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alibi.prototypes.protoselect

Constants

DEFAULT_DATA_PROTOSELECT

DEFAULT_DATA_PROTOSELECT: dict = {'prototypes': None, 'prototype_indices': None, 'prototype_labels': None}

DEFAULT_META_PROTOSELECT

DEFAULT_META_PROTOSELECT: dict = {'name': None, 'type': ['data'], 'explanation': ['global'], 'params': {}, 've...

logger

logger: logging.Logger = <Logger alibi.prototypes.protoselect (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.

ProtoSelect

Inherits from: Summariser, FitMixin, ABC, Base

Constructor

ProtoSelect(self, kernel_distance: Callable[[numpy.ndarray, numpy.ndarray], numpy.ndarray], eps: float, lambda_penalty: Optional[float] = None, batch_size: int = 10000000000, preprocess_fn: Optional[Callable[[Union[list, numpy.ndarray]], numpy.ndarray]] = None, verbose: bool = False)
Name
Type
Default
Description

kernel_distance

Callable[[.[<class 'numpy.ndarray'>, <class 'numpy.ndarray'>]], numpy.ndarray]

Kernel distance to be used. Expected to support computation in batches. Given an input x of size Nx x f1 x f2 x ... and an input y of size Ny x f1 x f2 x ..., the kernel distance should return a kernel matrix of size Nx x Ny.

eps

float

Epsilon ball size.

lambda_penalty

Optional[float]

None

Penalty for each prototype. Encourages a lower number of prototypes to be selected. Corresponds to :math:\lambda in the paper notation. If not specified, the default value is set to 1 / N where N is the size of the dataset to choose the prototype instances from, passed to the :py:meth:alibi.prototypes.protoselect.ProtoSelect.fit method.

batch_size

int

10000000000

Batch size to be used for kernel matrix computation.

preprocess_fn

Optional[Callable[[.[typing.Union[list, numpy.ndarray]]], numpy.ndarray]]

None

Preprocessing function used for kernel matrix computation. The preprocessing function takes the input as a list or a numpy array and transforms it into a numpy array which is then fed to the kernel_distance function. The use of preprocess_fn allows the method to be applied to any data modality.

verbose

bool

False

Whether to display progression bar while computing prototype points.

Methods

fit

fit(X: Union[list, numpy.ndarray], y: Optional[numpy.ndarray] = None, Z: Union[list, numpy.ndarray, None] = None) -> alibi.prototypes.protoselect.ProtoSelect
Name
Type
Default
Description

X

Union[list, numpy.ndarray]

Dataset to be summarised.

y

Optional[numpy.ndarray]

None

Labels of the dataset X to be summarised. The labels are expected to be represented as integers [0, 1, ..., L-1], where L is the number of classes in the dataset X.

Z

Union[list, numpy.ndarray, None]

None

Optional dataset to choose the prototypes from. If Z=None, the prototypes will be selected from the dataset X. Otherwise, if Z is provided, the dataset to be summarised is still X, but it is summarised by prototypes belonging to the dataset Z.

Returns

  • Type: alibi.prototypes.protoselect.ProtoSelect

summarise

summarise(num_prototypes: int = 1) -> alibi.api.interfaces.Explanation
Name
Type
Default
Description

num_prototypes

int

1

Maximum number of prototypes to be selected.

Returns

  • Type: alibi.api.interfaces.Explanation

Functions

compute_prototype_importances

compute_prototype_importances(summary: alibi.api.interfaces.Explanation, trainset: Tuple[numpy.ndarray, numpy.ndarray], preprocess_fn: Optional[Callable[[.[<class 'numpy.ndarray'>]], numpy.ndarray]] = None, knn_kw: Optional[dict] = None) -> Dict[str, Optional[numpy.ndarray]]

Computes the importance of each prototype. The importance of a prototype is the number of assigned training instances correctly classified according to the 1-KNN classifier (Bien and Tibshirani (2012): https://arxiv.org/abs/1202.5933).

Name
Type
Default
Description

summary

alibi.api.interfaces.Explanation

An Explanation object produced by a call to the :py:meth:alibi.prototypes.protoselect.ProtoSelect.summarise method.

trainset

Tuple[numpy.ndarray, numpy.ndarray]

Tuple, (X_train, y_train), consisting of the training data instances with the corresponding labels.

preprocess_fn

Optional[Callable[[.[<class 'numpy.ndarray'>]], numpy.ndarray]]

None

Optional preprocessor function. If preprocess_fn=None, no preprocessing is applied.

knn_kw

Optional[dict]

None

Keyword arguments passed to sklearn.neighbors.KNeighborsClassifier. The n_neighbors will be set automatically to 1, but the metric has to be specified according to the kernel distance used. If the metric is not specified, it will be set by default to 'euclidean'. See parameters description: https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html

Returns

  • Type: Dict[str, Optional[numpy.ndarray]]

cv_protoselect_euclidean

cv_protoselect_euclidean(trainset: Tuple[numpy.ndarray, numpy.ndarray], protoset: Optional[Tuple[numpy.ndarray]] = None, valset: Optional[Tuple[numpy.ndarray, numpy.ndarray]] = None, num_prototypes: int = 1, eps_grid: Optional[numpy.ndarray] = None, quantiles: Optional[Tuple[float, float]] = None, grid_size: int = 25, n_splits: int = 2, batch_size: int = 10000000000, preprocess_fn: Optional[Callable[[.[<class 'numpy.ndarray'>]], numpy.ndarray]] = None, protoselect_kw: Optional[dict] = None, knn_kw: Optional[dict] = None, kfold_kw: Optional[dict] = None) -> dict

Cross-validation parameter selection for ProtoSelect with Euclidean distance. The method computes the best epsilon radius.

Name
Type
Default
Description

trainset

Tuple[numpy.ndarray, numpy.ndarray]

Tuple, (X_train, y_train), consisting of the training data instances with the corresponding labels.

protoset

Optional[Tuple[numpy.ndarray]]

None

Tuple, (Z, ), consisting of the dataset to choose the prototypes from. If Z is not provided (i.e., protoset=None), the prototypes will be selected from the training dataset X. Otherwise, if Z is provided, the dataset to be summarised is still X, but it is summarised by prototypes belonging to the dataset Z. Note that the argument is passed as a tuple with a single element for consistency reasons.

valset

Optional[Tuple[numpy.ndarray, numpy.ndarray]]

None

Optional tuple (X_val, y_val) consisting of validation data instances with the corresponding validation labels. 1-KNN classifier is evaluated on the validation dataset to obtain the best epsilon radius. In case valset=None, then n-splits cross-validation is performed on the trainset.

num_prototypes

int

1

The number of prototypes to be selected.

eps_grid

Optional[numpy.ndarray]

None

Optional grid of values to select the epsilon radius from. If not specified, the search grid is automatically proposed based on the inter-distances between X and Z. The distances are filtered by considering only values in between the quantiles values. The minimum and maximum distance values are used to define the range of values to search the epsilon radius. The interval is discretized in grid_size equidistant bins.

quantiles

Optional[Tuple[float, float]]

None

Quantiles, (q_min, q_max), to be used to filter the range of values of the epsilon radius. The expected quantile values are in [0, 1] and clipped to [0, 1] if outside the range. See eps_grid for usage. If not specified, no filtering is applied. Only used if eps_grid=None.

grid_size

int

25

The number of equidistant bins to be used to discretize the eps_grid automatically proposed interval. Only used if eps_grid=None.

n_splits

int

2

batch_size

int

10000000000

Batch size to be used for kernel matrix computation.

preprocess_fn

Optional[Callable[[.[<class 'numpy.ndarray'>]], numpy.ndarray]]

None

Preprocessing function to be applied to the data instance before applying the kernel.

protoselect_kw

Optional[dict]

None

Keyword arguments passed to :py:meth:alibi.prototypes.protoselect.ProtoSelect.__init__.

knn_kw

Optional[dict]

None

Keyword arguments passed to sklearn.neighbors.KNeighborsClassifier. The n_neighbors will be set automatically to 1 and the metric will be set to 'euclidean. See parameters description: https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html

kfold_kw

Optional[dict]

None

Keyword arguments passed to sklearn.model_selection.KFold. See parameters description: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html

Returns

  • Type: dict

visualize_image_prototypes

visualize_image_prototypes(summary: alibi.api.interfaces.Explanation, trainset: Tuple[numpy.ndarray, numpy.ndarray], reducer: Callable[[.[<class 'numpy.ndarray'>]], numpy.ndarray], preprocess_fn: Optional[Callable[[.[<class 'numpy.ndarray'>]], numpy.ndarray]] = None, knn_kw: Optional[dict] = None, ax: Optional[matplotlib.axes._axes.Axes] = None, fig_kw: Optional[dict] = None, image_size: Tuple[int, int] = (28, 28), zoom_lb: float = 1.0, zoom_ub: float = 3.0) -> matplotlib.axes._axes.Axes

Plot the images of the prototypes at the location given by the reducer representation. The size of each prototype is proportional to the logarithm of the number of assigned training instances correctly classified according to the 1-KNN classifier (Bien and Tibshirani (2012): https://arxiv.org/abs/1202.5933).

Name
Type
Default
Description

summary

alibi.api.interfaces.Explanation

An Explanation object produced by a call to the :py:meth:alibi.prototypes.protoselect.ProtoSelect.summarise method.

trainset

Tuple[numpy.ndarray, numpy.ndarray]

Tuple, (X_train, y_train), consisting of the training data instances with the corresponding labels.

reducer

Callable[[.[<class 'numpy.ndarray'>]], numpy.ndarray]

2D reducer. Reduces the input feature representation to 2D. Note that the reducer operates directly on the input instances if preprocess_fn=None. If the preprocess_fn is specified, the reducer will be called on the feature representation obtained after passing the input instances through the preprocess_fn.

preprocess_fn

Optional[Callable[[.[<class 'numpy.ndarray'>]], numpy.ndarray]]

None

Optional preprocessor function. If preprocess_fn=None, no preprocessing is applied.

knn_kw

Optional[dict]

None

Keyword arguments passed to sklearn.neighbors.KNeighborsClassifier. The n_neighbors will be set automatically to 1, but the metric has to be specified according to the kernel distance used. If the metric is not specified, it will be set by default to 'euclidean'. See parameters description: https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html

ax

Optional[matplotlib.axes._axes.Axes]

None

A matplotlib axes object to plot on.

fig_kw

Optional[dict]

None

Keyword arguments passed to the fig.set function.

image_size

Tuple[int, int]

(28, 28)

Shape to which the prototype images will be resized. A zoom of 1 will display the image having the shape image_size.

zoom_lb

float

1.0

Zoom lower bound. The zoom will be scaled linearly between [zoom_lb, zoom_ub].

zoom_ub

float

3.0

Zoom upper bound. The zoom will be scaled linearly between [zoom_lb, zoom_ub].

Returns

  • Type: matplotlib.axes._axes.Axes

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