multioutput

Classes:

Name	Description
MultiTargetRegressor	A Regressor that supports multiple targets.
MultiTargetRegressorInitialized

MultiTargetRegressor

MultiTargetRegressor(
    module: Type[Module],
    loss_fn: Union[str, Callable] = "mse",
    optimizer_fn: Union[str, Callable] = "sgd",
    lr: float = 0.001,
    device: str = "cpu",
    seed: int = 42,
    **kwargs
)

Bases: MultiTargetRegressor, DeepEstimator

A Regressor that supports multiple targets.

Parameters:

Name	Type	Description	Default
module	Type[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.	required
loss_fn	Union[str, Callable]	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'.	'mse'
optimizer_fn	Union[str, Callable]	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".	'sgd'
lr	float	Learning rate of the optimizer.	0.001
device	str	Device to run the wrapped model on. Can be "cpu" or "gpu".	'cpu'
seed	int	Random seed for the wrapped model.	42
**kwargs		Parameters to be passed to the Module or the optimizer.	{}

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): 34.31

Methods:

Name	Description
clone	Clones the estimator.
draw	Draws the wrapped model.
initialize_module	Parameters
predict_one	Predicts the target value for a single example.

Source code in deep_river/regression/multioutput.py

def __init__(
    self,
    module: Type[torch.nn.Module],
    loss_fn: Union[str, Callable] = "mse",
    optimizer_fn: Union[str, Callable] = "sgd",
    lr: float = 1e-3,
    device: str = "cpu",
    seed: int = 42,
    **kwargs,
):
    super().__init__(
        module=module,
        loss_fn=loss_fn,
        device=device,
        optimizer_fn=optimizer_fn,
        lr=lr,
        seed=seed,
        **kwargs,
    )
    self.observed_targets: OrderedDict[FeatureName, RegTarget] = OrderedDict()

clone

clone(
    new_params: dict[Any, Any] | None = None,
    include_attributes=False,
)

Clones the estimator.

Parameters:

Name	Type	Description	Default
new_params	dict[Any, Any] | None	New parameters to be passed to the cloned estimator.	None
include_attributes		If True, the attributes of the estimator will be copied to the cloned estimator. This is useful when the estimator is a transformer and the attributes are the learned parameters.	False

Returns:

Type	Description
DeepEstimator	The cloned estimator.

Source code in deep_river/base.py

def clone(
    self,
    new_params: dict[Any, Any] | None = None,
    include_attributes=False,
):
    """Clones the estimator.

    Parameters
    ----------
    new_params
        New parameters to be passed to the cloned estimator.
    include_attributes
        If True, the attributes of the estimator will be copied to the
        cloned estimator. This is useful when the estimator is a
        transformer and the attributes are the learned parameters.

    Returns
    -------
    DeepEstimator
        The cloned estimator.
    """
    new_params = new_params or {}
    new_params.update(self.kwargs)
    new_params.update(self._get_params())
    new_params.update({"module": self.module_cls})

    clone = self.__class__(**new_params)
    if include_attributes:
        clone.__dict__.update(self.__dict__)
    return clone

draw

draw() -> Digraph

Draws the wrapped model.

Source code in deep_river/base.py

def draw(self) -> Digraph:
    """Draws the wrapped model."""
    first_parameter = next(self.module.parameters())
    input_shape = first_parameter.size()
    y_pred = self.module(torch.rand(input_shape))
    return make_dot(y_pred.mean(), params=dict(self.module.named_parameters()))

initialize_module

initialize_module(x: dict | DataFrame, **kwargs)

Parameters:

Name	Type	Description	Default
module		The instance or class or callable to be initialized, e.g. self.module.	required
kwargs	dict	The keyword arguments to initialize the instance or class. Can be an empty dict.	{}

Returns:

Type	Description
instance	The initialized component.

Source code in deep_river/base.py

def initialize_module(self, x: dict | pd.DataFrame, **kwargs):
    """
    Parameters
    ----------
    module
      The instance or class or callable to be initialized, e.g.
      ``self.module``.
    kwargs : dict
      The keyword arguments to initialize the instance or class. Can be an
      empty dict.
    Returns
    -------
    instance
      The initialized component.
    """
    torch.manual_seed(self.seed)
    if isinstance(x, Dict):
        n_features = len(x)
    elif isinstance(x, pd.DataFrame):
        n_features = len(x.columns)

    if not isinstance(self.module_cls, torch.nn.Module):
        self.module = self.module_cls(
            n_features=n_features,
            **self._filter_kwargs(self.module_cls, kwargs),
        )

    self.module.to(self.device)
    self.optimizer = self.optimizer_func(self.module.parameters(), lr=self.lr)
    self.module_initialized = True

    self._get_input_output_layers(n_features=n_features)

predict_one

predict_one(x: dict) -> Dict[FeatureName, RegTarget]

Predicts the target value for a single example.

Parameters:

Name	Type	Description	Default
x	dict	Input example.	required

Returns:

Type	Description
RegTarget	Predicted target value.

Source code in deep_river/regression/multioutput.py

def predict_one(self, x: dict) -> typing.Dict[FeatureName, RegTarget]:
    """
    Predicts the target value for a single example.

    Parameters
    ----------
    x
        Input example.

    Returns
    -------
    RegTarget
        Predicted target value.
    """
    if not self.module_initialized:
        self._update_observed_features(x)
        self.initialize_module(x=x, **self.kwargs)
    x_t = dict2tensor(x, SortedSet(x.keys()), device=self.device)
    self.module.eval()
    with torch.inference_mode():
        y_pred_t = self.module(x_t).squeeze().tolist()
        y_pred = {t: y_pred_t[i] for i, t in enumerate(self.observed_targets)}
    return y_pred

MultiTargetRegressorInitialized

MultiTargetRegressorInitialized(
    module: Module,
    loss_fn: Union[str, Callable] = "mse",
    optimizer_fn: Union[str, Callable] = "sgd",
    is_feature_incremental: bool = False,
    is_target_incremental: bool = False,
    lr: float = 0.001,
    device: str = "cpu",
    seed: int = 42,
    **kwargs
)

Bases: MultiTargetRegressor, DeepEstimatorInitialized

Methods:

Name	Description
learn_many	Learns from a batch of examples.
learn_one	Learns from a single example.
predict_one	Predicts the target value for a single example.

Source code in deep_river/regression/multioutput.py

def __init__(
    self,
    module: torch.nn.Module,
    loss_fn: Union[str, Callable] = "mse",
    optimizer_fn: Union[str, Callable] = "sgd",
    is_feature_incremental: bool = False,
    is_target_incremental: bool = False,
    lr: float = 1e-3,
    device: str = "cpu",
    seed: int = 42,
    **kwargs,
):
    super().__init__(
        module=module,
        loss_fn=loss_fn,
        optimizer_fn=optimizer_fn,
        lr=lr,
        device=device,
        seed=seed,
        is_feature_incremental=is_feature_incremental,
        **kwargs,
    )
    self.is_target_incremental = is_target_incremental
    self.observed_targets: SortedSet = SortedSet()

learn_many

learn_many(X: DataFrame, y: Series) -> None

Learns from a batch of examples.

Source code in deep_river/regression/multioutput.py

def learn_many(self, X: pd.DataFrame, y: pd.Series) -> None:
    """Learns from a batch of examples."""
    self._update_observed_features(X)

    x_t = self._df2tensor(X)
    self._learn(x_t, y)

learn_one

learn_one(
    x: dict, y: dict[FeatureName, RegTarget], **kwargs
) -> None

Learns from a single example.

Source code in deep_river/regression/multioutput.py

def learn_one(self, x: dict, y: dict[FeatureName, RegTarget], **kwargs) -> None:
    """Learns from a single example."""
    self._update_observed_features(x)
    self._update_observed_targets(y)
    x_t = self._dict2tensor(x)
    self._learn(x_t, y)

predict_one

predict_one(x: dict) -> Dict[FeatureName, RegTarget]

Predicts the target value for a single example.

Source code in deep_river/regression/multioutput.py

def predict_one(self, x: dict) -> typing.Dict[FeatureName, RegTarget]:
    """
    Predicts the target value for a single example.
    """

    self._update_observed_features(x)
    x_t = self._dict2tensor(x)
    self.module.eval()
    with torch.inference_mode():
        y_pred_t = self.module(x_t).squeeze().tolist()
        y_pred = {t: y_pred_t[i] for i, t in enumerate(self.observed_targets)}
    return y_pred