BaseClassifier

Abstract base class for classification algorithms.

Module: aisp.base
Import: from aisp.base import BaseClassifier

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Overview

This class defines the core interface for classification models. It enforces the implementation of the fit and predict methods in all derived classes, and provides a default implementation of the score method.

Use cases:

• Abstract base class for extending classification algorithm classes.

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Attributes

Name	Type	Default	Description
classes	Optional[npt.NDArray]	None	Class labels identified during training.

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Abstract Methods

fit

@abstractmethod
def fit(
    self,
    X: Union[npt.NDArray, list],
    y: Union[npt.NDArray, list],
    verbose: bool = True
) -> BaseClassifier:
    ...

Train the model using the input data X and corresponding labels y.
This abstract method is implemented by the class that inherits it.

Parameters

Name	Type	Default	Description
X	Union[npt.NDArray, list]	-	Training input samples. Each row corresponds to a samples and column to feature.
y	Union[npt.NDArray, list]	-	Target vector of shape (n_samples,). Must contain the same number of samples as X.
verbose	bool	True	If True, prints training progress information.

Returns

Type	Description
Self	Returns the instance of the class that implements this method.

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predict

@abstractmethod
def predict(self, X: Union[npt.NDArray, list]) -> npt.NDArray:
    ...

Generate predictions based on the input data X.
This abstract method is implemented by the class that inherits it.

Parameters

Name	Type	Default	Description
X	Union[npt.NDArray, list]	-	Input samples. Must have the same number of features used during training.

Returns

Type	Description
npt.NDArray	Predicted values for each input sample.

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Public Methods

score

def score(
    self,
    X: Union[npt.NDArray, list],
    y: Union[npt.NDArray, list]
) -> float:
    ...

Score function calculates forecast accuracy.

This function performs the prediction of X and checks how many elements are equal between vector y and y_predicted. This function was added for compatibility with some scikit-learn functions.

Parameters

Name	Type	Default	Description
X	Union[npt.NDArray, list]	-	Feature set with shape (n_samples, n_features).
y	Union[npt.NDArray, list]	-	True values with shape (n_samples,).

Returns

Type	Description
float	The accuracy of the model.