PyTorch2RiverClassifier¶
A river classifier that integrates neural Networks from PyTorch.
Parameters¶
-
build_fn
-
loss_fn (Type[torch.nn.modules.loss._Loss])
-
optimizer_fn (Type[torch.optim.optimizer.Optimizer]) – defaults to
<class 'torch.optim.adam.Adam'>
-
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
>>> from torch import manual_seed
>>> _ = manual_seed(0)
>>> def build_torch_mlp_classifier(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),
... nn.Sigmoid()
... )
... return net
...
>>> model = compat.PyTorch2RiverClassifier(
... build_fn= build_torch_mlp_classifier,
... loss_fn=nn.BCELoss,
... optimizer_fn=optim.Adam,
... learning_rate=1e-3
... )
>>> dataset = datasets.Phishing()
>>> metric = metrics.Accuracy()
>>> evaluate.progressive_val_score(dataset=dataset, model=model, metric=metric)
Accuracy: 74.38%
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_one
Update the model with a set of features x
and a label y
.
Parameters
- x (dict)
- y (Union[bool, str, int])
- kwargs
Returns
Classifier: self
predict_one
Predict the label of a set of features x
.
Parameters
- x (dict)
Returns
typing.Union[bool, str, int]: The predicted label.
predict_proba_many
predict_proba_one
Predict the probability of each label for a dictionary of features x
.
Parameters
- x (dict)
Returns
typing.Dict[typing.Union[bool, str, int], float]: A dictionary that associates a probability which each label.