lightgbm.LGBMModel

class lightgbm.LGBMModel(boosting_type='gbdt', num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=100, subsample_for_bin=200000, objective=None, class_weight=None, min_split_gain=0.0, min_child_weight=0.001, min_child_samples=20, subsample=1.0, subsample_freq=0, colsample_bytree=1.0, reg_alpha=0.0, reg_lambda=0.0, random_state=None, n_jobs=-1, silent=True, importance_type='split', **kwargs)[source]

Bases: object

Implementation of the scikit-learn API for LightGBM.

__init__(boosting_type='gbdt', num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=100, subsample_for_bin=200000, objective=None, class_weight=None, min_split_gain=0.0, min_child_weight=0.001, min_child_samples=20, subsample=1.0, subsample_freq=0, colsample_bytree=1.0, reg_alpha=0.0, reg_lambda=0.0, random_state=None, n_jobs=-1, silent=True, importance_type='split', **kwargs)[source]

Construct a gradient boosting model.

Parameters:
  • boosting_type (string, optional (default='gbdt')) – ‘gbdt’, traditional Gradient Boosting Decision Tree. ‘dart’, Dropouts meet Multiple Additive Regression Trees. ‘goss’, Gradient-based One-Side Sampling. ‘rf’, Random Forest.
  • num_leaves (int, optional (default=31)) – Maximum tree leaves for base learners.
  • max_depth (int, optional (default=-1)) – Maximum tree depth for base learners, <=0 means no limit.
  • learning_rate (float, optional (default=0.1)) – Boosting learning rate. You can use callbacks parameter of fit method to shrink/adapt learning rate in training using reset_parameter callback. Note, that this will ignore the learning_rate argument in training.
  • n_estimators (int, optional (default=100)) – Number of boosted trees to fit.
  • subsample_for_bin (int, optional (default=200000)) – Number of samples for constructing bins.
  • objective (string, callable or None, optional (default=None)) – Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below). Default: ‘regression’ for LGBMRegressor, ‘binary’ or ‘multiclass’ for LGBMClassifier, ‘lambdarank’ for LGBMRanker.
  • class_weight (dict, 'balanced' or None, optional (default=None)) – Weights associated with classes in the form {class_label: weight}. Use this parameter only for multi-class classification task; for binary classification task you may use is_unbalance or scale_pos_weight parameters. Note, that the usage of all these parameters will result in poor estimates of the individual class probabilities. You may want to consider performing probability calibration (https://scikit-learn.org/stable/modules/calibration.html) of your model. The ‘balanced’ mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)). If None, all classes are supposed to have weight one. Note, that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.
  • min_split_gain (float, optional (default=0.)) – Minimum loss reduction required to make a further partition on a leaf node of the tree.
  • min_child_weight (float, optional (default=1e-3)) – Minimum sum of instance weight (hessian) needed in a child (leaf).
  • min_child_samples (int, optional (default=20)) – Minimum number of data needed in a child (leaf).
  • subsample (float, optional (default=1.)) – Subsample ratio of the training instance.
  • subsample_freq (int, optional (default=0)) – Frequence of subsample, <=0 means no enable.
  • colsample_bytree (float, optional (default=1.)) – Subsample ratio of columns when constructing each tree.
  • reg_alpha (float, optional (default=0.)) – L1 regularization term on weights.
  • reg_lambda (float, optional (default=0.)) – L2 regularization term on weights.
  • random_state (int or None, optional (default=None)) – Random number seed. If None, default seeds in C++ code will be used.
  • n_jobs (int, optional (default=-1)) – Number of parallel threads.
  • silent (bool, optional (default=True)) – Whether to print messages while running boosting.
  • importance_type (string, optional (default='split')) – The type of feature importance to be filled into feature_importances_. If ‘split’, result contains numbers of times the feature is used in a model. If ‘gain’, result contains total gains of splits which use the feature.
  • **kwargs

    Other parameters for the model. Check http://lightgbm.readthedocs.io/en/latest/Parameters.html for more parameters.

    Warning

    **kwargs is not supported in sklearn, it may cause unexpected issues.

n_features_

The number of features of fitted model.

Type:int
classes_

The class label array (only for classification problem).

Type:array of shape = [n_classes]
n_classes_

The number of classes (only for classification problem).

Type:int
best_score_

The best score of fitted model.

Type:dict or None
best_iteration_

The best iteration of fitted model if early_stopping_rounds has been specified.

Type:int or None
objective_

The concrete objective used while fitting this model.

Type:string or callable
booster_

The underlying Booster of this model.

Type:Booster
evals_result_

The evaluation results if early_stopping_rounds has been specified.

Type:dict or None
feature_importances_

The feature importances (the higher, the more important the feature).

Type:array of shape = [n_features]

Note

A custom objective function can be provided for the objective parameter. In this case, it should have the signature objective(y_true, y_pred) -> grad, hess or objective(y_true, y_pred, group) -> grad, hess:

y_true : array-like of shape = [n_samples]
The target values.
y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The predicted values.
group : array-like
Group/query data, used for ranking task.
grad : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The value of the first order derivative (gradient) for each sample point.
hess : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The value of the second order derivative (Hessian) for each sample point.

For multi-class task, the y_pred is group by class_id first, then group by row_id. If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i] and you should group grad and hess in this way as well.

Methods

__init__([boosting_type, num_leaves, …]) Construct a gradient boosting model.
fit(X, y[, sample_weight, init_score, …]) Build a gradient boosting model from the training set (X, y).
get_params([deep]) Get parameters for this estimator.
predict(X[, raw_score, num_iteration, …]) Return the predicted value for each sample.
set_params(**params) Set the parameters of this estimator.

Attributes

best_iteration_ Get the best iteration of fitted model.
best_score_ Get the best score of fitted model.
booster_ Get the underlying lightgbm Booster of this model.
evals_result_ Get the evaluation results.
feature_importances_ Get feature importances.
n_features_ Get the number of features of fitted model.
objective_ Get the concrete objective used while fitting this model.
best_iteration_

Get the best iteration of fitted model.

best_score_

Get the best score of fitted model.

booster_

Get the underlying lightgbm Booster of this model.

evals_result_

Get the evaluation results.

feature_importances_

Get feature importances.

Note

Feature importance in sklearn interface used to normalize to 1, it’s deprecated after 2.0.4 and is the same as Booster.feature_importance() now. importance_type attribute is passed to the function to configure the type of importance values to be extracted.

fit(X, y, sample_weight=None, init_score=None, group=None, eval_set=None, eval_names=None, eval_sample_weight=None, eval_class_weight=None, eval_init_score=None, eval_group=None, eval_metric=None, early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None)[source]

Build a gradient boosting model from the training set (X, y).

Parameters:
  • X (array-like or sparse matrix of shape = [n_samples, n_features]) – Input feature matrix.
  • y (array-like of shape = [n_samples]) – The target values (class labels in classification, real numbers in regression).
  • sample_weight (array-like of shape = [n_samples] or None, optional (default=None)) – Weights of training data.
  • init_score (array-like of shape = [n_samples] or None, optional (default=None)) – Init score of training data.
  • group (array-like or None, optional (default=None)) – Group data of training data.
  • eval_set (list or None, optional (default=None)) – A list of (X, y) tuple pairs to use as validation sets.
  • eval_names (list of strings or None, optional (default=None)) – Names of eval_set.
  • eval_sample_weight (list of arrays or None, optional (default=None)) – Weights of eval data.
  • eval_class_weight (list or None, optional (default=None)) – Class weights of eval data.
  • eval_init_score (list of arrays or None, optional (default=None)) – Init score of eval data.
  • eval_group (list of arrays or None, optional (default=None)) – Group data of eval data.
  • eval_metric (string, list of strings, callable or None, optional (default=None)) – If string, it should be a built-in evaluation metric to use. If callable, it should be a custom evaluation metric, see note below for more details. In either case, the metric from the model parameters will be evaluated and used as well. Default: ‘l2’ for LGBMRegressor, ‘logloss’ for LGBMClassifier, ‘ndcg’ for LGBMRanker.
  • early_stopping_rounds (int or None, optional (default=None)) – Activates early stopping. The model will train until the validation score stops improving. Validation score needs to improve at least every early_stopping_rounds round(s) to continue training. Requires at least one validation data and one metric. If there’s more than one, will check all of them. But the training data is ignored anyway. To check only the first metric, set the first_metric_only parameter to True in additional parameters **kwargs of the model constructor.
  • verbose (bool or int, optional (default=True)) –

    Requires at least one evaluation data. If True, the eval metric on the eval set is printed at each boosting stage. If int, the eval metric on the eval set is printed at every verbose boosting stage. The last boosting stage or the boosting stage found by using early_stopping_rounds is also printed.

    Example

    With verbose = 4 and at least one item in eval_set, an evaluation metric is printed every 4 (instead of 1) boosting stages.

  • feature_name (list of strings or 'auto', optional (default='auto')) – Feature names. If ‘auto’ and data is pandas DataFrame, data columns names are used.
  • categorical_feature (list of strings or int, or 'auto', optional (default='auto')) – Categorical features. If list of int, interpreted as indices. If list of strings, interpreted as feature names (need to specify feature_name as well). If ‘auto’ and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature.
  • callbacks (list of callback functions or None, optional (default=None)) – List of callback functions that are applied at each iteration. See Callbacks in Python API for more information.
Returns:

self – Returns self.

Return type:

object

Note

Custom eval function expects a callable with following signatures: func(y_true, y_pred), func(y_true, y_pred, weight) or func(y_true, y_pred, weight, group) and returns (eval_name, eval_result, is_higher_better) or list of (eval_name, eval_result, is_higher_better):

y_true : array-like of shape = [n_samples]
The target values.
y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The predicted values.
weight : array-like of shape = [n_samples]
The weight of samples.
group : array-like
Group/query data, used for ranking task.
eval_name : string
The name of evaluation function (without whitespaces).
eval_result : float
The eval result.
is_higher_better : bool
Is eval result higher better, e.g. AUC is is_higher_better.

For multi-class task, the y_pred is group by class_id first, then group by row_id. If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i].

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters:deep (bool, optional (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
n_features_

Get the number of features of fitted model.

objective_

Get the concrete objective used while fitting this model.

predict(X, raw_score=False, num_iteration=None, pred_leaf=False, pred_contrib=False, **kwargs)[source]

Return the predicted value for each sample.

Parameters:
  • X (array-like or sparse matrix of shape = [n_samples, n_features]) – Input features matrix.
  • raw_score (bool, optional (default=False)) – Whether to predict raw scores.
  • num_iteration (int or None, optional (default=None)) – Limit number of iterations in the prediction. If None, if the best iteration exists, it is used; otherwise, all trees are used. If <= 0, all trees are used (no limits).
  • pred_leaf (bool, optional (default=False)) – Whether to predict leaf index.
  • pred_contrib (bool, optional (default=False)) –

    Whether to predict feature contributions.

    Note

    If you want to get more explanations for your model’s predictions using SHAP values, like SHAP interaction values, you can install the shap package (https://github.com/slundberg/shap). Note that unlike the shap package, with pred_contrib we return a matrix with an extra column, where the last column is the expected value.

  • **kwargs – Other parameters for the prediction.
Returns:

  • predicted_result (array-like of shape = [n_samples] or shape = [n_samples, n_classes]) – The predicted values.
  • X_leaves (array-like of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]) – If pred_leaf=True, the predicted leaf of every tree for each sample.
  • X_SHAP_values (array-like of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes]) – If pred_contrib=True, the feature contributions for each sample.

set_params(**params)[source]

Set the parameters of this estimator.

Parameters:**params – Parameter names with their new values.
Returns:self – Returns self.
Return type:object