heat.base

Provides mixins for high-level algorithms, e.g. classifiers or clustering algorithms.

Module Contents

class BaseEstimator

Abstract base class for all estimators, i.e. parametrized analysis algorithms, in Heat. Can be used as mixin.

_parameter_names() → List[str]

Get the names of all parameters that can be set inside the constructor of the estimator.

get_params(deep: bool = True) → Dict[str, object]

Get parameters for this estimator.

Parameters:

deep (bool, default: True) – If True, will return the parameters for this estimator and contained sub-objects that are estimators.

__repr__(indent: int = 1) → str

Returns a printable representation of the object.

Parameters:

indent (int, default: 1) – Indicates the indentation for the top-level output.

set_params(**params: Dict[str, object]) → BaseEstimator.set_params.self

Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The latter have to be nested dictionaries.

Parameters:

**params (dict[str, object]) – Estimator parameters to bet set.

class ClassificationMixin

Mixin for all classifiers in Heat.

fit(x: heat.core.dndarray.DNDarray, y: heat.core.dndarray.DNDarray)

Fits the classification model.

Parameters:

• x (DNDarray) – Training instances to train on. Shape = (n_samples, n_features)

• y (DNDarray) – Class values to fit. Shape = (n_samples, )

fit_predict(x: heat.core.dndarray.DNDarray, y: heat.core.dndarray.DNDarray) → heat.core.dndarray.DNDarray

Fits model and returns classes for each input sample Convenience method; equivalent to calling fit() followed by predict().

Parameters:

• x (DNDarray) – Input data to be predicted. Shape = (n_samples, n_features)

• y (DNDarray) – Class values to fit. Shape = (n_samples, )

predict(x: heat.core.dndarray.DNDarray) → heat.core.dndarray.DNDarray

Predicts the class labels for each sample.

Parameters:

x (DNDarray) – Values to predict the classes for. Shape = (n_samples, n_features)

class TransformMixin

Mixin for all transformations in Heat.

fit(x: heat.core.dndarray.DNDarray)

Fits the transformation model.

Parameters:

x (DNDarray) – Training instances to train on. Shape = (n_samples, n_features)

fit_transform(x: heat.core.dndarray.DNDarray) → heat.core.dndarray.DNDarray

Fits model and returns transformed data for each input sample Convenience method; equivalent to calling fit() followed by transform().

Parameters:

x (DNDarray) – Input data to be transformed. Shape = (n_samples, n_features)

transform(x: heat.core.dndarray.DNDarray) → heat.core.dndarray.DNDarray

Transforms the input data.

xDNDarray

Values to transform. Shape = (n_samples, n_features)

class ClusteringMixin

Clustering mixin for all clusterers in Heat.

fit(x: heat.core.dndarray.DNDarray)

Computes the clustering.

Parameters:

x (DNDarray) – Training instances to cluster. Shape = (n_samples, n_features)

fit_predict(x: heat.core.dndarray.DNDarray) → heat.core.dndarray.DNDarray

Compute clusters and returns the predicted cluster assignment for each sample. Returns index of the cluster each sample belongs to. Convenience method; equivalent to calling fit() followed by predict().

Parameters:

x (DNDarray) – Input data to be clustered. Shape = (n_samples, n_features)

class RegressionMixin

Mixin for all regression estimators in Heat.

fit(x: heat.core.dndarray.DNDarray, y: heat.core.dndarray.DNDarray)

Fits the regression model.

Parameters:

• x (DNDarray) – Training instances to train on. Shape = (n_samples, n_features)

• y (DNDarray) – Continuous values to fit. Shape = (n_samples,)

fit_predict(x: heat.core.dndarray.DNDarray, y: heat.core.dndarray.DNDarray) → heat.core.dndarray.DNDarray

Fits model and returns regression predictions for each input sample Convenience method; equivalent to calling fit() followed by predict().

Parameters:

• x (DNDarray) – Input data to be predicted. Shape = (n_samples, n_features)

• y (DNDarray) – Continuous values to fit. Shape = (n_samples,)

predict(x: heat.core.dndarray.DNDarray) → heat.core.dndarray.DNDarray

Predicts the continuous labels for each sample.

Parameters:

x (DNDarray) – Values to let the model predict. Shape = (n_samples, n_features)

is_classifier(estimator: object) → bool

Return True if the given estimator is a classifier, False otherwise.

Parameters:

estimator (object) – Estimator object to test.

is_transformer(estimator: object) → bool

Return True if the given estimator is a transformer, False otherwise.

Parameters:

estimator (object) – Estimator object to test.

is_estimator(estimator: object) → bool

Return True if the given estimator is an estimator, False otherwise.

Parameters:

estimator (object) – Estimator object to test.

is_clusterer(estimator: object) → bool

Return True if the given estimator is a clusterer, False otherwise.

Parameters:

estimator (object) – Estimator object to test.

is_regressor(estimator: object) → bool

Return True if the given estimator is a regressor, False otherwise.

Parameters:

estimator (object) – Estimator object to test.