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.

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

Bases: RollingDeepEstimatorInitialized, RegressorInitialized

RollingRegressorInitialized class built for regression tasks with a window-based learning mechanism. Handles incremental learning by maintaining a sliding window of training data for both individual examples and batches of data. Enables feature incremental updates and compatibility with PyTorch modules. Ideal for time-series or sequential data tasks where the training set changes dynamically.

ATTRIBUTE	DESCRIPTION
module	A PyTorch neural network model that defines the architecture of the regressor. TYPE: Module
loss_fn	Loss function used for optimization. Either a string (e.g., "mse") or a callable. TYPE: Union[str, Callable]
optimizer_fn	Optimizer function or string used for training the neural network model. TYPE: Union[str, Type[Optimizer]]
lr	Learning rate for the optimizer. TYPE: float
is_feature_incremental	Whether the model incrementally updates its features during training. TYPE: bool
device	Target device for model training and inference (e.g., "cpu", "cuda"). TYPE: str
seed	Random seed for reproducibility. TYPE: int
window_size	Size of the sliding window used for storing the most recent training examples. TYPE: int
append_predict	Whether predictions should contribute to the sliding window data. TYPE: bool

learn_many(X, y)

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

PARAMETER	DESCRIPTION
X	Input examples. TYPE: DataFrame
y	Target values. TYPE: Series

RETURNS	DESCRIPTION
Self	The regressor itself.

learn_one(x, y, **kwargs)

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

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

RETURNS	DESCRIPTION
Self	The regressor itself.

predict_many(X)

Predict the probability of each label given the most recent examples

PARAMETER	DESCRIPTION
X	TYPE: DataFrame

RETURNS	DESCRIPTION
DataFrame	DataFrame of probabilities for each label.

predict_one(x)

Predict the probability of each label given the most recent examples stored in the sliding window.

PARAMETER	DESCRIPTION
x	Input example. TYPE: dict

RETURNS	DESCRIPTION
Dict[ClfTarget, float]	Dictionary of probabilities for each label.