alibi.confidence.model_linearity

Constants

logger

logger: logging.Logger = <Logger alibi.confidence.model_linearity (WARNING)>

Instances of the Logger class represent a single logging channel. A "logging channel" indicates an area of an application. Exactly how an "area" is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of "input processing" might include sub-areas "read CSV files", "read XLS files" and "read Gnumeric files"). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be "input" for the upper level, and "input.csv", "input.xls" and "input.gnu" for the sub-levels. There is no arbitrary limit to the depth of nesting.

LinearityMeasure

Constructor

LinearityMeasure(self, method: str = 'grid', epsilon: float = 0.04, nb_samples: int = 10, res: int = 100, alphas: Optional[numpy.ndarray] = None, model_type: str = 'classifier', agg: str = 'pairwise', verbose: bool = False) -> None

Name	Type	Default	Description
method	str	'grid'	Method for sampling. Supported methods: 'knn'
epsilon	float	0.04	Size of the sampling region around the central instance as a percentage of the features range.
nb_samples	int	10	Number of samples to generate.
res	int	100	Resolution of the grid. Number of intervals in which the feature range is discretized.
alphas	Optional[numpy.ndarray]	None	Coefficients in the superposition.
model_type	str	'classifier'	Type of task. Supported values: 'regressor'
agg	str	'pairwise'	Aggregation method. Supported values: 'global'
verbose	bool	False

Methods

fit

fit(X_train: numpy.ndarray) -> None

Name	Type	Default	Description
X_train	numpy.ndarray		Training set.

Returns

• Type: None

score

score(predict_fn: Callable, x: numpy.ndarray) -> numpy.ndarray

Name	Type	Default	Description
predict_fn	Callable		Prediction function.
x	numpy.ndarray		Instance of interest.

Returns

• Type: numpy.ndarray

Functions

infer_feature_range

infer_feature_range(X_train: numpy.ndarray) -> numpy.ndarray

Infers the feature range from the training set.

Name	Type	Default	Description
X_train	numpy.ndarray		Training set.

Returns

• Type: numpy.ndarray

linearity_measure

linearity_measure(predict_fn: Callable, x: numpy.ndarray, feature_range: Union[List[Any], numpy.ndarray, None] = None, method: str = 'grid', X_train: Optional[numpy.ndarray] = None, epsilon: float = 0.04, nb_samples: int = 10, res: int = 100, alphas: Optional[numpy.ndarray] = None, agg: str = 'global', model_type: str = 'classifier') -> numpy.ndarray

Calculate the linearity measure of the model around an instance of interest x.

Name	Type	Default	Description
predict_fn	Callable		Predict function.
x	numpy.ndarray		Instance of interest.
feature_range	Union[List[Any], numpy.ndarray, None]	None	Array with min and max values for each feature.
method	str	'grid'	Method for sampling. Supported values: 'knn'
X_train	Optional[numpy.ndarray]	None	Training set.
epsilon	float	0.04	Size of the sampling region as a percentage of the feature range.
nb_samples	int	10	Number of samples to generate.
res	int	100	Resolution of the grid. Number of intervals in which the features range is discretized.
alphas	Optional[numpy.ndarray]	None	Coefficients in the superposition.
agg	str	'global'	Aggregation method. Supported values: 'global'
model_type	str	'classifier'	Type of task. Supported values: 'regressor'

Returns

• Type: numpy.ndarray