lightgbm_classifier ================================================================================ .. py:module:: evalml.pipelines.components.estimators.classifiers.lightgbm_classifier .. autoapi-nested-parse:: LightGBM Classifier. Module Contents --------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: evalml.pipelines.components.estimators.classifiers.lightgbm_classifier.LightGBMClassifier Contents ~~~~~~~~~~~~~~~~~~~ .. py:class:: LightGBMClassifier(boosting_type='gbdt', learning_rate=0.1, n_estimators=100, max_depth=0, num_leaves=31, min_child_samples=20, bagging_fraction=0.9, bagging_freq=0, n_jobs=-1, random_seed=0, **kwargs) LightGBM Classifier. :param boosting_type: Type of boosting to use. Defaults to "gbdt". - 'gbdt' uses traditional Gradient Boosting Decision Tree - "dart", uses Dropouts meet Multiple Additive Regression Trees - "goss", uses Gradient-based One-Side Sampling - "rf", uses Random Forest :type boosting_type: string :param learning_rate: Boosting learning rate. Defaults to 0.1. :type learning_rate: float :param n_estimators: Number of boosted trees to fit. Defaults to 100. :type n_estimators: int :param max_depth: Maximum tree depth for base learners, <=0 means no limit. Defaults to 0. :type max_depth: int :param num_leaves: Maximum tree leaves for base learners. Defaults to 31. :type num_leaves: int :param min_child_samples: Minimum number of data needed in a child (leaf). Defaults to 20. :type min_child_samples: int :param bagging_fraction: LightGBM will randomly select a subset of features on each iteration (tree) without resampling if this is smaller than 1.0. For example, if set to 0.8, LightGBM will select 80% of features before training each tree. This can be used to speed up training and deal with overfitting. Defaults to 0.9. :type bagging_fraction: float :param bagging_freq: Frequency for bagging. 0 means bagging is disabled. k means perform bagging at every k iteration. Every k-th iteration, LightGBM will randomly select bagging_fraction * 100 % of the data to use for the next k iterations. Defaults to 0. :type bagging_freq: int :param n_jobs: Number of threads to run in parallel. -1 uses all threads. Defaults to -1. :type n_jobs: int or None :param random_seed: Seed for the random number generator. Defaults to 0. :type random_seed: int **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **hyperparameter_ranges** - { "learning_rate": Real(0.000001, 1), "boosting_type": ["gbdt", "dart", "goss", "rf"], "n_estimators": Integer(10, 100), "max_depth": Integer(0, 10), "num_leaves": Integer(2, 100), "min_child_samples": Integer(1, 100), "bagging_fraction": Real(0.000001, 1), "bagging_freq": Integer(0, 1),} * - **model_family** - ModelFamily.LIGHTGBM * - **modifies_features** - True * - **modifies_target** - False * - **name** - LightGBM Classifier * - **SEED_MAX** - SEED_BOUNDS.max_bound * - **SEED_MIN** - 0 * - **supported_problem_types** - [ ProblemTypes.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS,] * - **training_only** - False **Methods** .. autoapisummary:: :nosignatures: evalml.pipelines.components.estimators.classifiers.lightgbm_classifier.LightGBMClassifier.clone evalml.pipelines.components.estimators.classifiers.lightgbm_classifier.LightGBMClassifier.default_parameters evalml.pipelines.components.estimators.classifiers.lightgbm_classifier.LightGBMClassifier.describe evalml.pipelines.components.estimators.classifiers.lightgbm_classifier.LightGBMClassifier.feature_importance evalml.pipelines.components.estimators.classifiers.lightgbm_classifier.LightGBMClassifier.fit evalml.pipelines.components.estimators.classifiers.lightgbm_classifier.LightGBMClassifier.get_prediction_intervals evalml.pipelines.components.estimators.classifiers.lightgbm_classifier.LightGBMClassifier.load evalml.pipelines.components.estimators.classifiers.lightgbm_classifier.LightGBMClassifier.needs_fitting evalml.pipelines.components.estimators.classifiers.lightgbm_classifier.LightGBMClassifier.parameters evalml.pipelines.components.estimators.classifiers.lightgbm_classifier.LightGBMClassifier.predict evalml.pipelines.components.estimators.classifiers.lightgbm_classifier.LightGBMClassifier.predict_proba evalml.pipelines.components.estimators.classifiers.lightgbm_classifier.LightGBMClassifier.save evalml.pipelines.components.estimators.classifiers.lightgbm_classifier.LightGBMClassifier.update_parameters .. py:method:: clone(self) Constructs a new component with the same parameters and random state. :returns: A new instance of this component with identical parameters and random state. .. py:method:: default_parameters(cls) Returns the default parameters for this component. Our convention is that Component.default_parameters == Component().parameters. :returns: Default parameters for this component. :rtype: dict .. py:method:: describe(self, print_name=False, return_dict=False) Describe a component and its parameters. :param print_name: whether to print name of component :type print_name: bool, optional :param return_dict: whether to return description as dictionary in the format {"name": name, "parameters": parameters} :type return_dict: bool, optional :returns: Returns dictionary if return_dict is True, else None. :rtype: None or dict .. py:method:: feature_importance(self) -> pandas.Series :property: Returns importance associated with each feature. :returns: Importance associated with each feature. :rtype: np.ndarray :raises MethodPropertyNotFoundError: If estimator does not have a feature_importance method or a component_obj that implements feature_importance. .. py:method:: fit(self, X, y=None) Fits LightGBM classifier component to data. :param X: The input training data of shape [n_samples, n_features]. :type X: pd.DataFrame :param y: The target training data of length [n_samples]. :type y: pd.Series :returns: self .. py:method:: get_prediction_intervals(self, X: pandas.DataFrame, y: Optional[pandas.Series] = None, coverage: List[float] = None, predictions: pandas.Series = None) -> Dict[str, pandas.Series] Find the prediction intervals using the fitted regressor. This function takes the predictions of the fitted estimator and calculates the rolling standard deviation across all predictions using a window size of 5. The lower and upper predictions are determined by taking the percent point (quantile) function of the lower tail probability at each bound multiplied by the rolling standard deviation. :param X: Data of shape [n_samples, n_features]. :type X: pd.DataFrame :param y: Target data. Ignored. :type y: pd.Series :param coverage: A list of floats between the values 0 and 1 that the upper and lower bounds of the prediction interval should be calculated for. :type coverage: list[float] :param predictions: Optional list of predictions to use. If None, will generate predictions using `X`. :type predictions: pd.Series :returns: Prediction intervals, keys are in the format {coverage}_lower or {coverage}_upper. :rtype: dict :raises MethodPropertyNotFoundError: If the estimator does not support Time Series Regression as a problem type. .. py:method:: load(file_path) :staticmethod: Loads component at file path. :param file_path: Location to load file. :type file_path: str :returns: ComponentBase object .. py:method:: needs_fitting(self) Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances. This can be overridden to False for components that do not need to be fit or whose fit methods do nothing. :returns: True. .. py:method:: parameters(self) :property: Returns the parameters which were used to initialize the component. .. py:method:: predict(self, X) Make predictions using the fitted LightGBM classifier. :param X: Data of shape [n_samples, n_features]. :type X: pd.DataFrame :returns: Predicted values. :rtype: pd.DataFrame .. py:method:: predict_proba(self, X) Make prediction probabilities using the fitted LightGBM classifier. :param X: Data of shape [n_samples, n_features]. :type X: pd.DataFrame :returns: Predicted probability values. :rtype: pd.DataFrame .. py:method:: save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL) Saves component at file path. :param file_path: Location to save file. :type file_path: str :param pickle_protocol: The pickle data stream format. :type pickle_protocol: int .. py:method:: update_parameters(self, update_dict, reset_fit=True) Updates the parameter dictionary of the component. :param update_dict: A dict of parameters to update. :type update_dict: dict :param reset_fit: If True, will set `_is_fitted` to False. :type reset_fit: bool, optional