pysad.models.PerfectModel¶

class pysad.models.PerfectModel[source]¶

This model directly outputs the ground truth labels. This method is added for testing and pipelining purposes.

Methods

`__init__`()
`fit`(X[, y])	Fits the model to all instances in order.
`fit_partial`(X, y)	Fits the model to the ground truth label.
`fit_score`(X[, y])	This helper method applies fit_score_partial to all instances in order.
`fit_score_partial`(X[, y])	Applies fit_partial and score_partial to the next instance, respectively.
`score`(X)	Scores all instaces via score_partial iteratively.
`score_partial`(X)	Pops the score from the self.label queue.

fit(X, y=None)¶

Fits the model to all instances in order.

Parameters:

X (np.float64 array of shape (num_instances, num_features)) – The instances in order to fit.
y (int) – The labels of the instances in order to fit (Optional for unsupervised models, default=None).

Returns:

Fitted model.

Return type:

object

fit_partial(X, y)[source]¶

Fits the model to the ground truth label. Adds the label to the self.label queue.

Parameters:

Returns:

Returns the self.

Return type:

object

fit_score(X, y=None)¶

This helper method applies fit_score_partial to all instances in order.

Parameters:

X (np.float64 array of shape (num_instances, num_features)) – The instances in order to fit.
y (np.int32 array of shape (num_instances, )) – The labels of the instances in order to fit (Optional for unsupervised models, default=None).

Returns:

The anomalousness scores of the instances in order.

Return type:

np.float64 array of shape (num_instances,)

fit_score_partial(X, y=None)¶

Applies fit_partial and score_partial to the next instance, respectively.

Parameters:

X (np.float64 array of shape (num_features,)) – The instance to fit and score.
y (int) – The label of the instance (Optional for unsupervised models, default=None).

Returns:

The anomalousness score of the input instance.

Return type:

float

score(X)¶

Scores all instaces via score_partial iteratively.

Parameters:: X (np.float64 array of shape (num_instances, num_features)) – The instances in order to score.
Returns:: The anomalousness scores of the instances in order.
Return type:: np.float64 array of shape (num_instances,)

score_partial(X)[source]¶

Pops the score from the self.label queue.