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PyTorch2RiverRegressor

Compatibility layer from PyTorch to River for regression.

Parameters

  • build_fn

  • loss_fn (Type[torch.nn.modules.loss._Loss])

  • optimizer_fn (Type[torch.optim.optimizer.Optimizer])

  • learning_rate – defaults to 0.001

  • net_params

Examples

>>> from river import compat
>>> from river import datasets
>>> from river import evaluate
>>> from river import metrics
>>> from river import preprocessing
>>> from torch import nn
>>> from torch import optim

>>> _ = torch.manual_seed(0)

>>> dataset = datasets.TrumpApproval()

>>> def build_torch_mlp_regressor(n_features):
...     net = nn.Sequential(
...         nn.Linear(n_features, 5),
...         nn.Linear(5, 5),
...         nn.Linear(5, 5),
...         nn.Linear(5, 5),
...         nn.Linear(5, 1)
...     )
...     return net
...
>>> model = compat.PyTorch2RiverRegressor(
...     build_fn= build_torch_mlp_regressor,
...     loss_fn=nn.MSELoss,
...     optimizer_fn=optim.Adam,
... )

>>> metric = metrics.MAE()

>>> metric = evaluate.progressive_val_score(dataset=dataset, model=model, metric=metric)
>>> round(metric.get(), 2)
78.98

Methods

clone

Return a fresh estimator with the same parameters.

The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.

learn_many

Update the model with a mini-batch of features X and boolean targets y.

Parameters

  • X (pandas.core.frame.DataFrame)
  • y (pandas.core.series.Series)
  • kwargs

Returns

self

learn_one

Update the model with a set of features x and a label y.

Parameters

  • x (dict)
  • y (Union[bool, str, int])

Returns

Regressor: self

predict_many

Predict the outcome for each given sample.

Parameters

  • X (pandas.core.frame.DataFrame)

Returns

Series: The predicted outcomes.

predict_one

Predicts the target value of a set of features x.

Parameters

  • x

Returns

The prediction.