rolling_regressor

RollingRegressor(module, loss_fn='mse', optimizer_fn='sgd', lr=0.001, is_feature_incremental=False, window_size=10, append_predict=False, device='cpu', seed=42, **kwargs)

Bases: RollingDeepEstimator, Regressor

Wrapper that feeds a sliding window of the most recent examples to the wrapped PyTorch regression model.

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
is_feature_incremental	Whether the model should adapt to the appearance of previously features by adding units to the input layer of the network. TYPE: bool DEFAULT: False
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
window_size	Number of recent examples to be fed to the wrapped model at each step. TYPE: int DEFAULT: 10
append_predict	Whether to append inputs passed for prediction to the rolling window. TYPE: bool DEFAULT: False
**kwargs	Parameters to be passed to the Module or the optimizer. DEFAULT: {}

learn_one(x, y, **kwargs)

Performs one step of training with the sliding window of the most recent examples.

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

RETURNS	DESCRIPTION
RollingRegressor	The regressor itself.

predict_one(x)

Predicts the target value for the current sliding window of most recent examples.

PARAMETER	DESCRIPTION
x	Input example. TYPE: dict

RETURNS	DESCRIPTION
RegTarget	Predicted target value.