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LogisticRegressionClassifier

Bases: Classifier

Regularized logistic regression.

Source code in src/safeds/ml/classical/classification/_logistic_regression.py 
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class LogisticRegressionClassifier(Classifier):
    """Regularized logistic regression."""

    def __hash__(self) -> int:
        return _structural_hash(Classifier.__hash__(self), self._target_name, self._feature_names)

    def __init__(self) -> None:
        # Internal state
        self._wrapped_classifier: sk_LogisticRegression | None = None
        self._feature_names: list[str] | None = None
        self._target_name: str | None = None

    def fit(self, training_set: TabularDataset | ExperimentalTabularDataset) -> LogisticRegressionClassifier:
        """
        Create a copy of this classifier and fit it with the given training data.

        This classifier is not modified.

        Parameters
        ----------
        training_set:
            The training data containing the feature and target vectors.

        Returns
        -------
        fitted_classifier:
            The fitted classifier.

        Raises
        ------
        LearningError
            If the training data contains invalid values or if the training failed.
        TypeError
            If a table is passed instead of a tabular dataset.
        NonNumericColumnError
            If the training data contains non-numerical values.
        MissingValuesColumnError
            If the training data contains missing values.
        DatasetMissesDataError
            If the training data contains no rows.
        """
        wrapped_classifier = self._get_sklearn_classifier()
        fit(wrapped_classifier, training_set)

        result = LogisticRegressionClassifier()
        result._wrapped_classifier = wrapped_classifier
        result._feature_names = training_set.features.column_names
        result._target_name = training_set.target.name

        return result

    def predict(self, dataset: Table | ExperimentalTable | ExperimentalTabularDataset) -> TabularDataset:
        """
        Predict a target vector using a dataset containing feature vectors. The model has to be trained first.

        Parameters
        ----------
        dataset:
            The dataset containing the feature vectors.

        Returns
        -------
        table:
            A dataset containing the given feature vectors and the predicted target vector.

        Raises
        ------
        ModelNotFittedError
            If the model has not been fitted yet.
        DatasetMissesFeaturesError
            If the dataset misses feature columns.
        PredictionError
            If predicting with the given dataset failed.
        NonNumericColumnError
            If the dataset contains non-numerical values.
        MissingValuesColumnError
            If the dataset contains missing values.
        DatasetMissesDataError
            If the dataset contains no rows.
        """
        return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name)

    @property
    def is_fitted(self) -> bool:
        """Whether the classifier is fitted."""
        return self._wrapped_classifier is not None

    def _get_sklearn_classifier(self) -> ClassifierMixin:
        """
        Return a new wrapped Classifier from sklearn.

        Returns
        -------
        wrapped_classifier:
            The sklearn Classifier.
        """
        from sklearn.linear_model import LogisticRegression as sk_LogisticRegression

        return sk_LogisticRegression(n_jobs=-1)

is_fitted: bool property

Whether the classifier is fitted.

fit(training_set)

Create a copy of this classifier and fit it with the given training data.

This classifier is not modified.

Parameters:

Name Type Description Default
training_set TabularDataset | ExperimentalTabularDataset

The training data containing the feature and target vectors.

 required

Returns:

Name Type Description
fitted_classifier LogisticRegressionClassifier

The fitted classifier.

Raises:

Type Description
LearningError

If the training data contains invalid values or if the training failed.
TypeError

If a table is passed instead of a tabular dataset.
NonNumericColumnError

If the training data contains non-numerical values.
MissingValuesColumnError

If the training data contains missing values.
DatasetMissesDataError

If the training data contains no rows.
Source code in src/safeds/ml/classical/classification/_logistic_regression.py 
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def fit(self, training_set: TabularDataset | ExperimentalTabularDataset) -> LogisticRegressionClassifier:
    """
    Create a copy of this classifier and fit it with the given training data.

    This classifier is not modified.

    Parameters
    ----------
    training_set:
        The training data containing the feature and target vectors.

    Returns
    -------
    fitted_classifier:
        The fitted classifier.

    Raises
    ------
    LearningError
        If the training data contains invalid values or if the training failed.
    TypeError
        If a table is passed instead of a tabular dataset.
    NonNumericColumnError
        If the training data contains non-numerical values.
    MissingValuesColumnError
        If the training data contains missing values.
    DatasetMissesDataError
        If the training data contains no rows.
    """
    wrapped_classifier = self._get_sklearn_classifier()
    fit(wrapped_classifier, training_set)

    result = LogisticRegressionClassifier()
    result._wrapped_classifier = wrapped_classifier
    result._feature_names = training_set.features.column_names
    result._target_name = training_set.target.name

    return result

predict(dataset)

Predict a target vector using a dataset containing feature vectors. The model has to be trained first.

Parameters:

Name Type Description Default
dataset Table | ExperimentalTable | ExperimentalTabularDataset

The dataset containing the feature vectors.

 required

Returns:

Name Type Description
table TabularDataset

A dataset containing the given feature vectors and the predicted target vector.

Raises:

Type Description
ModelNotFittedError

If the model has not been fitted yet.
DatasetMissesFeaturesError

If the dataset misses feature columns.
PredictionError

If predicting with the given dataset failed.
NonNumericColumnError

If the dataset contains non-numerical values.
MissingValuesColumnError

If the dataset contains missing values.
DatasetMissesDataError

If the dataset contains no rows.
Source code in src/safeds/ml/classical/classification/_logistic_regression.py 
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def predict(self, dataset: Table | ExperimentalTable | ExperimentalTabularDataset) -> TabularDataset:
    """
    Predict a target vector using a dataset containing feature vectors. The model has to be trained first.

    Parameters
    ----------
    dataset:
        The dataset containing the feature vectors.

    Returns
    -------
    table:
        A dataset containing the given feature vectors and the predicted target vector.

    Raises
    ------
    ModelNotFittedError
        If the model has not been fitted yet.
    DatasetMissesFeaturesError
        If the dataset misses feature columns.
    PredictionError
        If predicting with the given dataset failed.
    NonNumericColumnError
        If the dataset contains non-numerical values.
    MissingValuesColumnError
        If the dataset contains missing values.
    DatasetMissesDataError
        If the dataset contains no rows.
    """
    return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name)