Python API

Data Structure API

class lightgbm.Dataset(data, label=None, reference=None, weight=None, group=None, init_score=None, silent=False, feature_name='auto', categorical_feature='auto', params=None, free_raw_data=True)[source]

Bases: object

Dataset in LightGBM.

Initialize Dataset.

Parameters:
  • data (string, numpy array, pandas DataFrame, scipy.sparse or list of numpy arrays) – Data source of Dataset. If string, it represents the path to txt file.
  • label (list, numpy 1-D array, pandas Series / one-column DataFrame or None, optional (default=None)) – Label of the data.
  • reference (Dataset or None, optional (default=None)) – If this is Dataset for validation, training data should be used as reference.
  • weight (list, numpy 1-D array, pandas Series or None, optional (default=None)) – Weight for each instance.
  • group (list, numpy 1-D array, pandas Series or None, optional (default=None)) – Group/query size for Dataset.
  • init_score (list, numpy 1-D array, pandas Series or None, optional (default=None)) – Init score for Dataset.
  • silent (bool, optional (default=False)) – Whether to print messages during construction.
  • 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 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.
  • params (dict or None, optional (default=None)) – Other parameters for Dataset.
  • free_raw_data (bool, optional (default=True)) – If True, raw data is freed after constructing inner Dataset.
construct()[source]

Lazy init.

Returns:self – Constructed Dataset object.
Return type:Dataset
create_valid(data, label=None, weight=None, group=None, init_score=None, silent=False, params=None)[source]

Create validation data align with current Dataset.

Parameters:
  • data (string, numpy array, pandas DataFrame, scipy.sparse or list of numpy arrays) – Data source of Dataset. If string, it represents the path to txt file.
  • label (list, numpy 1-D array, pandas Series / one-column DataFrame or None, optional (default=None)) – Label of the data.
  • weight (list, numpy 1-D array, pandas Series or None, optional (default=None)) – Weight for each instance.
  • group (list, numpy 1-D array, pandas Series or None, optional (default=None)) – Group/query size for Dataset.
  • init_score (list, numpy 1-D array, pandas Series or None, optional (default=None)) – Init score for Dataset.
  • silent (bool, optional (default=False)) – Whether to print messages during construction.
  • params (dict or None, optional (default=None)) – Other parameters for validation Dataset.
Returns:

valid – Validation Dataset with reference to self.

Return type:

Dataset

get_field(field_name)[source]

Get property from the Dataset.

Parameters:field_name (string) – The field name of the information.
Returns:info – A numpy array with information from the Dataset.
Return type:numpy array
get_group()[source]

Get the group of the Dataset.

Returns:group – Group size of each group.
Return type:numpy array or None
get_init_score()[source]

Get the initial score of the Dataset.

Returns:init_score – Init score of Booster.
Return type:numpy array or None
get_label()[source]

Get the label of the Dataset.

Returns:label – The label information from the Dataset.
Return type:numpy array or None
get_ref_chain(ref_limit=100)[source]

Get a chain of Dataset objects.

Starts with r, then goes to r.reference (if exists), then to r.reference.reference, etc. until we hit ref_limit or a reference loop.

Parameters:ref_limit (int, optional (default=100)) – The limit number of references.
Returns:ref_chain – Chain of references of the Datasets.
Return type:set of Dataset
get_weight()[source]

Get the weight of the Dataset.

Returns:weight – Weight for each data point from the Dataset.
Return type:numpy array or None
num_data()[source]

Get the number of rows in the Dataset.

Returns:number_of_rows – The number of rows in the Dataset.
Return type:int
num_feature()[source]

Get the number of columns (features) in the Dataset.

Returns:number_of_columns – The number of columns (features) in the Dataset.
Return type:int
save_binary(filename)[source]

Save Dataset to a binary file.

Parameters:filename (string) – Name of the output file.
Returns:self – Returns self.
Return type:Dataset
set_categorical_feature(categorical_feature)[source]

Set categorical features.

Parameters:categorical_feature (list of int or strings) – Names or indices of categorical features.
Returns:self – Dataset with set categorical features.
Return type:Dataset
set_feature_name(feature_name)[source]

Set feature name.

Parameters:feature_name (list of strings) – Feature names.
Returns:self – Dataset with set feature name.
Return type:Dataset
set_field(field_name, data)[source]

Set property into the Dataset.

Parameters:
  • field_name (string) – The field name of the information.
  • data (list, numpy 1-D array, pandas Series or None) – The array of data to be set.
Returns:

self – Dataset with set property.

Return type:

Dataset

set_group(group)[source]

Set group size of Dataset (used for ranking).

Parameters:group (list, numpy 1-D array, pandas Series or None) – Group size of each group.
Returns:self – Dataset with set group.
Return type:Dataset
set_init_score(init_score)[source]

Set init score of Booster to start from.

Parameters:init_score (list, numpy 1-D array, pandas Series or None) – Init score for Booster.
Returns:self – Dataset with set init score.
Return type:Dataset
set_label(label)[source]

Set label of Dataset.

Parameters:label (list, numpy 1-D array, pandas Series / one-column DataFrame or None) – The label information to be set into Dataset.
Returns:self – Dataset with set label.
Return type:Dataset
set_reference(reference)[source]

Set reference Dataset.

Parameters:reference (Dataset) – Reference that is used as a template to construct the current Dataset.
Returns:self – Dataset with set reference.
Return type:Dataset
set_weight(weight)[source]

Set weight of each instance.

Parameters:weight (list, numpy 1-D array, pandas Series or None) – Weight to be set for each data point.
Returns:self – Dataset with set weight.
Return type:Dataset
subset(used_indices, params=None)[source]

Get subset of current Dataset.

Parameters:
  • used_indices (list of int) – Indices used to create the subset.
  • params (dict or None, optional (default=None)) – These parameters will be passed to Dataset constructor.
Returns:

subset – Subset of the current Dataset.

Return type:

Dataset

class lightgbm.Booster(params=None, train_set=None, model_file=None, silent=False)[source]

Bases: object

Booster in LightGBM.

Initialize the Booster.

Parameters:
  • params (dict or None, optional (default=None)) – Parameters for Booster.
  • train_set (Dataset or None, optional (default=None)) – Training dataset.
  • model_file (string or None, optional (default=None)) – Path to the model file.
  • silent (bool, optional (default=False)) – Whether to print messages during construction.
add_valid(data, name)[source]

Add validation data.

Parameters:
  • data (Dataset) – Validation data.
  • name (string) – Name of validation data.
Returns:

self – Booster with set validation data.

Return type:

Booster

attr(key)[source]

Get attribute string from the Booster.

Parameters:key (string) – The name of the attribute.
Returns:value – The attribute value. Returns None if attribute does not exist.
Return type:string or None
current_iteration()[source]

Get the index of the current iteration.

Returns:cur_iter – The index of the current iteration.
Return type:int
dump_model(num_iteration=None, start_iteration=0)[source]

Dump Booster to JSON format.

Parameters:
  • num_iteration (int or None, optional (default=None)) – Index of the iteration that should be dumped. If None, if the best iteration exists, it is dumped; otherwise, all iterations are dumped. If <= 0, all iterations are dumped.
  • start_iteration (int, optional (default=0)) – Start index of the iteration that should be dumped.
Returns:

json_repr – JSON format of Booster.

Return type:

dict

eval(data, name, feval=None)[source]

Evaluate for data.

Parameters:
  • data (Dataset) – Data for the evaluating.
  • name (string) – Name of the data.
  • feval (callable or None, optional (default=None)) – Customized evaluation function. Should accept two parameters: preds, train_data, and return (eval_name, eval_result, is_higher_better) or list of such tuples. For multi-class task, the preds is group by class_id first, then group by row_id. If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
Returns:

result – List with evaluation results.

Return type:

list

eval_train(feval=None)[source]

Evaluate for training data.

Parameters:feval (callable or None, optional (default=None)) – Customized evaluation function. Should accept two parameters: preds, train_data, and return (eval_name, eval_result, is_higher_better) or list of such tuples. For multi-class task, the preds is group by class_id first, then group by row_id. If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
Returns:result – List with evaluation results.
Return type:list
eval_valid(feval=None)[source]

Evaluate for validation data.

Parameters:feval (callable or None, optional (default=None)) – Customized evaluation function. Should accept two parameters: preds, train_data, and return (eval_name, eval_result, is_higher_better) or list of such tuples. For multi-class task, the preds is group by class_id first, then group by row_id. If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
Returns:result – List with evaluation results.
Return type:list
feature_importance(importance_type='split', iteration=None)[source]

Get feature importances.

Parameters:
  • importance_type (string, optional (default="split")) – How the importance is calculated. 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.
  • iteration (int or None, optional (default=None)) – Limit number of iterations in the feature importance calculation. If None, if the best iteration exists, it is used; otherwise, all trees are used. If <= 0, all trees are used (no limits).
Returns:

result – Array with feature importances.

Return type:

numpy array

feature_name()[source]

Get names of features.

Returns:result – List with names of features.
Return type:list
free_dataset()[source]

Free Booster’s Datasets.

Returns:self – Booster without Datasets.
Return type:Booster
free_network()[source]

Free Booster’s network.

Returns:self – Booster with freed network.
Return type:Booster
get_leaf_output(tree_id, leaf_id)[source]

Get the output of a leaf.

Parameters:
  • tree_id (int) – The index of the tree.
  • leaf_id (int) – The index of the leaf in the tree.
Returns:

result – The output of the leaf.

Return type:

float

model_from_string(model_str, verbose=True)[source]

Load Booster from a string.

Parameters:
  • model_str (string) – Model will be loaded from this string.
  • verbose (bool, optional (default=True)) – Whether to print messages while loading model.
Returns:

self – Loaded Booster object.

Return type:

Booster

model_to_string(num_iteration=None, start_iteration=0)[source]

Save Booster to string.

Parameters:
  • num_iteration (int or None, optional (default=None)) – Index of the iteration that should be saved. If None, if the best iteration exists, it is saved; otherwise, all iterations are saved. If <= 0, all iterations are saved.
  • start_iteration (int, optional (default=0)) – Start index of the iteration that should be saved.
Returns:

str_repr – String representation of Booster.

Return type:

string

num_feature()[source]

Get number of features.

Returns:num_feature – The number of features.
Return type:int
num_model_per_iteration()[source]

Get number of models per iteration.

Returns:model_per_iter – The number of models per iteration.
Return type:int
num_trees()[source]

Get number of weak sub-models.

Returns:num_trees – The number of weak sub-models.
Return type:int
predict(data, num_iteration=None, raw_score=False, pred_leaf=False, pred_contrib=False, data_has_header=False, is_reshape=True, **kwargs)[source]

Make a prediction.

Parameters:
  • data (string, numpy array, pandas DataFrame or scipy.sparse) – Data source for prediction. If string, it represents the path to txt file.
  • 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 iterations are used. If <= 0, all iterations are used (no limits).
  • raw_score (bool, optional (default=False)) – Whether to predict raw scores.
  • 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 explanation for your model’s predictions using SHAP values like SHAP interaction values, you can install shap package (https://github.com/slundberg/shap).

  • data_has_header (bool, optional (default=False)) – Whether the data has header. Used only if data is string.
  • is_reshape (bool, optional (default=True)) – If True, result is reshaped to [nrow, ncol].
  • **kwargs – Other parameters for the prediction.
Returns:

result – Prediction result.

Return type:

numpy array

refit(data, label, decay_rate=0.9, **kwargs)[source]

Refit the existing Booster by new data.

Parameters:
  • data (string, numpy array, pandas DataFrame or scipy.sparse) – Data source for refit. If string, it represents the path to txt file.
  • label (list, numpy 1-D array or pandas Series / one-column DataFrame) – Label for refit.
  • decay_rate (float, optional (default=0.9)) – Decay rate of refit, will use leaf_output = decay_rate * old_leaf_output + (1.0 - decay_rate) * new_leaf_output to refit trees.
  • **kwargs – Other parameters for refit. These parameters will be passed to predict method.
Returns:

result – Refitted Booster.

Return type:

Booster

reset_parameter(params)[source]

Reset parameters of Booster.

Parameters:params (dict) – New parameters for Booster.
Returns:self – Booster with new parameters.
Return type:Booster
rollback_one_iter()[source]

Rollback one iteration.

Returns:self – Booster with rolled back one iteration.
Return type:Booster
save_model(filename, num_iteration=None, start_iteration=0)[source]

Save Booster to file.

Parameters:
  • filename (string) – Filename to save Booster.
  • num_iteration (int or None, optional (default=None)) – Index of the iteration that should be saved. If None, if the best iteration exists, it is saved; otherwise, all iterations are saved. If <= 0, all iterations are saved.
  • start_iteration (int, optional (default=0)) – Start index of the iteration that should be saved.
Returns:

self – Returns self.

Return type:

Booster

set_attr(**kwargs)[source]

Set attributes to the Booster.

Parameters:**kwargs – The attributes to set. Setting a value to None deletes an attribute.
Returns:self – Booster with set attributes.
Return type:Booster
set_network(machines, local_listen_port=12400, listen_time_out=120, num_machines=1)[source]

Set the network configuration.

Parameters:
  • machines (list, set or string) – Names of machines.
  • local_listen_port (int, optional (default=12400)) – TCP listen port for local machines.
  • listen_time_out (int, optional (default=120)) – Socket time-out in minutes.
  • num_machines (int, optional (default=1)) – The number of machines for parallel learning application.
Returns:

self – Booster with set network.

Return type:

Booster

set_train_data_name(name)[source]

Set the name to the training Dataset.

Parameters:name (string) – Name for the training Dataset.
Returns:self – Booster with set training Dataset name.
Return type:Booster
shuffle_models(start_iteration=0, end_iteration=-1)[source]

Shuffle models.

Parameters:
  • start_iteration (int, optional (default=0)) – The first iteration that will be shuffled.
  • end_iteration (int, optional (default=-1)) – The last iteration that will be shuffled. If <= 0, means the last available iteration.
Returns:

self – Booster with shuffled models.

Return type:

Booster

update(train_set=None, fobj=None)[source]

Update Booster for one iteration.

Parameters:
  • train_set (Dataset or None, optional (default=None)) – Training data. If None, last training data is used.
  • fobj (callable or None, optional (default=None)) –

    Customized objective function.

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

Returns:

is_finished – Whether the update was successfully finished.

Return type:

bool

Training API

lightgbm.train(params, train_set, num_boost_round=100, valid_sets=None, valid_names=None, fobj=None, feval=None, init_model=None, feature_name='auto', categorical_feature='auto', early_stopping_rounds=None, evals_result=None, verbose_eval=True, learning_rates=None, keep_training_booster=False, callbacks=None)[source]

Perform the training with given parameters.

Parameters:
  • params (dict) – Parameters for training.
  • train_set (Dataset) – Data to be trained on.
  • num_boost_round (int, optional (default=100)) – Number of boosting iterations.
  • valid_sets (list of Datasets or None, optional (default=None)) – List of data to be evaluated on during training.
  • valid_names (list of strings or None, optional (default=None)) – Names of valid_sets.
  • fobj (callable or None, optional (default=None)) – Customized objective function.
  • feval (callable or None, optional (default=None)) – Customized evaluation function. Should accept two parameters: preds, train_data, and return (eval_name, eval_result, is_higher_better) or list of such tuples. For multi-class task, the preds is group by class_id first, then group by row_id. If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i]. To ignore the default metric corresponding to the used objective, set the metric parameter to the string "None" in params.
  • init_model (string, Booster or None, optional (default=None)) – Filename of LightGBM model or Booster instance used for continue training.
  • 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 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.
  • 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. The index of iteration that has the best performance will be saved in the best_iteration field if early stopping logic is enabled by setting early_stopping_rounds.
  • evals_result (dict or None, optional (default=None)) –

    This dictionary used to store all evaluation results of all the items in valid_sets.

    Example

    With a valid_sets = [valid_set, train_set], valid_names = [‘eval’, ‘train’] and a params = {‘metric’: ‘logloss’} returns {‘train’: {‘logloss’: [‘0.48253’, ‘0.35953’, …]}, ‘eval’: {‘logloss’: [‘0.480385’, ‘0.357756’, …]}}.

  • verbose_eval (bool or int, optional (default=True)) –

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

    Example

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

  • learning_rates (list, callable or None, optional (default=None)) – List of learning rates for each boosting round or a customized function that calculates learning_rate in terms of current number of round (e.g. yields learning rate decay).
  • keep_training_booster (bool, optional (default=False)) – Whether the returned Booster will be used to keep training. If False, the returned value will be converted into _InnerPredictor before returning. You can still use _InnerPredictor as init_model for future continue training.
  • callbacks (list of callables or None, optional (default=None)) – List of callback functions that are applied at each iteration. See Callbacks in Python API for more information.
Returns:

booster – The trained Booster model.

Return type:

Booster

lightgbm.cv(params, train_set, num_boost_round=100, folds=None, nfold=5, stratified=True, shuffle=True, metrics=None, fobj=None, feval=None, init_model=None, feature_name='auto', categorical_feature='auto', early_stopping_rounds=None, fpreproc=None, verbose_eval=None, show_stdv=True, seed=0, callbacks=None)[source]

Perform the cross-validation with given paramaters.

Parameters:
  • params (dict) – Parameters for Booster.
  • train_set (Dataset) – Data to be trained on.
  • num_boost_round (int, optional (default=100)) – Number of boosting iterations.
  • folds (generator or iterator of (train_idx, test_idx) tuples, scikit-learn splitter object or None, optional (default=None)) – If generator or iterator, it should yield the train and test indices for each fold. If object, it should be one of the scikit-learn splitter classes (http://scikit-learn.org/stable/modules/classes.html#splitter-classes) and have split method. This argument has highest priority over other data split arguments.
  • nfold (int, optional (default=5)) – Number of folds in CV.
  • stratified (bool, optional (default=True)) – Whether to perform stratified sampling.
  • shuffle (bool, optional (default=True)) – Whether to shuffle before splitting data.
  • metrics (string, list of strings or None, optional (default=None)) – Evaluation metrics to be monitored while CV. If not None, the metric in params will be overridden.
  • fobj (callable or None, optional (default=None)) – Custom objective function.
  • feval (callable or None, optional (default=None)) – Customized evaluation function. Should accept two parameters: preds, train_data, and return (eval_name, eval_result, is_higher_better) or list of such tuples. For multi-class task, the preds is group by class_id first, then group by row_id. If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i]. To ignore the default metric corresponding to the used objective, set metrics to the string "None".
  • init_model (string, Booster or None, optional (default=None)) – Filename of LightGBM model or Booster instance used for continue training.
  • 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 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.
  • early_stopping_rounds (int or None, optional (default=None)) – Activates early stopping. CV score needs to improve at least every early_stopping_rounds round(s) to continue. Requires at least one metric. If there’s more than one, will check all of them. Last entry in evaluation history is the one from the best iteration.
  • fpreproc (callable or None, optional (default=None)) – Preprocessing function that takes (dtrain, dtest, params) and returns transformed versions of those.
  • verbose_eval (bool, int, or None, optional (default=None)) – Whether to display the progress. If None, progress will be displayed when np.ndarray is returned. If True, progress will be displayed at every boosting stage. If int, progress will be displayed at every given verbose_eval boosting stage.
  • show_stdv (bool, optional (default=True)) – Whether to display the standard deviation in progress. Results are not affected by this parameter, and always contain std.
  • seed (int, optional (default=0)) – Seed used to generate the folds (passed to numpy.random.seed).
  • callbacks (list of callables or None, optional (default=None)) – List of callback functions that are applied at each iteration. See Callbacks in Python API for more information.
Returns:

eval_hist – Evaluation history. The dictionary has the following format: {‘metric1-mean’: [values], ‘metric1-stdv’: [values], ‘metric2-mean’: [values], ‘metric2-stdv’: [values], …}.

Return type:

dict

Scikit-learn API

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.

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, -1 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. 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.

    Note

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

n_features_

int – The number of features of fitted model.

classes_

array of shape = [n_classes] – The class label array (only for classification problem).

n_classes_

int – The number of classes (only for classification problem).

best_score_

dict or None – The best score of fitted model.

best_iteration_

int or None – The best iteration of fitted model if early_stopping_rounds has been specified.

objective_

string or callable – The concrete objective used while fitting this model.

booster_

Booster – The underlying Booster of this model.

evals_result_

dict or None – The evaluation results if early_stopping_rounds has been specified.

feature_importances_

array of shape = [n_features] – The feature importances (the higher, the more important the feature).

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 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 derivative 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.

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.
  • 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 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.
  • 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_bigger_better) or list of (eval_name, eval_result, is_bigger_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.
eval_result : float
The eval result.
is_bigger_better : bool
Is eval result bigger better, e.g. AUC is bigger_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 explanation for your model’s predictions using SHAP values like SHAP interaction values, you can install shap package (https://github.com/slundberg/shap).

  • **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 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 each 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
class lightgbm.LGBMClassifier(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, object

LightGBM classifier.

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, -1 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. 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.

    Note

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

n_features_

int – The number of features of fitted model.

classes_

array of shape = [n_classes] – The class label array (only for classification problem).

n_classes_

int – The number of classes (only for classification problem).

best_score_

dict or None – The best score of fitted model.

best_iteration_

int or None – The best iteration of fitted model if early_stopping_rounds has been specified.

objective_

string or callable – The concrete objective used while fitting this model.

booster_

Booster – The underlying Booster of this model.

evals_result_

dict or None – The evaluation results if early_stopping_rounds has been specified.

feature_importances_

array of shape = [n_features] – The feature importances (the higher, the more important the feature).

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 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 derivative 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.

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.

classes_

Get the class label array.

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, eval_set=None, eval_names=None, eval_sample_weight=None, eval_class_weight=None, eval_init_score=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.
  • 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 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.
  • 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_bigger_better) or list of (eval_name, eval_result, is_bigger_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.
eval_result : float
The eval result.
is_bigger_better : bool
Is eval result bigger better, e.g. AUC is bigger_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)

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_classes_

Get the number of classes.

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 explanation for your model’s predictions using SHAP values like SHAP interaction values, you can install shap package (https://github.com/slundberg/shap).

  • **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 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 each feature contributions for each sample.

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

Return the predicted probability for each class 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 explanation for your model’s predictions using SHAP values like SHAP interaction values, you can install shap package (https://github.com/slundberg/shap).

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

  • predicted_probability (array-like of shape = [n_samples, n_classes]) – The predicted probability for each class for each sample.
  • X_leaves (array-like of shape = [n_samples, n_trees * n_classes]) – If pred_leaf=True, the predicted leaf every tree for each sample.
  • X_SHAP_values (array-like of shape = [n_samples, (n_features + 1) * n_classes]) – If pred_contrib=True, the each 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
class lightgbm.LGBMRegressor(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, object

LightGBM regressor.

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, -1 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. 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.

    Note

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

n_features_

int – The number of features of fitted model.

classes_

array of shape = [n_classes] – The class label array (only for classification problem).

n_classes_

int – The number of classes (only for classification problem).

best_score_

dict or None – The best score of fitted model.

best_iteration_

int or None – The best iteration of fitted model if early_stopping_rounds has been specified.

objective_

string or callable – The concrete objective used while fitting this model.

booster_

Booster – The underlying Booster of this model.

evals_result_

dict or None – The evaluation results if early_stopping_rounds has been specified.

feature_importances_

array of shape = [n_features] – The feature importances (the higher, the more important the feature).

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 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 derivative 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.

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, eval_set=None, eval_names=None, eval_sample_weight=None, eval_init_score=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_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.
  • 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 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.
  • 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_bigger_better) or list of (eval_name, eval_result, is_bigger_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.
eval_result : float
The eval result.
is_bigger_better : bool
Is eval result bigger better, e.g. AUC is bigger_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)

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)

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 explanation for your model’s predictions using SHAP values like SHAP interaction values, you can install shap package (https://github.com/slundberg/shap).

  • **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 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 each 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
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.

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, -1 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. 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.

    Note

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

n_features_

int – The number of features of fitted model.

classes_

array of shape = [n_classes] – The class label array (only for classification problem).

n_classes_

int – The number of classes (only for classification problem).

best_score_

dict or None – The best score of fitted model.

best_iteration_

int or None – The best iteration of fitted model if early_stopping_rounds has been specified.

objective_

string or callable – The concrete objective used while fitting this model.

booster_

Booster – The underlying Booster of this model.

evals_result_

dict or None – The evaluation results if early_stopping_rounds has been specified.

feature_importances_

array of shape = [n_features] – The feature importances (the higher, the more important the feature).

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 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 derivative 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.

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_init_score=None, eval_group=None, eval_metric=None, eval_at=[1], 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_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.
  • eval_at (list of int, optional (default=[1])) – 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.
  • 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 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.
  • 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_bigger_better) or list of (eval_name, eval_result, is_bigger_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.
eval_result : float
The eval result.
is_bigger_better : bool
Is eval result bigger better, e.g. AUC is bigger_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)

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)

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 explanation for your model’s predictions using SHAP values like SHAP interaction values, you can install shap package (https://github.com/slundberg/shap).

  • **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 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 each 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

Callbacks

lightgbm.early_stopping(stopping_rounds, verbose=True)[source]

Create a callback that activates early stopping.

Note

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.

Parameters:
  • stopping_rounds (int) – The possible number of rounds without the trend occurrence.
  • verbose (bool, optional (default=True)) – Whether to print message with early stopping information.
Returns:

callback – The callback that activates early stopping.

Return type:

function

lightgbm.print_evaluation(period=1, show_stdv=True)[source]

Create a callback that prints the evaluation results.

Parameters:
  • period (int, optional (default=1)) – The period to print the evaluation results.
  • show_stdv (bool, optional (default=True)) – Whether to show stdv (if provided).
Returns:

callback – The callback that prints the evaluation results every period iteration(s).

Return type:

function

lightgbm.record_evaluation(eval_result)[source]

Create a callback that records the evaluation history into eval_result.

Parameters:eval_result (dict) – A dictionary to store the evaluation results.
Returns:callback – The callback that records the evaluation history into the passed dictionary.
Return type:function
lightgbm.reset_parameter(**kwargs)[source]

Create a callback that resets the parameter after the first iteration.

Note

The initial parameter will still take in-effect on first iteration.

Parameters:**kwargs (value should be list or function) – List of parameters for each boosting round or a customized function that calculates the parameter in terms of current number of round (e.g. yields learning rate decay). If list lst, parameter = lst[current_round]. If function func, parameter = func(current_round).
Returns:callback – The callback that resets the parameter after the first iteration.
Return type:function

Plotting

lightgbm.plot_importance(booster, ax=None, height=0.2, xlim=None, ylim=None, title='Feature importance', xlabel='Feature importance', ylabel='Features', importance_type='split', max_num_features=None, ignore_zero=True, figsize=None, grid=True, precision=None, **kwargs)[source]

Plot model’s feature importances.

Parameters:
  • booster (Booster or LGBMModel) – Booster or LGBMModel instance which feature importance should be plotted.
  • ax (matplotlib.axes.Axes or None, optional (default=None)) – Target axes instance. If None, new figure and axes will be created.
  • height (float, optional (default=0.2)) – Bar height, passed to ax.barh().
  • xlim (tuple of 2 elements or None, optional (default=None)) – Tuple passed to ax.xlim().
  • ylim (tuple of 2 elements or None, optional (default=None)) – Tuple passed to ax.ylim().
  • title (string or None, optional (default="Feature importance")) – Axes title. If None, title is disabled.
  • xlabel (string or None, optional (default="Feature importance")) – X-axis title label. If None, title is disabled.
  • ylabel (string or None, optional (default="Features")) – Y-axis title label. If None, title is disabled.
  • importance_type (string, optional (default="split")) – How the importance is calculated. 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.
  • max_num_features (int or None, optional (default=None)) – Max number of top features displayed on plot. If None or <1, all features will be displayed.
  • ignore_zero (bool, optional (default=True)) – Whether to ignore features with zero importance.
  • figsize (tuple of 2 elements or None, optional (default=None)) – Figure size.
  • grid (bool, optional (default=True)) – Whether to add a grid for axes.
  • precision (int or None, optional (default=None)) – Used to restrict the display of floating point values to a certain precision.
  • **kwargs – Other parameters passed to ax.barh().
Returns:

ax – The plot with model’s feature importances.

Return type:

matplotlib.axes.Axes

lightgbm.plot_metric(booster, metric=None, dataset_names=None, ax=None, xlim=None, ylim=None, title='Metric during training', xlabel='Iterations', ylabel='auto', figsize=None, grid=True)[source]

Plot one metric during training.

Parameters:
  • booster (dict or LGBMModel) – Dictionary returned from lightgbm.train() or LGBMModel instance.
  • metric (string or None, optional (default=None)) – The metric name to plot. Only one metric supported because different metrics have various scales. If None, first metric picked from dictionary (according to hashcode).
  • dataset_names (list of strings or None, optional (default=None)) – List of the dataset names which are used to calculate metric to plot. If None, all datasets are used.
  • ax (matplotlib.axes.Axes or None, optional (default=None)) – Target axes instance. If None, new figure and axes will be created.
  • xlim (tuple of 2 elements or None, optional (default=None)) – Tuple passed to ax.xlim().
  • ylim (tuple of 2 elements or None, optional (default=None)) – Tuple passed to ax.ylim().
  • title (string or None, optional (default="Metric during training")) – Axes title. If None, title is disabled.
  • xlabel (string or None, optional (default="Iterations")) – X-axis title label. If None, title is disabled.
  • ylabel (string or None, optional (default="auto")) – Y-axis title label. If ‘auto’, metric name is used. If None, title is disabled.
  • figsize (tuple of 2 elements or None, optional (default=None)) – Figure size.
  • grid (bool, optional (default=True)) – Whether to add a grid for axes.
Returns:

ax – The plot with metric’s history over the training.

Return type:

matplotlib.axes.Axes

lightgbm.plot_tree(booster, ax=None, tree_index=0, figsize=None, old_graph_attr=None, old_node_attr=None, old_edge_attr=None, show_info=None, precision=None, **kwargs)[source]

Plot specified tree.

Note

It is preferable to use create_tree_digraph() because of its lossless quality and returned objects can be also rendered and displayed directly inside a Jupyter notebook.

Parameters:
  • booster (Booster or LGBMModel) – Booster or LGBMModel instance to be plotted.
  • ax (matplotlib.axes.Axes or None, optional (default=None)) – Target axes instance. If None, new figure and axes will be created.
  • tree_index (int, optional (default=0)) – The index of a target tree to plot.
  • figsize (tuple of 2 elements or None, optional (default=None)) – Figure size.
  • show_info (list of strings or None, optional (default=None)) – What information should be shown in nodes. Possible values of list items: ‘split_gain’, ‘internal_value’, ‘internal_count’, ‘leaf_count’.
  • precision (int or None, optional (default=None)) – Used to restrict the display of floating point values to a certain precision.
  • **kwargs – Other parameters passed to Digraph constructor. Check https://graphviz.readthedocs.io/en/stable/api.html#digraph for the full list of supported parameters.
Returns:

ax – The plot with single tree.

Return type:

matplotlib.axes.Axes

lightgbm.create_tree_digraph(booster, tree_index=0, show_info=None, precision=None, old_name=None, old_comment=None, old_filename=None, old_directory=None, old_format=None, old_engine=None, old_encoding=None, old_graph_attr=None, old_node_attr=None, old_edge_attr=None, old_body=None, old_strict=False, **kwargs)[source]

Create a digraph representation of specified tree.

Note

For more information please visit https://graphviz.readthedocs.io/en/stable/api.html#digraph.

Parameters:
  • booster (Booster or LGBMModel) – Booster or LGBMModel instance to be converted.
  • tree_index (int, optional (default=0)) – The index of a target tree to convert.
  • show_info (list of strings or None, optional (default=None)) – What information should be shown in nodes. Possible values of list items: ‘split_gain’, ‘internal_value’, ‘internal_count’, ‘leaf_count’.
  • precision (int or None, optional (default=None)) – Used to restrict the display of floating point values to a certain precision.
  • **kwargs – Other parameters passed to Digraph constructor. Check https://graphviz.readthedocs.io/en/stable/api.html#digraph for the full list of supported parameters.
Returns:

graph – The digraph representation of specified tree.

Return type:

graphviz.Digraph