from .sklearn_base_regressor import SklearnBaseRegressor
from .sklearn_base_lss import SklearnBaseLSS
from .sklearn_base_classifier import SklearnBaseClassifier
from ..base_models.mambatab import MambaTab
from ..configs.mambatab_config import DefaultMambaTabConfig
[文档]class MambaTabRegressor(SklearnBaseRegressor):
"""
MambaTab regressor. This class extends the SklearnBaseRegressor class and uses the MambaTab model
with the default MambaTab configuration.
The accepted arguments to the MambaTabRegressor class include both the attributes in the DefaultMambaTabConfig dataclass
and the parameters for the Preprocessor class.
Parameters
----------
lr : float, default=1e-04
Learning rate for the optimizer.
lr_patience : int, default=10
Number of epochs with no improvement after which learning rate will be reduced.
weight_decay : float, default=1e-06
Weight decay (L2 penalty) for the optimizer.
lr_factor : float, default=0.1
Factor by which the learning rate will be reduced.
d_model : int, default=64
Dimensionality of the model.
n_layers : int, default=8
Number of layers in the model.
expand_factor : int, default=2
Expansion factor for the feed-forward layers.
bias : bool, default=False
Whether to use bias in the linear layers.
d_conv : int, default=16
Dimensionality of the convolutional layers.
conv_bias : bool, default=True
Whether to use bias in the convolutional layers.
dropout : float, default=0.05
Dropout rate for regularization.
dt_rank : str, default="auto"
Rank of the decision tree.
d_state : int, default=32
Dimensionality of the state in recurrent layers.
dt_scale : float, default=1.0
Scaling factor for decision tree.
dt_init : str, default="random"
Initialization method for decision tree.
dt_max : float, default=0.1
Maximum value for decision tree initialization.
dt_min : float, default=1e-04
Minimum value for decision tree initialization.
dt_init_floor : float, default=1e-04
Floor value for decision tree initialization.
norm : str, default="RMSNorm"
Normalization method to be used.
activation : callable, default=nn.SELU()
Activation function for the model.
num_embedding_activation : callable, default=nn.Identity()
Activation function for numerical embeddings.
head_layer_sizes : list, default=(128, 64, 32)
Sizes of the layers in the head of the model.
head_dropout : float, default=0.5
Dropout rate for the head layers.
head_skip_layers : bool, default=False
Whether to skip layers in the head.
head_activation : callable, default=nn.SELU()
Activation function for the head layers.
head_use_batch_norm : bool, default=False
Whether to use batch normalization in the head layers.
norm : str, default="LayerNorm"
Normalization method to be used.
axis : int, default=1
Axis over which Mamba iterates. If 1, it iterates over the rows; if 0, it iterates over the columns.
n_bins : int, default=50
The number of bins to use for numerical feature binning. This parameter is relevant
only if `numerical_preprocessing` is set to 'binning' or 'one_hot'.
numerical_preprocessing : str, default="ple"
The preprocessing strategy for numerical features. Valid options are
'binning', 'one_hot', 'standardization', and 'normalization'.
use_decision_tree_bins : bool, default=False
If True, uses decision tree regression/classification to determine
optimal bin edges for numerical feature binning. This parameter is
relevant only if `numerical_preprocessing` is set to 'binning' or 'one_hot'.
binning_strategy : str, default="uniform"
Defines the strategy for binning numerical features. Options include 'uniform',
'quantile', or other sklearn-compatible strategies.
cat_cutoff : float or int, default=0.03
Indicates the cutoff after which integer values are treated as categorical.
If float, it's treated as a percentage. If int, it's the maximum number of
unique values for a column to be considered categorical.
treat_all_integers_as_numerical : bool, default=False
If True, all integer columns will be treated as numerical, regardless
of their unique value count or proportion.
degree : int, default=3
The degree of the polynomial features to be used in preprocessing.
knots : int, default=12
The number of knots to be used in spline transformations.
"""
def __init__(self, **kwargs):
super().__init__(model=MambaTab, config=DefaultMambaTabConfig, **kwargs)
[文档]class MambaTabClassifier(SklearnBaseClassifier):
"""
MambaTab Classifier. This class extends the SklearnBaseClassifier class and uses the MambaTab model
with the default MambaTab configuration.
The accepted arguments to the MambaTabClassifier class include both the attributes in the DefaultMambaTabConfig dataclass
and the parameters for the Preprocessor class.
Parameters
----------
lr : float, default=1e-04
Learning rate for the optimizer.
lr_patience : int, default=10
Number of epochs with no improvement after which learning rate will be reduced.
weight_decay : float, default=1e-06
Weight decay (L2 penalty) for the optimizer.
lr_factor : float, default=0.1
Factor by which the learning rate will be reduced.
d_model : int, default=64
Dimensionality of the model.
n_layers : int, default=8
Number of layers in the model.
expand_factor : int, default=2
Expansion factor for the feed-forward layers.
bias : bool, default=False
Whether to use bias in the linear layers.
d_conv : int, default=16
Dimensionality of the convolutional layers.
conv_bias : bool, default=True
Whether to use bias in the convolutional layers.
dropout : float, default=0.05
Dropout rate for regularization.
dt_rank : str, default="auto"
Rank of the decision tree.
d_state : int, default=32
Dimensionality of the state in recurrent layers.
dt_scale : float, default=1.0
Scaling factor for decision tree.
dt_init : str, default="random"
Initialization method for decision tree.
dt_max : float, default=0.1
Maximum value for decision tree initialization.
dt_min : float, default=1e-04
Minimum value for decision tree initialization.
dt_init_floor : float, default=1e-04
Floor value for decision tree initialization.
norm : str, default="RMSNorm"
Normalization method to be used.
activation : callable, default=nn.SELU()
Activation function for the model.
num_embedding_activation : callable, default=nn.Identity()
Activation function for numerical embeddings.
head_layer_sizes : list, default=(128, 64, 32)
Sizes of the layers in the head of the model.
head_dropout : float, default=0.5
Dropout rate for the head layers.
head_skip_layers : bool, default=False
Whether to skip layers in the head.
head_activation : callable, default=nn.SELU()
Activation function for the head layers.
head_use_batch_norm : bool, default=False
Whether to use batch normalization in the head layers.
norm : str, default="LayerNorm"
Normalization method to be used.
axis : int, default=1
Axis over which Mamba iterates. If 1, it iterates over the rows; if 0, it iterates over the columns.
n_bins : int, default=50
The number of bins to use for numerical feature binning. This parameter is relevant
only if `numerical_preprocessing` is set to 'binning' or 'one_hot'.
numerical_preprocessing : str, default="ple"
The preprocessing strategy for numerical features. Valid options are
'binning', 'one_hot', 'standardization', and 'normalization'.
use_decision_tree_bins : bool, default=False
If True, uses decision tree regression/classification to determine
optimal bin edges for numerical feature binning. This parameter is
relevant only if `numerical_preprocessing` is set to 'binning' or 'one_hot'.
binning_strategy : str, default="uniform"
Defines the strategy for binning numerical features. Options include 'uniform',
'quantile', or other sklearn-compatible strategies.
cat_cutoff : float or int, default=0.03
Indicates the cutoff after which integer values are treated as categorical.
If float, it's treated as a percentage. If int, it's the maximum number of
unique values for a column to be considered categorical.
treat_all_integers_as_numerical : bool, default=False
If True, all integer columns will be treated as numerical, regardless
of their unique value count or proportion.
degree : int, default=3
The degree of the polynomial features to be used in preprocessing.
knots : int, default=12
The number of knots to be used in spline transformations.
"""
def __init__(self, **kwargs):
super().__init__(model=MambaTab, config=DefaultMambaTabConfig, **kwargs)
[文档]class MambaTabLSS(SklearnBaseLSS):
"""
MambaTab for distributinoal regression. This class extends the SklearnBaseLSS class and uses the MambaTab model
with the default MambaTab configuration.
The accepted arguments to the MambaTabLSS class include both the attributes in the DefaultMambaTabConfig dataclass
and the parameters for the Preprocessor class.
Parameters
----------
lr : float, default=1e-04
Learning rate for the optimizer.
lr_patience : int, default=10
Number of epochs with no improvement after which learning rate will be reduced.
family : str, default=None
Distributional family to be used for the model.
weight_decay : float, default=1e-06
Weight decay (L2 penalty) for the optimizer.
lr_factor : float, default=0.1
Factor by which the learning rate will be reduced.
d_model : int, default=64
Dimensionality of the model.
n_layers : int, default=8
Number of layers in the model.
expand_factor : int, default=2
Expansion factor for the feed-forward layers.
bias : bool, default=False
Whether to use bias in the linear layers.
d_conv : int, default=16
Dimensionality of the convolutional layers.
conv_bias : bool, default=True
Whether to use bias in the convolutional layers.
dropout : float, default=0.05
Dropout rate for regularization.
dt_rank : str, default="auto"
Rank of the decision tree.
d_state : int, default=32
Dimensionality of the state in recurrent layers.
dt_scale : float, default=1.0
Scaling factor for decision tree.
dt_init : str, default="random"
Initialization method for decision tree.
dt_max : float, default=0.1
Maximum value for decision tree initialization.
dt_min : float, default=1e-04
Minimum value for decision tree initialization.
dt_init_floor : float, default=1e-04
Floor value for decision tree initialization.
norm : str, default="RMSNorm"
Normalization method to be used.
activation : callable, default=nn.SELU()
Activation function for the model.
num_embedding_activation : callable, default=nn.Identity()
Activation function for numerical embeddings.
head_layer_sizes : list, default=(128, 64, 32)
Sizes of the layers in the head of the model.
head_dropout : float, default=0.5
Dropout rate for the head layers.
head_skip_layers : bool, default=False
Whether to skip layers in the head.
head_activation : callable, default=nn.SELU()
Activation function for the head layers.
head_use_batch_norm : bool, default=False
Whether to use batch normalization in the head layers.
norm : str, default="LayerNorm"
Normalization method to be used.
axis : int, default=1
Axis over which Mamba iterates. If 1, it iterates over the rows; if 0, it iterates over the columns.
n_bins : int, default=50
The number of bins to use for numerical feature binning. This parameter is relevant
only if `numerical_preprocessing` is set to 'binning' or 'one_hot'.
numerical_preprocessing : str, default="ple"
The preprocessing strategy for numerical features. Valid options are
'binning', 'one_hot', 'standardization', and 'normalization'.
use_decision_tree_bins : bool, default=False
If True, uses decision tree regression/classification to determine
optimal bin edges for numerical feature binning. This parameter is
relevant only if `numerical_preprocessing` is set to 'binning' or 'one_hot'.
binning_strategy : str, default="uniform"
Defines the strategy for binning numerical features. Options include 'uniform',
'quantile', or other sklearn-compatible strategies.
cat_cutoff : float or int, default=0.03
Indicates the cutoff after which integer values are treated as categorical.
If float, it's treated as a percentage. If int, it's the maximum number of
unique values for a column to be considered categorical.
treat_all_integers_as_numerical : bool, default=False
If True, all integer columns will be treated as numerical, regardless
of their unique value count or proportion.
degree : int, default=3
The degree of the polynomial features to be used in preprocessing.
knots : int, default=12
The number of knots to be used in spline transformations.
"""
def __init__(self, **kwargs):
super().__init__(model=MambaTab, config=DefaultMambaTabConfig, **kwargs)
