (bool) – Whether to apply normalization before other operations in each transformer block.

• bias (bool) – Whether to use bias in linear layers.

• head_layer_sizes (list) – Sizes of the fully connected layers in the model’s head.

• head_dropout (float) – Dropout rate for the head layers.

• head_skip_layers (bool) – Whether to use skip connections in the head layers.

• head_activation (Callable) – Activation function for the head layers.

• head_use_batch_norm (bool) – Whether to use batch normalization in the head layers.

• pooling_method (str) – Pooling method to be used (‘cls’, ‘avg’, etc.).

• use_cls (bool) – Whether to use a CLS token for pooling.

• cat_encoding (str) – Method for encoding categorical features (‘int’, ‘one-hot’, or ‘linear’).

attn_dropout: float = 0.2

batch_norm: bool = False

bias: bool = True

cat_encoding: str = 'int'

d_model: int = 128

embedding_bias: bool = False

embedding_projection: bool = True

embedding_type: str = 'linear'

ff_dropout: float = 0.1

frequencies_init_scale: float = 0.01

head_dropout: float = 0.5

head_layer_sizes: list

head_skip_layers: bool = False

head_use_batch_norm: bool = False

layer_norm: bool = False

layer_norm_after_embedding: bool = False

layer_norm_eps: float = 1e-05

lr: float = 0.0001

lr_factor: float = 0.1

lr_patience: int = 10

n_frequencies: int = 48

n_heads: int = 8

n_layers: int = 4

norm: str = 'LayerNorm'

norm_first: bool = False

plr_lite: bool = False

pooling_method: str = 'avg'

transformer_dim_feedforward: int = 256

use_cls: bool = False

use_embeddings: bool = False

weight_decay: float = 1e-06

class deeptab.configs.DefaultResNetConfig(lr=0.0001, lr_patience=10, weight_decay=1e-06, lr_factor=0.1, use_embeddings=False, embedding_activation=torch.nn.Identity, embedding_type='linear', embedding_bias=False, layer_norm_after_embedding=False, d_model=32, plr_lite=False, n_frequencies=48, frequencies_init_scale=0.01, embedding_projection=True, batch_norm=False, layer_norm=False, layer_norm_eps=1e-05, activation=torch.nn.SELU, cat_encoding='int', layer_sizes=<factory>, skip_layers=False, dropout=0.5, norm=False, use_glu=False, skip_connections=True, num_blocks=3, average_embeddings=True)

Configuration class for the default ResNet model with predefined hyperparameters.

Parameters:

• layer_sizes (list) – Sizes of the layers in the ResNet.

• activation (Callable) – Activation function for the ResNet layers.

• skip_layers (bool) – Whether to skip layers in the ResNet.

• dropout (float) – Dropout rate for regularization.

• norm (bool) – Whether to use normalization in the ResNet.

• use_glu (bool) – Whether to use Gated Linear Units (GLU) in the ResNet.

• skip_connections (bool) – Whether to use skip connections in the ResNet.

• num_blocks (int) – Number of residual blocks in the ResNet.

• average_embeddings (bool) – Whether to average embeddings during the forward pass.

average_embeddings: bool = True

batch_norm: bool = False

cat_encoding: str = 'int'

d_model: int = 32

dropout: float = 0.5

embedding_bias: bool = False

embedding_projection: bool = True

embedding_type: str = 'linear'

frequencies_init_scale: float = 0.01

layer_norm: bool = False

layer_norm_after_embedding: bool = False

layer_norm_eps: float = 1e-05

layer_sizes: list

lr: float = 0.0001

lr_factor: float = 0.1

lr_patience: int = 10

n_frequencies: int = 48

norm: bool = False

num_blocks: int = 3

plr_lite: bool = False

skip_connections: bool = True

skip_layers: bool = False

use_embeddings: bool = False

use_glu: bool = False

weight_decay: float = 1e-06

class deeptab.configs.DefaultMLPConfig(lr=0.0001, lr_patience=10, weight_decay=1e-06, lr_factor=0.1, use_embeddings=False, embedding_activation=torch.nn.Identity, embedding_type='linear', embedding_bias=False, layer_norm_after_embedding=False, d_model=32, plr_lite=False, n_frequencies=48, frequencies_init_scale=0.01, embedding_projection=True, batch_norm=False, layer_norm=False, layer_norm_eps=1e-05, activation=torch.nn.ReLU, cat_encoding='int', layer_sizes=<factory>, skip_layers=False, dropout=0.2, use_glu=False, skip_connections=False)

Configuration class for the default Multi-Layer Perceptron (MLP) model with predefined hyperparameters.

Parameters:

• layer_sizes (list) – Sizes of the layers in the MLP.

• activation (Callable) – Activation function for the MLP layers.

• skip_layers (bool) – Whether to skip layers in the MLP.

• dropout (float) – Dropout rate for regularization.

• use_glu (bool) – Whether to use Gated Linear Units (GLU) in the MLP.

• skip_connections (bool) – Whether to use skip connections in the MLP.

batch_norm: bool = False

cat_encoding: str = 'int'

d_model: int = 32

dropout: float = 0.2

embedding_bias: bool = False

embedding_projection: bool = True

embedding_type: str = 'linear'

frequencies_init_scale: float = 0.01

layer_norm: bool = False

layer_norm_after_embedding: bool = False

layer_norm_eps: float = 1e-05

layer_sizes: list

lr: float = 0.0001

lr_factor: float = 0.1

lr_patience: int = 10

n_frequencies: int = 48

plr_lite: bool = False

skip_connections: bool = False

skip_layers: bool = False

use_embeddings: bool = False

use_glu: bool = False

weight_decay: float = 1e-06

class deeptab.configs.DefaultTabTransformerConfig(lr=0.0001, lr_patience=10, weight_decay=1e-06, lr_factor=0.1, use_embeddings=False, embedding_activation=torch.nn.Identity, embedding_type='linear', embedding_bias=False, layer_norm_after_embedding=False, d_model=128, plr_lite=False, n_frequencies=48, frequencies_init_scale=0.01, embedding_projection=True, batch_norm=False, layer_norm=False, layer_norm_eps=1e-05, activation=torch.nn.SELU, cat_encoding='int', n_layers=4, n_heads=8, attn_dropout=0.2, ff_dropout=0.1, norm='LayerNorm', transformer_activation=torch.nn.Module, transformer_dim_feedforward=512, norm_first=True, bias=True, head_layer_sizes=<factory>, head_dropout=0.5, head_skip_layers=False, head_activation=torch.nn.SELU, head_use_batch_norm=False, pooling_method='avg')

Configuration class for the default Tab Transformer model with predefined hyperparameters.

Parameters:

• n_layers (int) – Number of layers in the transformer.

• n_heads (int) – Number of attention heads in the transformer.

• d_model (int) – Dimensionality of embeddings or model representations.

• attn_dropout (float) – Dropout rate for the attention mechanism.

• ff_dropout (float) – Dropout rate for the feed-forward layers.

• norm (str) – Normalization method to be used.

• activation (Callable) – Activation function for the transformer layers.

• transformer_activation (Callable) – Activation function for the transformer layers.

• transformer_dim_feedforward (int) – Dimensionality of the feed-forward layers in the transformer.

• norm_first (bool) – Whether to apply normalization before other operations in each transformer block.

• bias (bool) – Whether to use bias in the linear layers.

• head_layer_sizes (list) – Sizes of the layers in the model’s head.

• head_dropout (float) – Dropout rate for the head layers.

• head_skip_layers (bool) – Whether to skip layers in the head.

• head_activation (Callable) – Activation function for the head layers.

• head_use_batch_norm (bool) – Whether to use batch normalization in the head layers.

• pooling_method (str) – Pooling method to be used (‘cls’, ‘avg’, etc.).

• cat_encoding (str) – Encoding method for categorical features (‘int’, ‘one-hot’, etc.).

attn_dropout: float = 0.2

batch_norm: bool = False

bias: bool = True

cat_encoding: str = 'int'

d_model: int = 128

embedding_bias: bool = False

embedding_projection: bool = True

embedding_type: str = 'linear'

ff_dropout: float = 0.1

frequencies_init_scale: float = 0.01

head_dropout: float = 0.5

head_layer_sizes: list

head_skip_layers: bool = False

head_use_batch_norm: bool = False

layer_norm: bool = False

layer_norm_after_embedding: bool = False

layer_norm_eps: float = 1e-05

lr: float = 0.0001

lr_factor: float = 0.1

lr_patience: int = 10

n_frequencies: int = 48

n_heads: int = 8

n_layers: int = 4

norm: str = 'LayerNorm'

norm_first: bool = True

plr_lite: bool = False

pooling_method: str = 'avg'

transformer_dim_feedforward: int = 512

use_embeddings: bool = False

weight_decay: float = 1e-06

class deeptab.configs.DefaultMambaTabConfig(lr=0.0001, lr_patience=10, weight_decay=1e-06, lr_factor=0.1, use_embeddings=False, embedding_activation=torch.nn.Identity, embedding_type='linear', embedding_bias=False, layer_norm_after_embedding=False, d_model=64, plr_lite=False, n_frequencies=48, frequencies_init_scale=0.01, embedding_projection=True, batch_norm=False, layer_norm=False, layer_norm_eps=1e-05, activation=torch.nn.ReLU, cat_encoding='int', n_layers=1, expand_factor=2, bias=False, d_conv=16, conv_bias=True, dropout=0.05, dt_rank='auto', d_state=128, dt_scale=1.0, dt_init='random', dt_max=0.1, dt_min=0.0001, dt_init_floor=0.0001, axis=1, head_layer_sizes=<factory>, head_dropout=0.0, head_skip_layers=False, head_activation=torch.nn.ReLU, head_use_batch_norm=False, norm='LayerNorm', use_pscan=False, mamba_version='mamba-torch', bidirectional=False)

Configuration class for the Default MambaTab model with predefined hyperparameters.

Parameters:

• d_model (int) – Dimensionality of the model.

• n_layers (int) – Number of layers in the model.

• expand_factor (int) – Expansion factor for the feed-forward layers.

• bias (bool) – Whether to use bias in the linear layers.

• d_conv (int) – Dimensionality of the convolutional layers.

• conv_bias (bool) – Whether to use bias in the convolutional layers.

• dropout (float) – Dropout rate for regularization.

• dt_rank (str) – Rank of the decision tree used in the model.

• d_state (int) – Dimensionality of the state in recurrent layers.

• dt_scale (float) – Scaling factor for the decision tree.

• dt_init (str) – Initialization method for the decision tree.

• dt_max (float) – Maximum value for decision tree initialization.

• dt_min (float) – Minimum value for decision tree initialization.

• dt_init_floor (float) – Floor value for decision tree initialization.

• activation (Callable) – Activation function for the model.

• axis (int) – Axis along which operations are applied, if applicable.

• head_layer_sizes (list) – Sizes of the fully connected layers in the model’s head.

• head_dropout (float) – Dropout rate for the head layers.

• head_skip_layers (bool) – Whether to skip layers in the head.

• head_activation (Callable) – Activation function for the head layers.

• head_use_batch_norm (bool) – Whether to use batch normalization in the head layers.

• norm (str) – Type of normalization to be used (‘LayerNorm’, ‘RMSNorm’, etc.).

• use_pscan (bool) – Whether to use PSCAN for the state-space model.

• mamba_version (str) – Version of the Mamba model to use (‘mamba-torch’, ‘mamba1’, ‘mamba2’).

• bidirectional (bool) – Whether to process data bidirectionally.

axis: int = 1

batch_norm: bool = False

bias: bool = False

bidirectional: bool = False

cat_encoding: str = 'int'

conv_bias: bool = True

d_conv: int = 16

d_model: int = 64

d_state: int = 128

dropout: float = 0.05

dt_init: str = 'random'

dt_init_floor: float = 0.0001

dt_max: float = 0.1

dt_min: float = 0.0001

dt_rank: str = 'auto'

dt_scale: float = 1.0

embedding_bias: bool = False

embedding_projection: bool = True

embedding_type: str = 'linear'

expand_factor: int = 2

frequencies_init_scale: float = 0.01

head_dropout: float = 0.0

head_layer_sizes: list

head_skip_layers: bool = False

head_use_batch_norm: bool = False

layer_norm: bool = False

layer_norm_after_embedding: bool = False

layer_norm_eps: float = 1e-05

lr: float = 0.0001

lr_factor: float = 0.1

lr_patience: int = 10

mamba_version: str = 'mamba-torch'

n_frequencies: int = 48

n_layers: int = 1

norm: str = 'LayerNorm'

plr_lite: bool = False

use_embeddings: bool = False

use_pscan: bool = False

weight_decay: float = 1e-06

class deeptab.configs.DefaultTabulaRNNConfig(lr=0.0001, lr_patience=10, weight_decay=1e-06, lr_factor=0.1, use_embeddings=False, embedding_activation=torch.nn.Identity, embedding_type='linear', embedding_bias=False, layer_norm_after_embedding=False, d_model=128, plr_lite=False, n_frequencies=48, frequencies_init_scale=0.01, embedding_projection=True, batch_norm=False, layer_norm=False, layer_norm_eps=1e-05, activation=torch.nn.SELU, cat_encoding='int', model_type='RNN', n_layers=4, rnn_dropout=0.2, norm='RMSNorm', residuals=False, head_layer_sizes=<factory>, head_dropout=0.5, head_skip_layers=False, head_activation=torch.nn.SELU, head_use_batch_norm=False, pooling_method='avg', norm_first=False, bias=True, rnn_activation='relu', dim_feedforward=256, d_conv=4, dilation=1, conv_bias=True)

Configuration class for the TabulaRNN model with predefined hyperparameters.

Parameters:

• model_type (str) – Type of model, one of “RNN”, “LSTM”, “GRU”, “mLSTM”, “sLSTM”.

• n_layers (int) – Number of layers in the RNN.

• rnn_dropout (float) – Dropout rate for the RNN layers.

• d_model (int) – Dimensionality of embeddings or model representations.

• norm (str) – Normalization method to be used.

• activation (Callable) – Activation function for the RNN layers.

• residuals (bool) – Whether to include residual connections in the RNN.

• head_layer_sizes (list) – Sizes of the layers in the head of the model.

• head_dropout (float) – Dropout rate for the head layers.

• head_skip_layers (bool) – Whether to skip layers in the head.

• head_activation (Callable) – Activation function for the head layers.

• head_use_batch_norm (bool) – Whether to use batch normalization in the head layers.

• pooling_method (str) – Pooling method to be used (‘avg’, ‘cls’, etc.).

• norm_first (bool) – Whether to apply normalization before other operations in each block.

• layer_norm_eps (float) – Epsilon value for layer normalization.

• bias (bool) – Whether to use bias in the linear layers.

• rnn_activation (str) – Activation function for the RNN layers.

• dim_feedforward (int) – Size of the feedforward network.

• d_conv (int) – Size of the convolutional layer for embedding features.

• dilation (int) – Dilation factor for the convolution.

• conv_bias (bool) – Whether to use bias in the convolutional layers.

batch_norm: bool = False

bias: bool = True

cat_encoding: str = 'int'

conv_bias: bool = True

d_conv: int = 4

d_model: int = 128

dilation: int = 1

dim_feedforward: int = 256

embedding_bias: bool = False

embedding_projection: bool = True

embedding_type: str = 'linear'

frequencies_init_scale: float = 0.01

head_dropout: float = 0.5

head_layer_sizes: list

head_skip_layers: bool = False

head_use_batch_norm: bool = False

layer_norm: bool = False

layer_norm_after_embedding: bool = False

layer_norm_eps: float = 1e-05

lr: float = 0.0001

lr_factor: float = 0.1

lr_patience: int = 10

model_type: str = 'RNN'

n_frequencies: int = 48

n_layers: int = 4

norm: str = 'RMSNorm'

norm_first: bool = False

plr_lite: bool = False

pooling_method: str = 'avg'

residuals: bool = False

rnn_activation: str = 'relu'

rnn_dropout: float = 0.2

use_embeddings: bool = False

weight_decay: float = 1e-06

class deeptab.configs.DefaultMambAttentionConfig(lr=0.0001, lr_patience=10, weight_decay=1e-06, lr_factor=0.1, use_embeddings=False, embedding_activation=torch.nn.Identity, embedding_type='linear', embedding_bias=False, layer_norm_after_embedding=False, d_model=64, plr_lite=False, n_frequencies=48, frequencies_init_scale=0.01, embedding_projection=True, batch_norm=False, layer_norm=False, layer_norm_eps=1e-05, activation=torch.nn.SiLU, cat_encoding='int', n_layers=4, expand_factor=2, n_heads=8, last_layer='attn', n_mamba_per_attention=1, bias=False, d_conv=4, conv_bias=True, dropout=0.0, attn_dropout=0.2, dt_rank='auto', d_state=128, dt_scale=1.0, dt_init='random', dt_max=0.1, dt_min=0.0001, dt_init_floor=0.0001, norm='LayerNorm', head_layer_sizes=<factory>, head_dropout=0.5, head_skip_layers=False, head_activation=torch.nn.SELU, head_use_batch_norm=False, pooling_method='avg', bidirectional=False, use_learnable_interaction=False, use_cls=False, shuffle_embeddings=False, AD_weight_decay=True, BC_layer_norm=False, use_pscan=False, n_attention_layers=1)

Configuration class for the Default Mambular Attention model with predefined hyperparameters.

Parameters:

• d_model (int) – Dimensionality of the model.

• n_layers (int) – Number of layers in the model.

• expand_factor (int) – Expansion factor for the feed-forward layers.

• n_heads (int) – Number of attention heads in the model.

• last_layer (str) – Type of the last layer (e.g., ‘attn’).

• n_mamba_per_attention (int) – Number of Mamba blocks per attention layer.

• bias (bool) – Whether to use bias in the linear layers.

• d_conv (int) – Dimensionality of the convolutional layers.

• conv_bias (bool) – Whether to use bias in the convolutional layers.

• dropout (float) – Dropout rate for regularization.

• attn_dropout (float) – Dropout rate for the attention mechanism.

• dt_rank (str) – Rank of the decision tree.

• d_state (int) – Dimensionality of the state in recurrent layers.

• dt_scale (float) – Scaling factor for the decision tree.

• dt_init (str) – Initialization method for the decision tree.

• dt_max (float) – Maximum value for decision tree initialization.

• dt_min (float) – Minimum value for decision tree initialization.

• dt_init_floor (float) – Floor value for decision tree initialization.

• norm (str) – Type of normalization used in the model.

• activation (Callable) – Activation function for the model.

• head_layer_sizes (list) – Sizes of the fully connected layers in the model’s head.

• head_dropout (float) – Dropout rate for the head layers.

• head_skip_layers (bool) – Whether to use skip connections in the head layers.

• head_activation (Callable) – Activation function for the head layers.

• head_use_batch_norm (bool) – Whether to use batch normalization in the head layers.

• pooling_method (str) – Pooling method to be used (‘avg’, ‘max’, etc.).

• bidirectional (bool) – Whether to process input sequences bidirectionally.

• use_learnable_interaction (bool) – Whether to use learnable feature interactions before passing through Mamba blocks.

• use_cls (bool) – Whether to append a CLS token for sequence pooling.

• shuffle_embeddings (bool) – Whether to shuffle embeddings before passing to Mamba layers.

• cat_encoding (str) – Encoding method for categorical features (‘int’, ‘one-hot’, etc.).

• AD_weight_decay (bool) – Whether weight decay is applied to A-D matrices.

• BC_layer_norm (bool) – Whether to apply layer normalization to B-C matrices.

• use_pscan (bool) – Whether to use PSCAN for the state-space model.

• n_attention_layers (int) – Number of attention layers in the model.

AD_weight_decay: bool = True

BC_layer_norm: bool = False

attn_dropout: float = 0.2

batch_norm: bool = False

bias: bool = False

bidirectional: bool = False

cat_encoding: str = 'int'

conv_bias: bool = True

d_conv: int = 4

d_model: int = 64

d_state: int = 128

dropout: float = 0.0

dt_init: str = 'random'

dt_init_floor: float = 0.0001

dt_max: float = 0.1

dt_min: float = 0.0001

dt_rank: str = 'auto'

dt_scale: float = 1.0

embedding_bias: bool = False

embedding_projection: bool = True

embedding_type: str = 'linear'

expand_factor: int = 2

frequencies_init_scale: float = 0.01

head_dropout: float = 0.5

head_layer_sizes: list

head_skip_layers: bool = False

head_use_batch_norm: bool = False

last_layer: str = 'attn'

layer_norm: bool = False

layer_norm_after_embedding: bool = False

layer_norm_eps: float = 1e-05

lr: float = 0.0001

lr_factor: float = 0.1

lr_patience: int = 10

n_attention_layers: int = 1

n_frequencies: int = 48

n_heads: int = 8

n_layers: int = 4

n_mamba_per_attention: int = 1

norm: str = 'LayerNorm'

plr_lite: bool = False

pooling_method: str = 'avg'

shuffle_embeddings: bool = False

use_cls: bool = False

use_embeddings: bool = False

use_learnable_interaction: bool = False

use_pscan: bool = False

weight_decay: float = 1e-06

class deeptab.configs.DefaultNDTFConfig(lr=0.0001, lr_patience=10, weight_decay=1e-06, lr_factor=0.1, use_embeddings=False, embedding_activation=torch.nn.Identity, embedding_type='linear', embedding_bias=False, layer_norm_after_embedding=False, d_model=32, plr_lite=False, n_frequencies=48, frequencies_init_scale=0.01, embedding_projection=True, batch_norm=False, layer_norm=False, layer_norm_eps=1e-05, activation=torch.nn.ReLU, cat_encoding='int', min_depth=4, max_depth=16, temperature=0.1, node_sampling=0.3, lamda=0.3, n_ensembles=12, penalty_factor=1e-08)

Configuration class for the default Neural Decision Tree Forest (NDTF) model with predefined hyperparameters.

Parameters:

• min_depth (int) – Minimum depth of trees in the forest. Controls the simplest model structure.

• max_depth (int) – Maximum depth of trees in the forest. Controls the maximum complexity of the trees.

• temperature (float) – Temperature parameter for softening the node decisions during path probability calculation.

• node_sampling (float) – Fraction of nodes sampled for regularization penalty calculation. Reduces computation by focusing on a subset of nodes.

• lamda (float) – Regularization parameter to control the complexity of the paths, penalizing overconfident or imbalanced paths.

• n_ensembles (int) – Number of trees in the forest

• penalty_factor (float) – Factor with which the penalty is multiplied

batch_norm: bool = False

cat_encoding: str = 'int'

d_model: int = 32

embedding_bias: bool = False

embedding_projection: bool = True

embedding_type: str = 'linear'

frequencies_init_scale: float = 0.01

lamda: float = 0.3

layer_norm: bool = False

layer_norm_after_embedding: bool = False

layer_norm_eps: float = 1e-05

lr: float = 0.0001

lr_factor: float = 0.1

lr_patience: int = 10

max_depth: int = 16

min_depth: int = 4

n_ensembles: int = 12

n_frequencies: int = 48

node_sampling: float = 0.3

penalty_factor: float = 1e-08

plr_lite: bool = False

temperature: float = 0.1

use_embeddings: bool = False

weight_decay: float = 1e-06

class deeptab.configs.DefaultNODEConfig(lr=0.0001, lr_patience=10, weight_decay=1e-06, lr_factor=0.1, use_embeddings=False, embedding_activation=torch.nn.Identity, embedding_type='linear', embedding_bias=False, layer_norm_after_embedding=False, d_model=32, plr_lite=False, n_frequencies=48, frequencies_init_scale=0.01, embedding_projection=True, batch_norm=False, layer_norm=False, layer_norm_eps=1e-05, activation=torch.nn.ReLU, cat_encoding='int', num_layers=4, layer_dim=128, tree_dim=1, depth=6, norm=None, head_layer_sizes=<factory>, head_dropout=0.3, head_skip_layers=False, head_activation=torch.nn.ReLU, head_use_batch_norm=False)

Configuration class for the Neural Oblivious Decision Ensemble (NODE) model.

Parameters:

• num_layers (int) – Number of dense layers in the model.

• layer_dim (int) – Dimensionality of each dense layer.

• tree_dim (int) – Dimensionality of the output from each tree leaf.

• depth (int) – Depth of each decision tree in the ensemble.

• norm (str | None) – Type of normalization to use in the model.

• head_layer_sizes (list) – Sizes of the layers in the model’s head.

• head_dropout (float) – Dropout rate for the head layers.

• head_skip_layers (bool) – Whether to skip layers in the head.

• head_activation (Callable) – Activation function for the head layers.

• head_use_batch_norm (bool) – Whether to use batch normalization in the head layers.

batch_norm: bool = False

cat_encoding: str = 'int'

d_model: int = 32

depth: int = 6

embedding_bias: bool = False

embedding_projection: bool = True

embedding_type: str = 'linear'

frequencies_init_scale: float = 0.01

head_dropout: float = 0.3

head_layer_sizes: list

head_skip_layers: bool = False

head_use_batch_norm: bool = False

layer_dim: int = 128

layer_norm: bool = False

layer_norm_after_embedding: bool = False

layer_norm_eps: float = 1e-05

lr: float = 0.0001

lr_factor: float = 0.1

lr_patience: int = 10

n_frequencies: int = 48

norm: str | None = None

num_layers: int = 4

plr_lite: bool = False

tree_dim: int = 1

use_embeddings: bool = False

weight_decay: float = 1e-06

class deeptab.configs.DefaultTabMConfig(lr=0.0001, lr_patience=10, weight_decay=1e-06, lr_factor=0.1, use_embeddings=False, embedding_activation=torch.nn.Identity, embedding_type='linear', embedding_bias=False, layer_norm_after_embedding=False, d_model=32, plr_lite=False, n_frequencies=48, frequencies_init_scale=0.01, embedding_projection=True, batch_norm=False, layer_norm=False, layer_norm_eps=1e-05, activation=torch.nn.ReLU, cat_encoding='int', layer_sizes=<factory>, dropout=0.5, norm=None, use_glu=False, ensemble_size=32, ensemble_scaling_in=True, ensemble_scaling_out=True, ensemble_bias=True, scaling_init='ones', average_ensembles=False, model_type='mini', average_embeddings=True)

Configuration class for the TabM model with batch ensembling and predefined hyperparameters.

Parameters:

• layer_sizes (list) – Sizes of the layers in the model.

• activation (Callable) – Activation function for the model layers.

• dropout (float) – Dropout rate for regularization.

• norm (str | None) – Normalization method to be used, if any.

• use_glu (bool) – Whether to use Gated Linear Units (GLU) in the model.

• ensemble_size (int) – Number of ensemble members for batch ensembling.

• ensemble_scaling_in (bool) – Whether to use input scaling for each ensemble member.

• ensemble_scaling_out (bool) – Whether to use output scaling for each ensemble member.

• ensemble_bias (bool) – Whether to use a unique bias term for each ensemble member.

• scaling_init (Literal['ones', 'random-signs', 'normal']) – Initialization method for scaling weights.

• average_ensembles (bool) – Whether to average the outputs of the ensembles.

• model_type (Literal['mini', 'full']) – Model type to use (‘mini’ for reduced version, ‘full’ for complete model).

average_embeddings: bool = True

average_ensembles: bool = False

batch_norm: bool = False

cat_encoding: str = 'int'

d_model: int = 32

dropout: float = 0.5

embedding_bias: bool = False

embedding_projection: bool = True

embedding_type: str = 'linear'

ensemble_bias: bool = True

ensemble_scaling_in: bool = True

ensemble_scaling_out: bool = True

ensemble_size: int = 32

frequencies_init_scale: float = 0.01

layer_norm: bool = False

layer_norm_after_embedding: bool = False

layer_norm_eps: float = 1e-05

layer_sizes: list

lr: float = 0.0001

lr_factor: float = 0.1

lr_patience: int = 10

model_type: Literal['mini', 'full'] = 'mini'

n_frequencies: int = 48

norm: str | None = None

plr_lite: bool = False

scaling_init: Literal['ones', 'random-signs', 'normal'] = 'ones'

use_embeddings: bool = False

use_glu: bool = False

weight_decay: float = 1e-06

class deeptab.configs.DefaultSAINTConfig(lr=0.0001, lr_patience=10, weight_decay=1e-06, lr_factor=0.1, use_embeddings=False, embedding_activation=torch.nn.Identity, embedding_type='linear', embedding_bias=False, layer_norm_after_embedding=False, d_model=128, plr_lite=False, n_frequencies=48, frequencies_init_scale=0.01, embedding_projection=True, batch_norm=False, layer_norm=False, layer_norm_eps=1e-05, activation=torch.nn.GELU, cat_encoding='int', n_layers=1, n_heads=2, attn_dropout=0.2, ff_dropout=0.1, norm='LayerNorm', norm_first=False, bias=True, head_layer_sizes=<factory>, head_dropout=0.5, head_skip_layers=False, head_activation=torch.nn.SELU, head_use_batch_norm=False, pooling_method='cls', use_cls=True)

Configuration class for the SAINT model with predefined hyperparameters.

Parameters:

• n_layers (int) – Number of transformer layers.

• n_heads (int) – Number of attention heads in the transformer.

• d_model (int) – Dimensionality of embeddings or model representations.

• attn_dropout (float) – Dropout rate for the attention mechanism.

• ff_dropout (float) – Dropout rate for the feed-forward layers.

• norm (str) – Type of normalization to be used (‘LayerNorm’, ‘RMSNorm’, etc.).

• activation (Callable) – Activation function for the transformer layers.

• transformer_activation (callable, default=ReGLU()) – Activation function for the transformer feed-forward layers.

• transformer_dim_feedforward (int, default=256) – Dimensionality of the feed-forward layers in the transformer.

• norm_first (bool) – Whether to apply normalization before other operations in each transformer block.

• bias (bool) – Whether to use bias in linear layers.

• head_layer_sizes (list) – Sizes of the fully connected layers in the model’s head.

• head_dropout (float) – Dropout rate for the head layers.

• head_skip_layers (bool) – Whether to use skip connections in the head layers.

• head_activation (Callable) – Activation function for the head layers.

• head_use_batch_norm (bool) – Whether to use batch normalization in the head layers.

• pooling_method (str) – Pooling method to be used (‘cls’, ‘avg’, etc.).

• use_cls (bool) – Whether to use a CLS token for pooling.

• cat_encoding (str) – Method for encoding categorical features (‘int’, ‘one-hot’, or ‘linear’).

attn_dropout: float = 0.2

batch_norm: bool = False

bias: bool = True

cat_encoding: str = 'int'

d_model: int = 128

embedding_bias: bool = False

embedding_projection: bool = True

embedding_type: str = 'linear'

ff_dropout: float = 0.1

frequencies_init_scale: float = 0.01

head_dropout: float = 0.5

head_layer_sizes: list

head_skip_layers: bool = False

head_use_batch_norm: bool = False

layer_norm: bool = False

layer_norm_after_embedding: bool = False

layer_norm_eps: float = 1e-05

lr: float = 0.0001

lr_factor: float = 0.1

lr_patience: int = 10

n_frequencies: int = 48

n_heads: int = 2

n_layers: int = 1

norm: str = 'LayerNorm'

norm_first: bool = False

plr_lite: bool = False

pooling_method: str = 'cls'

use_cls: bool = True

use_embeddings: bool = False

weight_decay: float = 1e-06

class deeptab.configs.DefaultAutoIntConfig(lr=0.0001, lr_patience=10, weight_decay=1e-06, lr_factor=0.1, use_embeddings=False, embedding_activation=torch.nn.Identity, embedding_type='linear', embedding_bias=False, layer_norm_after_embedding=False, d_model=128, plr_lite=False, n_frequencies=48, frequencies_init_scale=0.01, embedding_projection=True, batch_norm=False, layer_norm=False, layer_norm_eps=1e-05, activation=torch.nn.ReLU, cat_encoding='int', n_layers=4, n_heads=8, attn_dropout=0.2, fprenorm=False, transformer_dim_feedforward=256, bias=True, use_cls=False, kv_compression=0.5, kv_compression_sharing='key-value')

Configuration class for the AutoInt model with predefined hyperparameters.

Parameters:

• d_model (int) – Dimensionality of the transformer model.

• n_layers (int) – Number of transformer layers.

• n_heads (int) – Number of attention heads in the transformer.

• attn_dropout (float) – Dropout rate for the attention mechanism.

• transformer_dim_feedforward (int) – Dimensionality of the feed-forward layers in the transformer.

• prenorm (bool, default=False) – Whether to apply normalization before last layer.

• bias (bool) – Whether to use bias in linear layers.

• cat_encoding (str) – Method for encoding categorical features (‘int’, ‘one-hot’, or ‘linear’).

• kv_compression (float) – Compression ratio for key-value pairs.

• kv_compression_sharing (str) – Sharing strategy for key-value compression (‘headwise’, or ‘key-value’).

attn_dropout: float = 0.2

batch_norm: bool = False

bias: bool = True

cat_encoding: str = 'int'

d_model: int = 128

embedding_bias: bool = False

embedding_projection: bool = True

embedding_type: str = 'linear'

fprenorm: bool = False

frequencies_init_scale: float = 0.01

kv_compression: float = 0.5

kv_compression_sharing: str = 'key-value'

layer_norm: bool = False

layer_norm_after_embedding: bool = False

layer_norm_eps: float = 1e-05

lr: float = 0.0001

lr_factor: float = 0.1

lr_patience: int = 10

n_frequencies: int = 48

n_heads: int = 8

n_layers: int = 4

plr_lite: bool = False

transformer_dim_feedforward: int = 256

use_cls: bool = False

use_embeddings: bool = False

weight_decay: float = 1e-06

class deeptab.configs.DefaultENODEConfig(lr=0.0001, lr_patience=10, weight_decay=1e-06, lr_factor=0.1, use_embeddings=False, embedding_activation=torch.nn.Identity, embedding_type='linear', embedding_bias=False, layer_norm_after_embedding=False, d_model=8, plr_lite=False, n_frequencies=48, frequencies_init_scale=0.01, embedding_projection=True, batch_norm=False, layer_norm=False, layer_norm_eps=1e-05, activation=torch.nn.ReLU, cat_encoding='int', num_layers=4, layer_dim=64, tree_dim=1, depth=6, norm=None, head_layer_sizes=<factory>, head_dropout=0.3, head_skip_layers=False, head_activation=torch.nn.ReLU, head_use_batch_norm=False)

Configuration class for the Neural Oblivious Decision Ensemble (NODE) model.

Parameters:

• num_layers (int) – Number of dense layers in the model.

• layer_dim (int) – Dimensionality of each dense layer.

• tree_dim (int) – Dimensionality of the output from each tree leaf.

• depth (int) – Depth of each decision tree in the ensemble.

• norm (str | None) – Type of normalization to use in the model.

• head_layer_sizes (list) – Sizes of the layers in the model’s head.

• head_dropout (float) – Dropout rate for the head layers.

• head_skip_layers (bool) – Whether to skip layers in the head.

• head_activation (Callable) – Activation function for the head layers.

• head_use_batch_norm (bool) – Whether to use batch normalization in the head layers.

batch_norm: bool = False

cat_encoding: str = 'int'

d_model: int = 8

depth: int = 6

embedding_bias: bool = False

embedding_projection: bool = True

embedding_type: str = 'linear'

frequencies_init_scale: float = 0.01

head_dropout: float = 0.3

head_layer_sizes: list

head_skip_layers: bool = False

head_use_batch_norm: bool = False

layer_dim: int = 64

layer_norm: bool = False

layer_norm_after_embedding: bool = False

layer_norm_eps: float = 1e-05

lr: float = 0.0001

lr_factor: float = 0.1

lr_patience: int = 10

n_frequencies: int = 48

norm: str | None = None

num_layers: int = 4

plr_lite: bool = False

tree_dim: int = 1

use_embeddings: bool = False

weight_decay: float = 1e-06

class deeptab.configs.DefaultModernNCAConfig(lr=0.0001, lr_patience=10, weight_decay=1e-05, lr_factor=0.1, use_embeddings=False, embedding_activation=torch.nn.Identity, embedding_type='plr', embedding_bias=False, layer_norm_after_embedding=False, d_model=32, plr_lite=True, n_frequencies=75, frequencies_init_scale=0.045, embedding_projection=True, batch_norm=False, layer_norm=False, layer_norm_eps=1e-05, activation=torch.nn.ReLU, cat_encoding='int', dim=128, d_block=512, n_blocks=4, dropout=0.1, temperature=0.75, sample_rate=0.5, num_embeddings=None, optimizer_type='AdamW', learning_rate=0.01, head_layer_sizes=<factory>, head_dropout=0.5, head_skip_layers=False, head_activation=torch.nn.SELU, head_use_batch_norm=False)

Default configuration for the ModernNCA model.

batch_norm: bool = False

cat_encoding: str = 'int'

d_block: int = 512

d_model: int = 32

dim: int = 128

dropout: float = 0.1

embedding_bias: bool = False

embedding_projection: bool = True

embedding_type: str = 'plr'

frequencies_init_scale: float = 0.045

head_dropout: float = 0.5

head_layer_sizes: list

head_skip_layers: bool = False

head_use_batch_norm: bool = False

layer_norm: bool = False

layer_norm_after_embedding: bool = False

layer_norm_eps: float = 1e-05

learning_rate: float = 0.01

lr: float = 0.0001

lr_factor: float = 0.1

lr_patience: int = 10

n_blocks: int = 4

n_frequencies: int = 75

num_embeddings: dict | None = None

optimizer_type: str = 'AdamW'

plr_lite: bool = True

sample_rate: float = 0.5

temperature: float = 0.75

use_embeddings: bool = False

weight_decay: float = 1e-05

class deeptab.configs.DefaultTangosConfig(lr=0.0001, lr_patience=10, weight_decay=1e-06, lr_factor=0.1, use_embeddings=False, embedding_activation=torch.nn.Identity, embedding_type='linear', embedding_bias=False, layer_norm_after_embedding=False, d_model=32, plr_lite=False, n_frequencies=48, frequencies_init_scale=0.01, embedding_projection=True, batch_norm=False, layer_norm=False, layer_norm_eps=1e-05, activation=torch.nn.ReLU, cat_encoding='int', layer_sizes=<factory>, skip_layers=False, dropout=0.2, use_glu=False, skip_connections=False, lamda1=0.5, lamda2=0.1, subsample=0.5)

Configuration class for the default Multi-Layer Perceptron (TANGOS) model with predefined hyperparameters.

Parameters:

• layer_sizes (list) – Sizes of the layers in the TANGOS.

• activation (Callable) – Activation function for the TANGOS layers.

• skip_layers (bool) – Whether to skip layers in the TANGOS.

• dropout (float) – Dropout rate for regularization.

• use_glu (bool) – Whether to use Gated Linear Units (GLU) in the TANGOS.

• skip_connections (bool) – Whether to use skip connections in the TANGOS.

batch_norm: bool = False

cat_encoding: str = 'int'

d_model: int = 32

dropout: float = 0.2

embedding_bias: bool = False

embedding_projection: bool = True

embedding_type: str = 'linear'

frequencies_init_scale: float = 0.01

lamda1: float = 0.5

lamda2: float = 0.1

layer_norm: bool = False

layer_norm_after_embedding: bool = False

layer_norm_eps: float = 1e-05

layer_sizes: list

lr: float = 0.0001

lr_factor: float = 0.1

lr_patience: int = 10

n_frequencies: int = 48

plr_lite: bool = False

skip_connections: bool = False

skip_layers: bool = False

subsample: float = 0.5

use_embeddings: bool = False

use_glu: bool = False

weight_decay: float = 1e-06

class deeptab.configs.DefaultTromptConfig(lr=0.0001, lr_patience=10, weight_decay=1e-06, lr_factor=0.1, use_embeddings=False, embedding_activation=torch.nn.Identity, embedding_type='linear', embedding_bias=False, layer_norm_after_embedding=False, d_model=128, plr_lite=False, n_frequencies=48, frequencies_init_scale=0.01, embedding_projection=True, batch_norm=False, layer_norm=False, layer_norm_eps=1e-05, activation=torch.nn.ReLU, cat_encoding='int', n_cycles=6, n_cells=4, P=128)

Configuration class for the Trompt model with predefined hyperparameters.

Parameters:

• d_model (int) – Dimensionality of the transformer model.

• n_cycles (int) – Number of cycles in the Trompt model.

• n_cells (int) – Number of cells in each cycle.

• P (int) – Number of steps in the Trompt model.

P: int = 128

batch_norm: bool = False

cat_encoding: str = 'int'

d_model: int = 128

embedding_bias: bool = False

embedding_projection: bool = True

embedding_type: str = 'linear'

frequencies_init_scale: float = 0.01

layer_norm: bool = False

layer_norm_after_embedding: bool = False

layer_norm_eps: float = 1e-05

lr: float = 0.0001

lr_factor: float = 0.1

lr_patience: int = 10

n_cells: int = 4

n_cycles: int = 6

n_frequencies: int = 48

plr_lite: bool = False

use_embeddings: bool = False

weight_decay: float = 1e-06