multioutput

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

Bases: MultiTargetRegressor, Regressor

A Regressor that supports multiple targets.

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 "gpu". TYPE: str DEFAULT: 'cpu'
seed	Random seed for the wrapped model. TYPE: int DEFAULT: 42
**kwargs	Parameters to be passed to the Module or the optimizer. DEFAULT: {}

Examples:

>>> from river import evaluate, compose
>>> from river import metrics
>>> from river import preprocessing
>>> from river import stream
>>> from sklearn import datasets
>>> from torch import nn
>>> from deep_river.regression.multioutput import MultiTargetRegressor

>>> class MyModule(nn.Module):
...     def __init__(self, n_features):
...         super(MyModule, self).__init__()
...         self.dense0 = nn.Linear(n_features,3)
...
...     def forward(self, X, **kwargs):
...         X = self.dense0(X)
...         return X

>>> dataset = stream.iter_sklearn_dataset(
...         dataset=datasets.load_linnerud(),
...         shuffle=True,
...         seed=42
...     )
>>> model = compose.Pipeline(
...     preprocessing.StandardScaler(),
...     MultiTargetRegressor(
...         module=MyModule,
...         loss_fn='mse',
...         lr=0.3,
...         optimizer_fn='sgd',
...     ))
>>> metric = metrics.multioutput.MicroAverage(metrics.MAE())
>>> ev = evaluate.progressive_val_score(dataset, model, metric)
>>> print(f"MicroAverage(MAE): {metric.get():.2f}")
MicroAverage(MAE): 28.36

predict_one(x)

Predicts the target value for a single example.

PARAMETER	DESCRIPTION
x	Input example. TYPE: dict

RETURNS	DESCRIPTION
RegTarget	Predicted target value.