lightgbm.Booster

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

Bases: object

Booster in LightGBM.

__init__(params=None, train_set=None, model_file=None, model_str=None, silent=False)[source]

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.

  • model_str (string or None, optional (default=None)) – Model will be loaded from this string.

  • silent (bool, optional (default=False)) – Whether to print messages during construction.

Methods

__init__([params, train_set, model_file, …])

Initialize the Booster.

add_valid(data, name)

Add validation data.

attr(key)

Get attribute string from the Booster.

current_iteration()

Get the index of the current iteration.

dump_model([num_iteration, start_iteration, …])

Dump Booster to JSON format.

eval(data, name[, feval])

Evaluate for data.

eval_train([feval])

Evaluate for training data.

eval_valid([feval])

Evaluate for validation data.

feature_importance([importance_type, iteration])

Get feature importances.

feature_name()

Get names of features.

free_dataset()

Free Booster’s Datasets.

free_network()

Free Booster’s network.

get_leaf_output(tree_id, leaf_id)

Get the output of a leaf.

get_split_value_histogram(feature[, bins, …])

Get split value histogram for the specified feature.

lower_bound()

Get lower bound value of a model.

model_from_string(model_str[, verbose])

Load Booster from a string.

model_to_string([num_iteration, …])

Save Booster to string.

num_feature()

Get number of features.

num_model_per_iteration()

Get number of models per iteration.

num_trees()

Get number of weak sub-models.

predict(data[, start_iteration, …])

Make a prediction.

refit(data, label[, decay_rate])

Refit the existing Booster by new data.

reset_parameter(params)

Reset parameters of Booster.

rollback_one_iter()

Rollback one iteration.

save_model(filename[, num_iteration, …])

Save Booster to file.

set_attr(**kwargs)

Set attributes to the Booster.

set_network(machines[, local_listen_port, …])

Set the network configuration.

set_train_data_name(name)

Set the name to the training Dataset.

shuffle_models([start_iteration, end_iteration])

Shuffle models.

trees_to_dataframe()

Parse the fitted model and return in an easy-to-read pandas DataFrame.

update([train_set, fobj])

Update Booster for one iteration.

upper_bound()

Get upper bound value of a model.

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, importance_type='split')[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.

  • importance_type (string, optional (default="split")) – What type of feature importance should be dumped. 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.

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, eval_data, and return (eval_name, eval_result, is_higher_better) or list of such tuples.

    predslist or numpy 1-D array

    The predicted values.

    eval_dataDataset

    The evaluation dataset.

    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 preds is probability of positive class (or margin in case of specified fobj). 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.

predslist or numpy 1-D array

The predicted values.

train_dataDataset

The training dataset.

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 preds is probability of positive class (or margin in case of specified fobj). 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, valid_data, and return (eval_name, eval_result, is_higher_better) or list of such tuples.

predslist or numpy 1-D array

The predicted values.

valid_dataDataset

The validation dataset.

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 preds is probability of positive class (or margin in case of specified fobj). 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

get_split_value_histogram(feature, bins=None, xgboost_style=False)[source]

Get split value histogram for the specified feature.

Parameters
  • feature (int or string) –

    The feature name or index the histogram is calculated for. If int, interpreted as index. If string, interpreted as name.

    Warning

    Categorical features are not supported.

  • bins (int, string or None, optional (default=None)) – The maximum number of bins. If None, or int and > number of unique split values and xgboost_style=True, the number of bins equals number of unique split values. If string, it should be one from the list of the supported values by numpy.histogram() function.

  • xgboost_style (bool, optional (default=False)) – Whether the returned result should be in the same form as it is in XGBoost. If False, the returned value is tuple of 2 numpy arrays as it is in numpy.histogram() function. If True, the returned value is matrix, in which the first column is the right edges of non-empty bins and the second one is the histogram values.

Returns

  • result_tuple (tuple of 2 numpy arrays) – If xgboost_style=False, the values of the histogram of used splitting values for the specified feature and the bin edges.

  • result_array_like (numpy array or pandas DataFrame (if pandas is installed)) – If xgboost_style=True, the histogram of used splitting values for the specified feature.

lower_bound()[source]

Get lower bound value of a model.

Returns

lower_bound – Lower bound value of the model.

Return type

double

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, importance_type='split')[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.

  • importance_type (string, optional (default="split")) – What type of feature importance should be saved. 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.

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, start_iteration=0, 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, H2O DataTable's Frame or scipy.sparse) – Data source for prediction. If string, it represents the path to txt file.

  • start_iteration (int, optional (default=0)) – Start index of the iteration to predict. If <= 0, starts from the first iteration.

  • num_iteration (int or None, optional (default=None)) – Total number of iterations used in the prediction. If None, if the best iteration exists and start_iteration <= 0, the best iteration is used; otherwise, all iterations from start_iteration are used (no limits). If <= 0, all iterations from start_iteration 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 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.

  • 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. Can be sparse or a list of sparse objects (each element represents predictions for one class) for feature contributions (when pred_contrib=True).

Return type

numpy array, scipy.sparse or list of scipy.sparse

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

Refit the existing Booster by new data.

Parameters
  • data (string, numpy array, pandas DataFrame, H2O DataTable's Frame 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, importance_type='split')[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.

  • importance_type (string, optional (default="split")) – What type of feature importance should be saved. 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.

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

trees_to_dataframe()[source]

Parse the fitted model and return in an easy-to-read pandas DataFrame.

Returns

result – Returns a pandas DataFrame of the parsed model.

Return type

pandas DataFrame

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. Should accept two parameters: preds, train_data, and return (grad, hess).

    predslist or numpy 1-D array

    The predicted values.

    train_dataDataset

    The training dataset.

    gradlist or numpy 1-D array

    The value of the first order derivative (gradient) for each sample point.

    hesslist or numpy 1-D array

    The value of the second order derivative (Hessian) for each sample point.

    For binary task, the preds is probability of positive class (or margin in case of specified fobj). 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 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

upper_bound()[source]

Get upper bound value of a model.

Returns

upper_bound – Upper bound value of the model.

Return type

double