ml_grid.model_classes.lightgbm_class

Classes

LightGBMClassifier

Initializes the LightGBMClassifier wrapper.

Module Contents

class ml_grid.model_classes.lightgbm_class.LightGBMClassifier(boosting_type: str = 'gbdt', num_leaves: int = 31, learning_rate: float = 0.05, n_estimators: int = 100, objective: str = 'multiclass', num_class: int = 1, metric: str = 'multi_logloss', feature_fraction: float = 0.9, early_stopping_rounds: int | None = None, verbosity: int = -1)

Bases: sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin

Initializes the LightGBMClassifier wrapper.

Parameters:

• boosting_type (str) – The type of boosting to use.

• num_leaves (int) – Maximum number of leaves in one tree.

• learning_rate (float) – Boosting learning rate.

• n_estimators (int) – Number of boosting rounds.

• objective (str) – The learning objective.

• num_class (int) – The number of classes for multiclass classification.

• metric (str) – The metric to be used for evaluation.

• feature_fraction (float) – Fraction of features to be considered for each tree.

• early_stopping_rounds (Optional[int]) – Activates early stopping. Defaults to None.

• verbosity (int) – Controls the level of LightGBM’s verbosity.

boosting_type = 'gbdt'

num_leaves = 31

learning_rate = 0.05

n_estimators = 100

objective = 'multiclass'

num_class = 1

metric = 'multi_logloss'

feature_fraction = 0.9

early_stopping_rounds = None

model: lightgbm.LGBMClassifier | None = None

verbosity = -1

classes_: numpy.ndarray | None = None

fit(X: pandas.DataFrame, y: pandas.Series | numpy.ndarray) → LightGBMClassifier

Fits the LightGBM model.

This method sanitizes the feature names in X before fitting the underlying lgb.LGBMClassifier.

Parameters:

• X (pd.DataFrame) – The training input samples.

• y (Union[pd.Series, np.ndarray]) – The target values.

Returns:

The fitted estimator.

Return type:

LightGBMClassifier

predict(X: pandas.DataFrame) → numpy.ndarray

Predicts class labels for samples in X.

This method sanitizes the feature names in X to match those used during training.

Parameters:

X (pd.DataFrame) – The input samples to predict.

Raises:

ValueError – If the model has not been fitted yet.

Returns:

The predicted class labels.

Return type:

np.ndarray

score(X: pandas.DataFrame, y: pandas.Series | numpy.ndarray) → float

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

Parameters:

• X (pd.DataFrame) – Test samples.

• y (Union[pd.Series, np.ndarray]) – True labels for X.

Raises:

ValueError – If the model has not been fitted yet.

Returns:

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

Return type:

float