rolling_classifier

`RollingClassifier(module, loss_fn='binary_cross_entropy', optimizer_fn='sgd', lr=0.001, output_is_logit=True, is_class_incremental=False, device='cpu', seed=42, window_size=10, append_predict=False, **kwargs)` ¶

Bases: Classifier, RollingDeepEstimator

Wrapper that feeds a sliding window of the most recent examples to the wrapped PyTorch classification model. The class also automatically handles increases in the number of classes by adding output neurons in case the number of observed classes exceeds the current number of output neurons.

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: `'binary_cross_entropy'`
`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`
`output_is_logit`	`Whether the module produces logits as output. If true, either softmax or sigmoid is applied to the outputs when predicting.` TYPE: `bool` DEFAULT: `True`
`is_class_incremental`	Whether the classifier should adapt to the appearance of previously unobserved classes by adding an unit to the output layer of the network. This works only if the last trainable layer is an nn.Linear layer. Note also, that output activation functions can not be adapted, meaning that a binary classifier with a sigmoid output can not be altered to perform multi-class predictions. 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 `build_fn` function aside from `n_features`. DEFAULT: `{}`

Examples:

>>> from deep_river.classification import RollingClassifier
>>> from river import metrics, datasets, compose, preprocessing
>>> import torch

>>> class MyModule(torch.nn.Module):
...
...    def __init__(self, n_features, hidden_size=1):
...        super().__init__()
...        self.n_features=n_features
...        self.hidden_size = hidden_size
...        self.lstm = torch.nn.LSTM(input_size=n_features,
...                                  hidden_size=hidden_size,
...                                  batch_first=False,
...                                  num_layers=1,
...                                  bias=False)
...        self.softmax = torch.nn.Softmax(dim=-1)
...
...    def forward(self, X, **kwargs):
...        output, (hn, cn) = self.lstm(X)
...        hn = hn.view(-1, self.lstm.hidden_size)
...        return self.softmax(hn)

>>> dataset = datasets.Keystroke()
>>> metric = metrics.Accuracy()
>>> optimizer_fn = torch.optim.SGD

>>> model_pipeline = preprocessing.StandardScaler()
>>> model_pipeline |= RollingClassifier(
...     module=MyModule,
...     loss_fn="binary_cross_entropy",
...     optimizer_fn=torch.optim.SGD,
...     window_size=20,
...     lr=1e-2,
...     append_predict=True,
...     is_class_incremental=True
... )

>>> for x, y in dataset.take(5000):
...     y_pred = model_pipeline.predict_one(x)  # make a prediction
...     metric = metric.update(y, y_pred)  # update the metric
...     model = model_pipeline.learn_one(x, y)  # make the model learn
>>> print(f'Accuracy: {metric.get()}')
Accuracy: 0.4552

`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
`Classifier`	The classifier 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: `ClfTarget`

RETURNS	DESCRIPTION
`Classifier`	The classifier itself.

`predict_proba_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_proba_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.

rolling_classifier