regressor

Regressor(module, loss_fn='mse', optimizer_fn='sgd', lr=0.001, is_feature_incremental=False, device='cpu', seed=42, **kwargs)

Bases: DeepEstimator, MiniBatchRegressor

Wrapper for PyTorch regression models that enables compatibility with River.

PARAMETER	DESCRIPTION
module	Torch Module that builds the autoencoder to be wrapped. The Module should accept parameter n_features so that the returned model's input shape can be determined based on the number of features in the initial training example. TYPE: Type[Module]
loss_fn	Loss function to be used for training the wrapped model. Can be a loss function provided by torch.nn.functional or one of the following: 'mse', 'l1', 'cross_entropy', 'binary_crossentropy', 'smooth_l1', 'kl_div'. TYPE: Union[str, Callable] DEFAULT: 'mse'
optimizer_fn	Optimizer to be used for training the wrapped model. Can be an optimizer class provided by torch.optim or one of the following: "adam", "adam_w", "sgd", "rmsprop", "lbfgs". TYPE: Union[str, Callable] DEFAULT: 'sgd'
lr	Learning rate of the optimizer. TYPE: float DEFAULT: 0.001
device	Device to run the wrapped model on. Can be "cpu" or "cuda". TYPE: str DEFAULT: 'cpu'
seed	Random seed to be used for training the wrapped model. TYPE: int DEFAULT: 42
**kwargs	Parameters to be passed to the Module or the optimizer. DEFAULT: {}

Examples:

learn_many(X, y)

Performs one step of training with a batch of examples.

PARAMETER	DESCRIPTION
x	Input examples.
y	Target values. TYPE: Series

RETURNS	DESCRIPTION
Regressor	The regressor itself.

learn_one(x, y, **kwargs)

Performs one step of training with a single example.

PARAMETER	DESCRIPTION
x	Input example. TYPE: dict
y	Target value. TYPE: RegTarget

RETURNS	DESCRIPTION
Regressor	The regressor itself.

predict_many(X)

Predicts the target value for a batch of examples.

PARAMETER	DESCRIPTION
x	Input examples.

RETURNS	DESCRIPTION
List	Predicted target values.

predict_one(x)

Predicts the target value for a single example.

PARAMETER	DESCRIPTION
x	Input example. TYPE: dict

RETURNS	DESCRIPTION
RegTarget	Predicted target value.

RegressorInitialized(module, loss_fn, optimizer_fn, lr=0.001, output_is_logit=True, is_feature_incremental=False, device='cpu', seed=42, **kwargs)

Bases: DeepEstimatorInitialized, MiniBatchRegressor

Wrapper for PyTorch classification models that supports feature and class incremental learning.

PARAMETER	DESCRIPTION
module	A PyTorch model. Can be pre-initialized or uninitialized. TYPE: Module
loss_fn	Loss function for training. Can be a string ('mse', 'cross_entropy', etc.) or a PyTorch function. TYPE: Union[str, Callable]
optimizer_fn	Optimizer for training (e.g., "adam", "sgd", or a PyTorch optimizer class). TYPE: Union[str, Type[Optimizer]]
lr	Learning rate of the optimizer. TYPE: float DEFAULT: 0.001
output_is_logit	If True, applies softmax/sigmoid during inference. TYPE: bool DEFAULT: True
is_class_incremental	If True, adds neurons when new classes appear. TYPE: bool DEFAULT: False
is_feature_incremental	If True, adds neurons when new features appear. TYPE: bool DEFAULT: False
device	Whether to use "cpu" or "cuda". TYPE: str DEFAULT: "cpu"
seed	Random seed for reproducibility. TYPE: Optional[int] DEFAULT: None
**kwargs	Additional parameters for model initialization. DEFAULT: {}

predict_many(X)

Predicts probabilities for multiple examples.

predict_one(x)

Predicts the target value for a single example.

PARAMETER	DESCRIPTION
x	Input example. TYPE: dict

RETURNS	DESCRIPTION
RegTarget	Predicted target value.