`wildboar.explain`#

Explanation methods for classifiers and regressors.

Subpackages#

wildboar.explain.counterfactual

Package Contents#

Classes#

`AmplitudeImportance`	Compute the importance of equi-probable amplitude intervals.
`FrequencyImportance`	Mixin class for all explainers in wildboar.
`IntervalImportance`	Interval importance for time series.
`ShapeletImportance`	Compute the importance of shapelets.

Functions#

plot_importances(importances[, ax, labels])

Plot the importances as a boxplot.

class wildboar.explain.AmplitudeImportance(scoring=None, n_intervals='sqrt', window=None, binning='normal', n_bins=4, n_repeat=1, random_state=None)[source]#

Compute the importance of equi-probable amplitude intervals.

The implementation uses transform.SAX to discretize the time series and then for each bin permute the samples along that bin.

fit_explain(estimator, x=None, y=None, **kwargs)[source]#

Fit and return the explanation.

Parameters:

estimatorEstimator: The estimator to explain.
xtime-series, optional: The input time series.
yarray-like of shape (n_samples, ), optional: The labels.
**kwargs: Optional extra arguments.

Returns:

ndarray: The explanation.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

plot(x=None, y=None, *, ax=None, n_samples=100, scoring=None, preprocess=True, k=None, show_bins=False, show_grid=True)[source]#

Plot the importances.

If x is given, the importances are plotted over the samples optionally labeling each sample using the supplied labels. If x is not give, the importances are plotted as one or more boxplots.

Parameters:

xarray-like of shape (n_samples, n_timesteps), optional

The samples.

yarray-like of shape (n_samples, ), optional

The labels.

axAxes, optional

Axes to plot. If ax is set, x is None and scoring is None, the number of axes must be the same as the number of scorers.

n_samplesint or float, optional

The number of samples to plot, set to None to plot all.

scoringstr, optional

The scoring to plot if multiple scorers were used when fitting.

preprocessbool, optional

Preprocess the time series to align with the bins, ignored if x is not None.

kint or float, optional

The number of top bins to plot, ignored if x is not None.

if int, the specified number of bins are shown
if float, a fraction of the number of bins are shown.

show_binsbool, optional

Annotate the plot with the index of the bin, ignored if x is not None.

show_gridbool, optional

Annotate the plot with the bin thresholds, ignored if x is not None.

Returns:

axAxis: The axis.
mappableScalarMappable, optional: Return the mappable used to plot the colorbar. Only returned if ax is not None and x is not None.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

class wildboar.explain.FrequencyImportance(window=1, scoring=None, n_repeat=1, random_state=None)[source]#

Mixin class for all explainers in wildboar.

fit_explain(estimator, x=None, y=None, **kwargs)[source]#

Fit and return the explanation.

Parameters:

estimatorEstimator: The estimator to explain.
xtime-series, optional: The input time series.
yarray-like of shape (n_samples, ), optional: The labels.
**kwargs: Optional extra arguments.

Returns:

ndarray: The explanation.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

plot(x=None, y=None, ax=None)[source]#

Plot the explanation.

Returns:

axAxes: The axes object

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

class wildboar.explain.IntervalImportance(*, scoring=None, n_repeat=5, n_intervals='sqrt', window=None, random_state=None)[source]#

Interval importance for time series.

Parameters:

scoringstr, list, dict or callable, optional

The scoring function. By default the estimators score function is used.

n_repeatint, optional

The number of repeated permutations.

n_intervalsstr, optional

The number of intervals.

if “sqrt”, the number of intervals is the square root of n_timestep.
if “log2”, the number of intervals is the log2 of n_timestep.
if int, exact number of intervals.

windowint, optional

The window size. If specicied, n_intervals is ignored and the number of intervals is computed such that each interval is (at least) of size window.

random_stateint or RandomState

If int, random_state is the seed used by the random number generator
If RandomState instance, random_state is the random number generator
If None, the random number generator is the RandomState instance used
by np.random.

Attributes:

importances_dict or Importance: The importance scores for each interval. If dict, one value per scoring function.
components_ndarray of shape (n_intervals, 2): The interval start and end positions.

fit_explain(estimator, x=None, y=None, **kwargs)[source]#

Fit and return the explanation.

Parameters:

estimatorEstimator: The estimator to explain.
xtime-series, optional: The input time series.
yarray-like of shape (n_samples, ), optional: The labels.
**kwargs: Optional extra arguments.

Returns:

ndarray: The explanation.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

plot(x=None, y=None, *, ax=None, scoring=None, k=None, n_samples=100, show_grid=True)[source]#

Plot the explanation.

Returns:

axAxes: The axes object

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

class wildboar.explain.ShapeletImportance(scoring=None, n_repeat=1, n_shapelets=10, min_shapelet_size=0.0, max_shapelet_size=1.0, metric='euclidean', metric_params=None, random_state=None)[source]#

Compute the importance of shapelets.

The importance is given by permuting time series sections with the minimum distance to shapelets.

Parameters:

scoringstr, list, dict or callable, optional

The scoring function. By default the estimators score function is used.

n_repeatint, optional

The number of repeated permutations.

n_shapeletsint, optional

The number of shapelets to sample for the explanation.

min_shapelet_sizefloat, optional

The minimum size of shapelets used for explanation.

max_shapelet_sizefloat, optional

The maximum size of shapelets used for explanation.

metricstr, optional

The metric.

metric_paramsstr, optional

The metric parameters.

random_stateint or RandomState, optional

Controls the random resampling of the original dataset.

If int, random_state is the seed used by the random number generator.
If numpy.random.RandomState instance, random_state is the random number generator.
If None, the random number generator is the numpy.random.RandomState instance used by numpy.random.

Attributes:

componentsndarray: The shapelets

fit_explain(estimator, x=None, y=None, **kwargs)[source]#

Fit and return the explanation.

Parameters:

estimatorEstimator: The estimator to explain.
xtime-series, optional: The input time series.
yarray-like of shape (n_samples, ), optional: The labels.
**kwargs: Optional extra arguments.

Returns:

ndarray: The explanation.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

plot(X=None, y=None, k=None, scoring=None, kernel_scale=0.25, ax=None)[source]#

Plot the explanation.

Returns:

axAxes: The axes object

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

wildboar.explain.plot_importances(importances, ax=None, labels=None)[source]#

Plot the importances as a boxplot.

Parameters:

importancesImportance or dict: The importances.
axAxes, optional: The axes to plot. If importances is dict, ax must contain at least len(importances) Axes objects.
labelsarray-like, optional: The labels for the importances.

Returns:

Axes: The plotted Axes.

wildboar.explain#

Subpackages#

Package Contents#

Classes#

Functions#

`wildboar.explain`#