autoqild.automl.automl_core

Abstract base class for AutoML classifiers.

Classes

AutomlClassifier()

Abstract base class for AutoML classifiers.

class autoqild.automl.automl_core.AutomlClassifier

Bases: BaseEstimator, ClassifierMixin

Abstract base class for AutoML classifiers.

This class serves as a base for implementing various AutoML classifiers. It inherits from sklearn.base.BaseEstimator and sklearn.base.ClassifierMixin, providing standard interfaces for scikit-learn estimators and classifiers.

The class is abstract, meaning it cannot be instantiated directly. Subclasses must implement the ‘fit’ method to provide functionality for training the classifier.

Examples

To create a custom AutoML classifier, you can subclass AutomlClassifier and implement the required methods, such as ‘fit’, ‘fit’, and ‘fit’. Below is an example of how you might implement a simple custom classifier.

First, create a subclass of AutomlClassifier:

>>> from sklearn.pipeline import Pipeline
>>> class CustomClassifier(AutomlClassifier):
>>>     def fit(self, X, y, **kwd):
>>>         # Implement your fitting logic here
>>>         # For instance, you might train a model using the training data X and labels y
>>>         self.model_ = Pipeline([("scaler", StandardScaler()), ("custom_classifier", CustomClassifier())])
>>>         return self
>>>
>>>     def predict(self, X, verbose=0):
>>>         # Implement your prediction logic here
>>>         # For example, use the trained model to make predictions on new data X
>>>         return self.model_.predict(X)
>>>
>>>     def score(self, X, y, sample_weight=None, verbose=0):
>>>         # Implement your scoring logic here
>>>         # For instance, calculate the accuracy of predictions compared to true labels y
>>>         predictions = self.predict(X)
>>>         return accuracy_score(y, predictions)
>>>
>>>     def predict_proba(self, X, verbose=0):
>>>         # Implement your probability prediction logic here
>>>         # For example, return the predicted probabilities for each class
>>>         return self.model_.predict_proba(X)
>>>
>>>     def decision_function(self, X, verbose=0):
>>>         # Implement your decision function logic here
>>>         # For instance, return the decision function values (e.g., distances to decision boundary)
>>>         return self.model_.decision_function(X)

After defining your custom classifier, you can use it like any other scikit-learn estimator:

>>> clf = CustomClassifier()
>>> clf.fit(X_train, y_train)
>>> predictions = clf.predict(X_test)
>>> accuracy = clf.score(X_test, y_test)

abstract decision_function(X, verbose=0)

Predict confidence scores for samples, sometimes coinciding with the probability scores in X. The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.

Parameters:

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

• verbose (int, optional, default=0) – Verbosity level.

Returns:

decision – Predicted confidence scores.

Return type:

array-like of shape (n_samples,)

Raises:

NotImplementedError – If the method is not implemented by the subclass.

abstract fit(X, y, **kwd)

Fit the AutoML classifier on the provided dataset.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Training data.

• y (array-like of shape (n_samples,)) – Target values.

• **kwd (keyword arguments) – Additional parameters for the fit method.

Returns:

self – Returns the instance itself.

Return type:

object

Raises:

NotImplementedError – If the method is not implemented by the subclass.

Notes

This method must be implemented by subclasses. It should contain the logic for training the classifier on the dataset provided in X and y.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – 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:

dict

abstract predict(X, verbose=0)

Predict class labels for samples in X.

Parameters:

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

• verbose (int, optional, default=0) – Verbosity level.

Returns:

y_pred – Predicted class labels.

Return type:

array-like of shape (n_samples,)

Raises:

NotImplementedError – If the method is not implemented by the subclass.

abstract predict_proba(X, verbose=0)

Predict class probabilities for samples in X.

Parameters:

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

• verbose (int, optional, default=0) – Verbosity level.

Returns:

y_proba – Predicted class probabilities.

Return type:

array-like of shape (n_samples, n_classes)

Raises:

NotImplementedError – If the method is not implemented by the subclass.

abstract score(X, y, sample_weight=None, verbose=0)

Return the score based on the metric on the given test data and labels.

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.

• verbose (int, optional, default=0) – Verbosity level.

Returns:

score – Mean accuracy of self.predict(X) with respect to y.

Return type:

float

Raises:

NotImplementedError – If the method is not implemented by the subclass.

set_params(**parameters)

Set the parameters of this estimator.

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

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance