hyperimpute.plugins.prediction.classifiers package

class ClassifierPlugin(random_state: int = 0, **kwargs: Any)

Bases: ClassifierMixin, BaseEstimator, PredictionPlugin

Base class for the classifier plugins.

It provides the implementation for plugin.Plugin’s subtype, _fit and _predict methods.

Each derived class must implement the following methods(inherited from plugin.Plugin):

name() - a static method that returns the name of the plugin. hyperparameter_space() - a static method that returns the hyperparameters that can be tuned during the optimization. The method will return a list of Params derived objects.

If any method implementation is missing, the class constructor will fail.

_abc_impl = <_abc_data object>
fit(X: DataFrame, *args: Any, **kwargs: Any) → Plugin
get_args() → dict
module_relative_path: Optional[Path]
score(X: DataFrame, y: DataFrame, metric: str = 'aucroc') → float

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – Test samples.

  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.

  • sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score – Mean accuracy of self.predict(X) wrt. y.

Return type:

float

static subtype() → str
class Classifiers

Bases: PluginLoader

Submodules