skpro.baselines package

Submodules

skpro.baselines.density module

class skpro.baselines.density.DensityBaseline(adapter=None)[source]

Bases: skpro.base.ProbabilisticEstimator

class Distribution(estimator, X, selection=slice(None, None, None), mode='elementwise')[source]

Bases: skpro.base.Distribution

X

Reference of the test features that are ought to correspond with the predictive distribution represented by the interface.

The interface methods (e.g. pdf) can use X to construct and exhibit the predictive distribution properties of the interface (e.g. construct the predicted pdf based on X)

Note that X automatically reflects the feature point for which the interface is ought to represent the distributional prediction. For given M x n features, X will thus represent an 1 x n vector that provides the bases for the predicted distribution. However, if the vectorvalued() decorator is applied X will represent the full M x n matrix for an efficient vectorized implementation.

Getter:Returns the test features based on the current subset selection
Setter:Sets the data reference
Type:array
cdf(x, *args, **kwargs)
lp2
mean(*args, **kwargs)

Mean prediction

Returns:
Return type:The mean prediction that corresponds to self.X
pdf(x, *args, **kwargs)
point
ppf(q, *args, **kwargs)

Percent point function (inverse of cdf — percentiles).

Parameters:q –
Returns:
Return type:float
replicate(selection=None, mode=None)

Replicates the distribution object

Parameters:
  • selection (None | slice | int (optional)) – Subset point selection of the distribution copy
  • mode (str (optional)) – Interface mode (‘elementwise’ or ‘batch’)
Returns:
Return type:

skpro.base.ProbabilisticEstimator.Distribution
std
class ImplementsEnhancedInterface(name, bases, clsdict)

Bases: abc.ABCMeta

Meta-class for distribution interface

Enhances the distribution interface behind the scenes with automatic caching and syntactic sugar for element-wise access of the distributions

mro() → list

return a type’s method resolution order

register(subclass)

Register a virtual subclass of an ABC.

Returns the subclass, to allow usage as a class decorator.

fit(X, y)[source]

Fits the model

Parameters:
  • X (numpy array or sparse matrix of shape [n_samples,n_features]) – Training data
  • y (numpy array of shape [n_samples, n_targets]) – Target values. Will be cast to X’s dtype if necessary
Returns:

self
Return type:

returns an instance of self.
get_params(deep=True)

Get parameters for this estimator.

Parameters:deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:params – Parameter names mapped to their values.
Return type:mapping of string to any
name()
predict(X)

Predicts using the model

Parameters:X ({array-like, sparse matrix}, shape = (n_samples, n_features)) – Samples.
Returns:Returns predicted distributions
Return type:Distribution interface representing n_samples predictions
score(X, y, sample=True, return_std=False)

Returns the log-loss score

Parameters:
  • X (np.array) – Features
  • y (np.array) – Labels
  • sample (boolean, default=True) – If true, loss will be averaged across the sample
  • return_std (boolean, default=False) – If true, the standard deviation of the loss sample will be returned
Returns:

Log-loss score
Return type:

mixed
set_params(**params)

Set the parameters of this estimator.

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

Returns:
Return type:self

Module contents