lightgbm.LGBMRanker¶

class
lightgbm.
LGBMRanker
(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:
lightgbm.sklearn.LGBMModel
LightGBM ranker.

__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)¶ 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’, Gradientbased OneSide 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 offit
method to shrink/adapt learning rate in training usingreset_parameter
callback. Note, that this will ignore thelearning_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 multiclass classification task; for binary classification task you may useis_unbalance
orscale_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://scikitlearn.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 asn_samples / (n_classes * np.bincount(y))
. If None, all classes are supposed to have weight one. Note, that these weights will be multiplied withsample_weight
(passed through thefit
method) ifsample_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=1e3)) – 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, RandomState object or None, optional (default=None)) – Random number seed. If int, this number is used to seed the C++ code. If RandomState object (numpy), a random integer is picked based on its state to seed the C++ code. If None, default seeds in C++ code are 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.
Note
A custom objective function can be provided for the
objective
parameter. In this case, it should have the signatureobjective(y_true, y_pred) > grad, hess
orobjective(y_true, y_pred, group) > grad, hess
: y_truearraylike of shape = [n_samples]
The target values.
 y_predarraylike of shape = [n_samples] or shape = [n_samples * n_classes] (for multiclass task)
The predicted values.
 grouparraylike
Group/query data, used for ranking task.
 gradarraylike of shape = [n_samples] or shape = [n_samples * n_classes] (for multiclass task)
The value of the first order derivative (gradient) for each sample point.
 hessarraylike of shape = [n_samples] or shape = [n_samples * n_classes] (for multiclass task)
The value of the second order derivative (Hessian) for each sample point.
For binary task, the y_pred is margin. For multiclass task, the y_pred is group by class_id first, then group by row_id. If you want to get ith row y_pred in jth 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
The best iteration of fitted model if
early_stopping_rounds
has been specified.The best score of fitted model.
The underlying Booster of this model.
The evaluation results if
early_stopping_rounds
has been specified.The feature importances (the higher, the more important).
The names of features.
The number of features of fitted model.
The number of features of fitted model.
The concrete objective used while fitting this model.

property
best_iteration_
¶ The best iteration of fitted model if
early_stopping_rounds
has been specified. Type
int
orNone

property
best_score_
¶ The best score of fitted model.
 Type
dict
orNone

property
evals_result_
¶ The evaluation results if
early_stopping_rounds
has been specified. Type
dict
orNone

property
feature_importances_
¶ The feature importances (the higher, the more important).
Note
importance_type
attribute is passed to the function to configure the type of importance values to be extracted. Type
array
of shape = [n_features]

property
feature_name_
¶ The names of features.
 Type
array
of shape = [n_features]

fit
(X, y, sample_weight=None, init_score=None, group=None, eval_set=None, eval_names=None, eval_sample_weight=None, eval_init_score=None, eval_group=None, eval_metric=None, eval_at=[1, 2, 3, 4, 5], early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None, init_model=None)[source]¶ Build a gradient boosting model from the training set (X, y).
 Parameters
X (arraylike or sparse matrix of shape = [n_samples, n_features]) – Input feature matrix.
y (arraylike of shape = [n_samples]) – The target values (class labels in classification, real numbers in regression).
sample_weight (arraylike of shape = [n_samples] or None, optional (default=None)) – Weights of training data.
init_score (arraylike of shape = [n_samples] or None, optional (default=None)) – Init score of training data.
group (arraylike 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_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 builtin 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.eval_at (list of int, optional (default=[1, 2, 3, 4, 5])) – The evaluation positions of the specified metric.
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 thefirst_metric_only
parameter toTrue
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 usingearly_stopping_rounds
is also printed.Example
With
verbose
= 4 and at least one item ineval_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.
init_model (string, Booster, LGBMModel or None, optional (default=None)) – Filename of LightGBM model, Booster instance or LGBMModel instance used for continue training.
 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)
orfunc(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_truearraylike of shape = [n_samples]
The target values.
 y_predarraylike of shape = [n_samples] or shape = [n_samples * n_classes] (for multiclass task)
The predicted values.
 weightarraylike of shape = [n_samples]
The weight of samples.
 grouparraylike
Group/query data, used for ranking task.
 eval_namestring
The name of evaluation function (without whitespaces).
 eval_resultfloat
The eval result.
 is_higher_betterbool
Is eval result higher better, e.g. AUC is
is_higher_better
.
For binary task, the y_pred is probability of positive class (or margin in case of custom
objective
). For multiclass task, the y_pred is group by class_id first, then group by row_id. If you want to get ith row y_pred in jth class, the access way is y_pred[j * num_data + i].

get_params
(deep=True)¶ 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

property
n_features_
¶ The number of features of fitted model.
 Type
int

property
n_features_in_
¶ The number of features of fitted model.
 Type
int

property
objective_
¶ The concrete objective used while fitting this model.
 Type
string
orcallable

predict
(X, raw_score=False, num_iteration=None, pred_leaf=False, pred_contrib=False, **kwargs)¶ Return the predicted value for each sample.
 Parameters
X (arraylike 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 (arraylike of shape = [n_samples] or shape = [n_samples, n_classes]) – The predicted values.
X_leaves (arraylike 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 (arraylike of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or list with n_classes length of such objects) – If
pred_contrib=True
, the feature contributions for each sample.

set_params
(**params)¶ Set the parameters of this estimator.
 Parameters
**params – Parameter names with their new values.
 Returns
self – Returns self.
 Return type
object
