from .sklearn_base_regressor import SklearnBaseRegressor
from .sklearn_base_classifier import SklearnBaseClassifier
from .sklearn_base_lss import SklearnBaseLSS
from ..base_models.mlp import MLP
from ..configs.mlp_config import DefaultMLPConfig
[文档]class MLPRegressor(SklearnBaseRegressor):
"""
Multi-Layer Perceptron regressor. This class extends the SklearnBaseRegressor class and uses the MLP model
with the default MLP configuration.
The accepted arguments to the MLPRegressor class include both the attributes in the DefaultMLPConfig 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.
layer_sizes : list, default=(128, 128, 32)
Sizes of the layers in the MLP.
activation : callable, default=nn.SELU()
Activation function for the MLP layers.
skip_layers : bool, default=False
Whether to skip layers in the MLP.
dropout : float, default=0.5
Dropout rate for regularization.
norm : str, default=None
Normalization method to be used, if any.
use_glu : bool, default=False
Whether to use Gated Linear Units (GLU) in the MLP.
skip_connections : bool, default=False
Whether to use skip connections in the MLP.
batch_norm : bool, default=False
Whether to use batch normalization in the MLP layers.
layer_norm : bool, default=False
Whether to use layer normalization in the MLP layers.
use_embeddings : bool, default=False
Whether to use embedding layers for all features.
embedding_activation : callable, default=nn.Identity()
Activation function for embeddings.
layer_norm_after_embedding : bool, default=False
Whether to apply layer normalization after embedding.
d_model : int, default=32
Dimensionality of the embeddings.
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.
Notes
-----
- The accepted arguments to the MLPRegressor class are the same as the attributes in the DefaultMLPConfig dataclass.
- MLPRegressor uses SklearnBaseRegressor as the parent class. The methods for fitting, predicting, and evaluating the model are inherited from the parent class. Please refer to the parent class for more information.
See Also
--------
mambular.models.SklearnBaseRegressor : The parent class for MLPRegressor.
Examples
--------
>>> from mambular.models import MLPRegressor
>>> model = MLPRegressor(layer_sizes=[128, 128, 64], activation=nn.ReLU())
>>> model.fit(X_train, y_train)
>>> preds = model.predict(X_test)
>>> model.evaluate(X_test, y_test)
"""
def __init__(self, **kwargs):
super().__init__(model=MLP, config=DefaultMLPConfig, **kwargs)
[文档]class MLPClassifier(SklearnBaseClassifier):
"""
Multi-Layer Perceptron classifier. This class extends the SklearnBaseClassifier class and uses the MLP model
with the default MLP configuration.
The accepted arguments to the MLPClassifier class include both the attributes in the DefaultMLPConfig 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.
layer_sizes : list, default=(128, 128, 32)
Sizes of the layers in the MLP.
activation : callable, default=nn.SELU()
Activation function for the MLP layers.
skip_layers : bool, default=False
Whether to skip layers in the MLP.
dropout : float, default=0.5
Dropout rate for regularization.
norm : str, default=None
Normalization method to be used, if any.
use_glu : bool, default=False
Whether to use Gated Linear Units (GLU) in the MLP.
skip_connections : bool, default=False
Whether to use skip connections in the MLP.
batch_norm : bool, default=False
Whether to use batch normalization in the MLP layers.
layer_norm : bool, default=False
Whether to use layer normalization in the MLP layers.
use_embeddings : bool, default=False
Whether to use embedding layers for all features.
embedding_activation : callable, default=nn.Identity()
Activation function for embeddings.
layer_norm_after_embedding : bool, default=False
Whether to apply layer normalization after embedding.
d_model : int, default=32
Dimensionality of the embeddings.
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.
Notes
-----
- The accepted arguments to the MLPClassifier class are the same as the attributes in the DefaultMLPConfig dataclass.
- MLPClassifier uses SklearnBaseClassifieras the parent class. The methods for fitting, predicting, and evaluating the model are inherited from the parent class. Please refer to the parent class for more information.
See Also
--------
mambular.models.SklearnBaseRegressor : The parent class for MLPClassifier.
Examples
--------
>>> from mambular.models import MLPClassifier
>>> model = MLPClassifier(layer_sizes=[128, 128, 64], activation=nn.ReLU())
>>> model.fit(X_train, y_train)
>>> preds = model.predict(X_test)
>>> model.evaluate(X_test, y_test)
"""
def __init__(self, **kwargs):
super().__init__(model=MLP, config=DefaultMLPConfig, **kwargs)
[文档]class MLPLSS(SklearnBaseLSS):
"""
Multi-Layer Perceptron for distributional regression. This class extends the SklearnBaseLSS class and uses the MLP model
with the default MLP configuration.
The accepted arguments to the MLPLSS class include both the attributes in the DefaultMLPConfig 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.
layer_sizes : list, default=(128, 128, 32)
Sizes of the layers in the MLP.
activation : callable, default=nn.SELU()
Activation function for the MLP layers.
skip_layers : bool, default=False
Whether to skip layers in the MLP.
dropout : float, default=0.5
Dropout rate for regularization.
norm : str, default=None
Normalization method to be used, if any.
use_glu : bool, default=False
Whether to use Gated Linear Units (GLU) in the MLP.
skip_connections : bool, default=False
Whether to use skip connections in the MLP.
batch_norm : bool, default=False
Whether to use batch normalization in the MLP layers.
layer_norm : bool, default=False
Whether to use layer normalization in the MLP layers.
use_embeddings : bool, default=False
Whether to use embedding layers for all features.
embedding_activation : callable, default=nn.Identity()
Activation function for embeddings.
layer_norm_after_embedding : bool, default=False
Whether to apply layer normalization after embedding.
d_model : int, default=32
Dimensionality of the embeddings.
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.
task : str, default="regression"
Indicates the type of machine learning task ('regression' or 'classification'). This can
influence certain preprocessing behaviors, especially when using decision tree-based binning as ple.
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.
Notes
-----
- The accepted arguments to the MLPLSS class are the same as the attributes in the DefaultMLPConfig dataclass.
- MLPLSS uses SklearnBaseLSS as the parent class. The methods for fitting, predicting, and evaluating the model are inherited from the parent class. Please refer to the parent class for more information.
See Also
--------
mambular.models.SklearnBaseLSS : The parent class for MLPLSS.
Examples
--------
>>> from mambular.models import MLPLSS
>>> model = MLPLSS(layer_sizes=[128, 128, 64], activation=nn.ReLU())
>>> model.fit(X_train, y_train)
>>> preds = model.predict(X_test)
>>> model.evaluate(X_test, y_test)
"""
def __init__(self, **kwargs):
super().__init__(model=MLP, config=DefaultMLPConfig, **kwargs)
