probability_weighted_ae

Classes:

Name	Description
ProbabilityWeightedAutoencoder	Wrapper for PyTorch autoencoder models for anomaly detection that
ProbabilityWeightedAutoencoderInitialized

ProbabilityWeightedAutoencoder

ProbabilityWeightedAutoencoder(
    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,
    skip_threshold: float = 0.9,
    window_size=250,
    **kwargs
)

Bases: Autoencoder

Wrapper for PyTorch autoencoder models for anomaly detection that reduces the employed learning rate based on an outlier probability estimate of the input example as well as a threshold probability skip_threshold. If the outlier probability is above the threshold, the learning rate is reduced to less than 0. Given the probability estimate \(p_out\), the adjusted learning rate \(lr_adj\) is \(lr * 1 - (rac{p_out}{skip_threshold})\).

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	Base learning rate of the optimizer.	0.001
skip_threshold	float	Threshold probability to use as a reference for the reduction of the base learning rate.	0.9
device	str	Device to run the wrapped model on. Can be "cpu" or "cuda".	'cpu'
seed	int	Random seed to be used for training the wrapped model.	42
**kwargs		Parameters to be passed to the module function aside from n_features. Examples	{}
Rolling			required

Methods:

Name	Description
clone	Clones the estimator.
draw	Draws the wrapped model.
initialize_module	Parameters
learn_one	Performs one step of training with a single example,
score_many	Returns an anomaly score for the provided batch of examples in
score_one	Returns an anomaly score for the provided example in the form of

Source code in deep_river/anomaly/probability_weighted_ae.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,
    skip_threshold: float = 0.9,
    window_size=250,
    **kwargs,
):
    super().__init__(
        module=module,
        loss_fn=loss_fn,
        optimizer_fn=optimizer_fn,
        lr=lr,
        device=device,
        seed=seed,
        **kwargs,
    )
    self.window_size = window_size
    self.skip_threshold = skip_threshold
    self.rolling_mean = utils.Rolling(stats.Mean(), window_size=window_size)
    self.rolling_var = utils.Rolling(stats.Var(), window_size=window_size)

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)

learn_one

learn_one(x: dict, y: Any = None, **kwargs) -> None

Performs one step of training with a single example, scaling the employed learning rate based on the outlier probability estimate of the input example.

Parameters:

Name	Type	Description	Default
**kwargs			{}
x	dict	Input example.	required

Returns:

Type	Description
ProbabilityWeightedAutoencoder	The autoencoder itself.

Source code in deep_river/anomaly/probability_weighted_ae.py

def learn_one(self, x: dict, y: Any = None, **kwargs) -> None:
    """
    Performs one step of training with a single example,
    scaling the employed learning rate based on the outlier
    probability estimate of the input example.

    Parameters
    ----------
    **kwargs
    x
        Input example.

    Returns
    -------
    ProbabilityWeightedAutoencoder
        The autoencoder itself.
    """
    if not self.module_initialized:
        self._update_observed_features(x)
        self.initialize_module(x=x, **self.kwargs)

    self._adapt_input_dim(x)
    x_t = dict2tensor(x, self.observed_features, device=self.device)

    self.module.train()
    x_pred = self.module(x_t)
    loss = self.loss_func(x_pred, x_t)
    self._apply_loss(loss)

score_many

score_many(X: DataFrame) -> ndarray

Returns an anomaly score for the provided batch of examples in the form of the autoencoder's reconstruction error.

Parameters:

Name	Type	Description	Default
x		Input batch of examples.	required

Returns:

Type	Description
float	Anomaly scores for the given batch of examples. Larger values indicate more anomalous examples.

Source code in deep_river/anomaly/ae.py

def score_many(self, X: pd.DataFrame) -> np.ndarray:
    """
    Returns an anomaly score for the provided batch of examples in
    the form of the autoencoder's reconstruction error.

    Parameters
    ----------
    x
        Input batch of examples.

    Returns
    -------
    float
        Anomaly scores for the given batch of examples. Larger values
        indicate more anomalous examples.
    """
    if not self.module_initialized:
        self._update_observed_features(X)
        self.initialize_module(x=X, **self.kwargs)

    self._adapt_input_dim(X)
    X_t = df2tensor(X, features=self.observed_features, device=self.device)

    self.module.eval()
    with torch.inference_mode():
        X_pred = self.module(X_t)
    loss = torch.mean(
        self.loss_func(X_pred, X_t, reduction="none"),
        dim=list(range(1, X_t.dim())),
    )
    score = loss.cpu().detach().numpy()
    return score

score_one

score_one(x: dict) -> float

Returns an anomaly score for the provided example in the form of the autoencoder's reconstruction error.

Parameters:

Name	Type	Description	Default
x	dict	Input example.	required

Returns:

Type	Description
float	Anomaly score for the given example. Larger values indicate more anomalous examples.

Source code in deep_river/anomaly/ae.py

def score_one(self, x: dict) -> float:
    """
    Returns an anomaly score for the provided example in the form of
    the autoencoder's reconstruction error.

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

    Returns
    -------
    float
        Anomaly score for the given example. Larger values indicate
        more anomalous examples.

    """

    if not self.module_initialized:
        self._update_observed_features(x)
        self.initialize_module(x=x, **self.kwargs)

    self._adapt_input_dim(x)

    x_t = dict2tensor(x, features=self.observed_features, device=self.device)
    self.module.eval()
    with torch.inference_mode():
        x_pred = self.module(x_t)
    loss = self.loss_func(x_pred, x_t).item()
    return loss

ProbabilityWeightedAutoencoderInitialized

ProbabilityWeightedAutoencoderInitialized(
    module: Module,
    loss_fn: Union[str, Callable] = "mse",
    optimizer_fn: Union[str, Callable] = "sgd",
    lr: float = 0.001,
    device: str = "cpu",
    seed: int = 42,
    skip_threshold: float = 0.9,
    window_size=250,
    **kwargs
)

Bases: AutoencoderInitialized

Methods:

Name	Description
learn_one	Performs one step of training with a single example,
score_many	Returns an anomaly score for the provided batch of examples in
score_one	Returns an anomaly score for the provided example in the form of

Source code in deep_river/anomaly/probability_weighted_ae.py

def __init__(
    self,
    module: 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,
    skip_threshold: float = 0.9,
    window_size=250,
    **kwargs,
):
    warnings.warn(
        "This is deprecated and will be removed in future releases. "
        "Please instead use the AutoencoderInitialized class and "
        "initialize the module beforehand"
    )
    super().__init__(
        module=module,
        loss_fn=loss_fn,
        optimizer_fn=optimizer_fn,
        lr=lr,
        device=device,
        seed=seed,
        **kwargs,
    )
    self.window_size = window_size
    self.skip_threshold = skip_threshold
    self.rolling_mean = utils.Rolling(stats.Mean(), window_size=window_size)
    self.rolling_var = utils.Rolling(stats.Var(), window_size=window_size)

learn_one

learn_one(x: dict, y: Any = None, **kwargs) -> None

Performs one step of training with a single example, scaling the employed learning rate based on the outlier probability estimate of the input example.

Parameters:

Name	Type	Description	Default
**kwargs			{}
x	dict	Input example.	required

Returns:

Type	Description
ProbabilityWeightedAutoencoder	The autoencoder itself.

Source code in deep_river/anomaly/probability_weighted_ae.py

def learn_one(self, x: dict, y: Any = None, **kwargs) -> None:
    """
    Performs one step of training with a single example,
    scaling the employed learning rate based on the outlier
    probability estimate of the input example.

    Parameters
    ----------
    **kwargs
    x
        Input example.

    Returns
    -------
    ProbabilityWeightedAutoencoder
        The autoencoder itself.
    """

    self._update_observed_features(x)
    x_t = self._dict2tensor(x)

    self.module.train()
    x_pred = self.module(x_t)
    loss = self.loss_func(x_pred, x_t)
    self._apply_loss(loss)

score_many

score_many(X: DataFrame) -> ndarray

Returns an anomaly score for the provided batch of examples in the form of the autoencoder's reconstruction error.

Parameters:

Name	Type	Description	Default
x		Input batch of examples.	required

Returns:

Type	Description
float	Anomaly scores for the given batch of examples. Larger values indicate more anomalous examples.

Source code in deep_river/anomaly/ae.py

def score_many(self, X: pd.DataFrame) -> np.ndarray:
    """
    Returns an anomaly score for the provided batch of examples in
    the form of the autoencoder's reconstruction error.

    Parameters
    ----------
    x
        Input batch of examples.

    Returns
    -------
    float
        Anomaly scores for the given batch of examples. Larger values
        indicate more anomalous examples.
    """
    self._update_observed_features(X)
    x_t = self._df2tensor(X)

    self.module.eval()
    with torch.inference_mode():
        x_pred = self.module(x_t)
    loss = torch.mean(
        self.loss_func(x_pred, x_t, reduction="none"),
        dim=list(range(1, x_t.dim())),
    )
    score = loss.cpu().detach().numpy()
    return score

score_one

score_one(x: dict) -> float

Returns an anomaly score for the provided example in the form of the autoencoder's reconstruction error.

Parameters:

Name	Type	Description	Default
x	dict	Input example.	required

Returns:

Type	Description
float	Anomaly score for the given example. Larger values indicate more anomalous examples.

Source code in deep_river/anomaly/ae.py

def score_one(self, x: dict) -> float:
    """
    Returns an anomaly score for the provided example in the form of
    the autoencoder's reconstruction error.

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

    Returns
    -------
    float
        Anomaly score for the given example. Larger values indicate
        more anomalous examples.

    """

    self._update_observed_features(x)
    x_t = self._dict2tensor(x)
    self.module.eval()
    with torch.inference_mode():
        x_pred = self.module(x_t)
    loss = self.loss_func(x_pred, x_t).item()
    return loss