Python API Reference

Catalog

• Data Structure API
  • Dataset
  • Booster
• Training API
  • train
  • cv
• Scikit-learn API
  • Common Methods
  • Common Attributes
  • LGBMClassifier
  • LGBMRegressor
  • LGBMRanker
• Callbacks
  • Before iteration
    • reset_parameter
  • After iteration
    • print_evaluation
    • record_evaluation
    • early_stopping
• Plotting

The methods of each Class is in alphabetical order.

---

Basic Data Structure API

Dataset

__init__(data, label=None, max_bin=255, reference=None, weight=None, group=None, silent=False, feature_name=’auto’, categorical_feature=’auto’, params=None, free_raw_data=True)

Parameters
----------
data : str/numpy array/scipy.sparse
    Data source of Dataset.
    When data type is string, it represents the path of txt file
label : list or numpy 1-D array, optional
    Label of the data
max_bin : int, required
    Max number of discrete bin for features
reference : Other Dataset, optional
    If this dataset validation, need to use training data as reference
weight : list or numpy 1-D array, optional
    Weight for each instance.
group : list or numpy 1-D array, optional
    Group/query size for dataset
silent : boolean, optional
    Whether print messages during construction
feature_name : list of str, or 'auto'
    Feature names
    If 'auto' and data is pandas DataFrame, use data columns name
categorical_feature : list of str or int, or 'auto'
    Categorical features,
    type int represents index,
    type str represents feature names (need to specify feature_name as well)
    If 'auto' and data is pandas DataFrame, use pandas categorical columns
params : dict, optional
    Other parameters
free_raw_data : Bool
    True if need to free raw data after construct inner dataset

create_valid(data, label=None, weight=None, group=None, silent=False, params=None)

Create validation data align with current dataset.

Parameters
----------
data : str/numpy array/scipy.sparse
    Data source of _InnerDataset.
    When data type is string, it represents the path of txt file
label : list or numpy 1-D array, optional
    Label of the training data.
weight : list or numpy 1-D array, optional
    Weight for each instance.
group : list or numpy 1-D array, optional
    Group/query size for dataset
silent : boolean, optional
    Whether print messages during construction
params : dict, optional
    Other parameters

get_group()

Get the initial score of the Dataset.

Returns
-------
init_score : array

get_init_score()

Get the initial score of the Dataset.

Returns
-------
init_score : array

get_label()

Get the label of the Dataset.

Returns
-------
label : array

get_weight()

Get the weight of the Dataset.

Returns
-------
weight : array

num_data()

Get the number of rows in the Dataset.

Returns
-------
number of rows : int

num_feature()

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

Returns
-------
number of columns : int

save_binary(filename)

Save Dataset to binary file.

Parameters
----------
filename : str
    Name of the output file.

set_categorical_feature(categorical_feature)

Set categorical features.

Parameters
----------
categorical_feature : list of str or list of int
    Name (str) or index (int) of categorical features

set_feature_name(feature_name)

Set feature name.

Parameters
----------
feature_name : list of str
    Feature names

set_group(group)

Set group size of Dataset (used for ranking).

Parameters
----------
group : numpy array or list or None
    Group size of each group

set_init_score(init_score)

Set init score of booster to start from.

Parameters
----------
init_score : numpy array or list or None
    Init score for booster

set_label(label)

Set label of Dataset.

Parameters
----------
label : numpy array or list or None
    The label information to be set into Dataset

set_reference(reference)

Set reference dataset.

Parameters
----------
reference : Dataset
    Will use reference as template to consturct current dataset

set_weight(weight)

Set weight of each instance.

Parameters
----------
weight : numpy array or list or None
    Weight for each data point

subset(used_indices, params=None)

Get subset of current dataset.

Parameters
----------
used_indices : list of int
    Used indices of this subset
params : dict
    Other parameters

Booster

__init__(params=None, train_set=None, model_file=None, silent=False)

Initialize the Booster.

Parameters
----------
params : dict
    Parameters for boosters.
train_set : Dataset
    Training dataset
model_file : str
    Path to the model file.
silent : boolean, optional
    Whether print messages during construction

add_valid(data, name)

Add an validation data.

Parameters
----------
data : Dataset
    Validation data
name : str
    Name of validation data

attr(key)

Get attribute string from the Booster.

Parameters
----------
key : str
    The key to get attribute from.

Returns
-------
value : str
    The attribute value of the key, returns None if attribute do not exist.

current_iteration()

Get current number of iterations.

Returns
-------
result : int
    Current number of iterations

dump_model()

Dump model to json format.

Returns
-------
result : dict or list
    Json format of model

eval(data, name, feval=None)

Evaluate for data.

Parameters
----------
data : _InnerDataset object
name :
    Name of data
feval : function
    Custom evaluation function.
Returns
-------
result : list
    Evaluation result list.

eval_train(feval=None)

Evaluate for training data.

Parameters
----------
feval : function
    Custom evaluation function.

Returns
-------
result: str
    Evaluation result list.

eval_valid(feval=None)

Evaluate for validation data.

Parameters
----------
feval : function
    Custom evaluation function.

Returns
-------
result : str
    Evaluation result list.

feature_name()

Get feature names.

Returns
-------
result : array
    Array of feature names.

feature_importance(importance_type=”split”)

Get feature importances.

Parameters
----------
importance_type : str, default "split"
How the importance is calculated: "split" or "gain"
"split" is the number of times a feature is used in a model
"gain" is the total gain of splits which use the feature

Returns
-------
result : array
    Array of feature importances.

predict(data, num_iteration=-1, raw_score=False, pred_leaf=False, data_has_header=False, is_reshape=True)

Predict logic.

Parameters
----------
data : str/numpy array/scipy.sparse
    Data source for prediction
    When data type is string, it represents the path of txt file
num_iteration : int
    Used iteration for prediction
raw_score : bool
    True for predict raw score
pred_leaf : bool
    True for predict leaf index
data_has_header : bool
    Used for txt data
is_reshape : bool
    Reshape to (nrow, ncol) if true

Returns
-------
Prediction result

reset_parameter(params)

Reset parameters for booster.

Parameters
----------
params : dict
    New parameters for boosters
silent : boolean, optional
    Whether print messages during construction

rollback_one_iter()

Rollback one iteration.

save_model(filename, num_iteration=-1)

Save model of booster to file.

Parameters
----------
filename : str
    Filename to save
num_iteration : int
    Number of iteration that want to save. < 0 means save all

set_attr(**kwargs)

Set the attribute of the Booster.

Parameters
----------
**kwargs
    The attributes to set. Setting a value to None deletes an attribute.

set_train_data_name(name)

Set training data name.

Parameters
----------
name : str
    Name of training data.

update(train_set=None, fobj=None)

Update for one iteration.
Note: 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.

Parameters
----------
train_set :
    Training data, None means use last training data
fobj : function
    Customized objective function.

Returns
-------
is_finished, bool

Training API

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, callbacks=None)

Train with given parameters.

Parameters
----------
params : dict
    Parameters for training.
train_set : Dataset
    Data to be trained.
num_boost_round: int
    Number of boosting iterations.
valid_sets: list of Datasets
    List of data to be evaluated during training
valid_names: list of str
    Names of valid_sets
fobj : function
    Customized objective function.
feval : function
    Customized evaluation function.
    Note: should return (eval_name, eval_result, is_higher_better) of list of this
init_model : file name of lightgbm model or 'Booster' instance
    model used for continued train
feature_name : list of str, or 'auto'
    Feature names
    If 'auto' and data is pandas DataFrame, use data columns name
categorical_feature : list of str or int, or 'auto'
    Categorical features,
    type int represents index,
    type str represents feature names (need to specify feature_name as well)
    If 'auto' and data is pandas DataFrame, use pandas categorical columns
early_stopping_rounds: int
    Activates early stopping.
    Requires at least one validation data and one metric
    If there's more than one, will check all of them
    Returns the model with (best_iter + early_stopping_rounds)
    If early stopping occurs, the model will add 'best_iteration' field
evals_result: dict or None
    This dictionary used to store all evaluation results of all the items in valid_sets.
    Example: with a valid_sets containing [valid_set, train_set]
             and valid_names containing ['eval', 'train']
             and a paramater containing ('metric':'logloss')
    Returns: {'train': {'logloss': ['0.48253', '0.35953', ...]},
              'eval': {'logloss': ['0.480385', '0.357756', ...]}}
    passed with None means no using this function
verbose_eval : bool or int
    Requires at least one item in evals.
    If `verbose_eval` is True,
        the eval metric on the valid set is printed at each boosting stage.
    If `verbose_eval` is 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 evals,
        an evaluation metric is printed every 4 (instead of 1) boosting stages.
learning_rates : list or function
    List of learning rate 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)
    - list l: learning_rate = l[current_round]
    - function f: learning_rate = f(current_round)
callbacks : list of callback functions
    List of callback functions that are applied at each iteration.
    See Callbacks in Python-API.md for more information.

Returns
-------
booster : a trained booster model

cv(params, train_set, num_boost_round=10, folds=None, nfold=5, stratified=False, 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)

Cross-validation with given paramaters.

Parameters
----------
params : dict
    Booster params.
train_set : Dataset
    Data to be trained.
num_boost_round : int
    Number of boosting iterations.
folds : a generator or iterator of (train_idx, test_idx) tuples
    The train indices and test indices for each folds.
    This argument has highest priority over other data split arguments.
nfold : int
    Number of folds in CV.
stratified : bool
    Perform stratified sampling.
shuffle: bool
    Whether shuffle before split data.
metrics : str or list of str, default None
    Evaluation metrics to be watched in CV.
    If `metrics` is not None, the metric in `params` will be overridden.
fobj : function
    Custom objective function.
feval : function
    Custom evaluation function.
init_model : file name of lightgbm model or 'Booster' instance
    model used for continued train
feature_name : list of str, or 'auto'
    Feature names
    If 'auto' and data is pandas DataFrame, use data columns name
categorical_feature : list of str or int, or 'auto'
    Categorical features,
    type int represents index,
    type str represents feature names (need to specify feature_name as well)
    If 'auto' and data is pandas DataFrame, use pandas categorical columns
early_stopping_rounds: int
    Activates early stopping. CV error needs to decrease at least
    every <early_stopping_rounds> round(s) to continue.
    Last entry in evaluation history is the one from best iteration.
fpreproc : function
    Preprocessing function that takes (dtrain, dtest, param)
    and returns transformed versions of those.
verbose_eval : bool, int, or None, 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 boosting stage.
    If an integer is given,
        progress will be displayed at every given `verbose_eval` boosting stage.
show_stdv : bool, default True
    Whether to display the standard deviation in progress.
    Results are not affected, and always contains std.
seed : int
    Seed used to generate the folds (passed to numpy.random.seed).
callbacks : list of callback functions
    List of callback functions that are applied at end of each iteration.

Returns
-------
evaluation history : list of str

Scikit-learn API

Common Methods

__init__(boosting_type=”gbdt”, num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=10, max_bin=255, subsample_for_bin=50000, objective=”regression”, min_split_gain=0, min_child_weight=5, min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=1, reg_alpha=0, reg_lambda=0, scale_pos_weight=1, is_unbalance=False, seed=0, nthread=-1, silent=True, sigmoid=1.0, huber_delta=1.0, gaussian_eta=1.0, fair_c=1.0, poisson_max_delta_step=0.7, max_position=20, label_gain=None, drop_rate=0.1, skip_drop=0.5, max_drop=50, uniform_drop=False, xgboost_dart_mode=False)

Implementation of the Scikit-Learn API for LightGBM.

Parameters
----------
boosting_type : str
    gbdt, traditional Gradient Boosting Decision Tree
    dart, Dropouts meet Multiple Additive Regression Trees
num_leaves : int
    Maximum tree leaves for base learners.
max_depth : int
    Maximum tree depth for base learners, -1 means no limit.
learning_rate : float
    Boosting learning rate
n_estimators : int
    Number of boosted trees to fit.
max_bin : int
    Number of bucketed bin for feature values
subsample_for_bin : int
    Number of samples for constructing bins.
objective : str or callable
    Specify the learning task and the corresponding learning objective or
    a custom objective function to be used (see note below).
    default: binary for LGBMClassifier, regression for LGBMRegressor, lambdarank for LGBMRanker
min_split_gain : float
    Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight : int
    Minimum sum of instance weight(hessian) needed in a child(leaf)
min_child_samples : int
    Minimum number of data need in a child(leaf)
subsample : float
    Subsample ratio of the training instance.
subsample_freq : int
    frequence of subsample, <=0 means no enable
colsample_bytree : float
    Subsample ratio of columns when constructing each tree.
reg_alpha : float
    L1 regularization term on weights
reg_lambda : float
    L2 regularization term on weights
scale_pos_weight : float
    Balancing of positive and negative weights.
is_unbalance : bool
    Is unbalance for binary classification
seed : int
    Random number seed.
nthread : int
    Number of parallel threads
silent : boolean
    Whether to print messages while running boosting.
sigmoid : float
    Only used in binary classification and lambdarank. Parameter for sigmoid function.
huber_delta : float
    Only used in regression. Parameter for Huber loss function.
gaussian_eta : float
    Only used in regression. Parameter for L1 and Huber loss function.
    It is used to control the width of Gaussian function to approximate hessian.
fair_c : float
    Only used in regression. Parameter for Fair loss function.
poisson_max_delta_step : float
    parameter used to safeguard optimization in Poisson regression.
max_position : int
    Only used in lambdarank, will optimize NDCG at this position.
label_gain : list of float
    Only used in lambdarank, relevant gain for labels.
    For example, the gain of label 2 is 3 if using default label gains.
    None (default) means use default value of CLI version: {0,1,3,7,15,31,63,...}.
drop_rate : float
    Only used when boosting_type='dart'. Probablity to select dropping trees.
skip_drop : float
    Only used when boosting_type='dart'. Probablity to skip dropping trees.
max_drop : int
    Only used when boosting_type='dart'. Max number of dropped trees in one iteration.
uniform_drop : bool
    Only used when boosting_type='dart'. If true, drop trees uniformly, else drop according to weights.
xgboost_dart_mode : bool
    Only used when boosting_type='dart'. Whether use xgboost dart mode.

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_class]
        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_class]
        The value of the gradient for each sample point.
    hess: array_like of shape [n_samples] or shape[n_samples * n_class]
        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

apply(X, num_iteration=0)

Return the predicted leaf every tree for each sample.

Parameters
----------
X : array_like, shape=[n_samples, n_features]
    Input features matrix.

num_iteration : int
    Limit number of iterations in the prediction; defaults to 0 (use all trees).

Returns
-------
X_leaves : array_like, shape=[n_samples, n_trees]

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, early_stopping_rounds=None, verbose=True, feature_name=’auto’, categorical_feature=’auto’, callbacks=None)

Fit the gradient boosting model.

Parameters
----------
X : array_like
    Feature matrix
y : array_like
    Labels
sample_weight : array_like
    weight of training data
init_score : array_like
    init score of training data
group : array_like
    group data of training data
eval_set : list, optional
    A list of (X, y) tuple pairs to use as a validation set for early-stopping
eval_names: list of string
    Names of eval_set
eval_sample_weight : list or dict of array
    weight of eval data; if you use dict, the index should start from 0
eval_init_score : list or dict of array
    init score of eval data; if you use dict, the index should start from 0
eval_group : list or dict of array
    group data of eval data; if you use dict, the index should start from 0
eval_metric : str, list of str, callable, optional
    If a str, should be a built-in evaluation metric to use.
    If callable, a custom evaluation metric, see note for more details.
    default: logloss for LGBMClassifier, l2 for LGBMRegressor, ndcg for LGBMRanker
    Can directly use 'logloss' or 'error' for LGBMClassifier.
early_stopping_rounds : int
verbose : bool
    If `verbose` and an evaluation set is used, writes the evaluation
feature_name : list of str, or 'auto'
    Feature names
    If 'auto' and data is pandas DataFrame, use data columns name
categorical_feature : list of str or int, or 'auto'
    Categorical features,
    type int represents index,
    type str represents feature names (need to specify feature_name as well)
    If 'auto' and data is pandas DataFrame, use pandas categorical columns
callbacks : list of callback functions
    List of callback functions that are applied at each iteration.
    See Callbacks in Python-API.md for more information.

Note
----
Custom eval function expects a callable with following functions:
    ``func(y_true, y_pred)``, ``func(y_true, y_pred, weight)``
        or ``func(y_true, y_pred, weight, group)``.
    return (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_class] (for multi-class)
        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: str
        name of evaluation
    eval_result: float
        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]

predict(X, raw_score=False, num_iteration=0)

Return the predicted value for each sample.

Parameters
----------
X : array_like, shape=[n_samples, n_features]
    Input features matrix.

num_iteration : int
    Limit number of iterations in the prediction; defaults to 0 (use all trees).

Returns
-------
predicted_result : array_like, shape=[n_samples] or [n_samples, n_classes]

Common Attributes

booster_

Get the underlying lightgbm Booster of this model.

evals_result_

Get the evaluation results.

feature_importances_

Get normailized feature importances.

LGBMClassifier

predict_proba(X, raw_score=False, num_iteration=0)

Return the predicted probability for each class for each sample.

Parameters
----------
X : array_like, shape=[n_samples, n_features]
    Input features matrix.

num_iteration : int
    Limit number of iterations in the prediction; defaults to 0 (use all trees).

Returns
-------
predicted_probability : array_like, shape=[n_samples, n_classes]

classes_

Get class label array.

n_classes_

Get number of classes.

LGBMRegressor

LGBMRanker

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=’ndcg’, eval_at=1, early_stopping_rounds=None, verbose=True, feature_name=’auto’, categorical_feature=’auto’, callbacks=None)

Most arguments are same as Common Methods except:

eval_at : int or list of int, default=1
    The evaulation positions of NDCG

Callbacks

Before iteration

reset_parameter(**kwargs)

Reset parameter after 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 learning_rate in terms of
    current number of round (e.g. yields learning rate decay)
    - list l: parameter = l[current_round]
    - function f: parameter = f(current_round)
Returns
-------
callback : function
    The requested callback function.

After iteration

print_evaluation(period=1, show_stdv=True)

Create a callback that print evaluation result.
(Same function as `verbose_eval` in lightgbm.train())

Parameters
----------
period : int
    The period to log the evaluation results

show_stdv : bool, optional
    Whether show standard deviation if provided

Returns
-------
callback : function
    A callback that prints evaluation every period iterations.

record_evaluation(eval_result)

Create a call back that records the evaluation history into eval_result.
(Same function as `evals_result` in lightgbm.train())

Parameters
----------
eval_result : dict
   A dictionary to store the evaluation results.

Returns
-------
callback : function
    The requested callback function.

early_stopping(stopping_rounds, verbose=True)

Create a callback that activates early stopping.
To activates early stopping, at least one validation data and one metric is required.
If there's more than one, all of them will be checked.
(Same function as `early_stopping_rounds` in lightgbm.train())

Parameters
----------
stopping_rounds : int
   The stopping rounds before the trend occur.

verbose : optional, bool
    Whether to print message about early stopping information.

Returns
-------
callback : function
    The requested callback function.

Plotting

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, **kwargs):

Plot model feature importances.

Parameters
----------
booster : Booster or LGBMModel
    Booster or LGBMModel instance.
ax : matplotlib Axes
    Target axes instance. If None, new figure and axes will be created.
height : float
    Bar height, passed to ax.barh().
xlim : tuple of 2 elements
    Tuple passed to axes.xlim().
ylim : tuple of 2 elements
    Tuple passed to axes.ylim().
title : str
    Axes title. Pass None to disable.
xlabel : str
    X axis title label. Pass None to disable.
ylabel : str
    Y axis title label. Pass None to disable.
importance_type : str
    How the importance is calculated: "split" or "gain".
    "split" is the number of times a feature is used in a model.
    "gain" is the total gain of splits which use the feature.
max_num_features : int
    Max number of top features displayed on plot.
    If None or smaller than 1, all features will be displayed.
ignore_zero : bool
    Ignore features with zero importance.
figsize : tuple of 2 elements
    Figure size.
grid : bool
    Whether add grid for axes.
**kwargs :
    Other keywords passed to ax.barh().

Returns
-------
ax : matplotlib Axes

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):

Plot one metric during training.

Parameters
----------
booster : dict or LGBMModel
    Evals_result recorded by lightgbm.train() or LGBMModel instance
metric : str or None
    The metric name to plot.
    Only one metric supported because different metrics have various scales.
    Pass None to pick `first` one (according to dict hashcode).
dataset_names : None or list of str
    List of the dataset names to plot.
    Pass None to plot all datasets.
ax : matplotlib Axes
    Target axes instance. If None, new figure and axes will be created.
xlim : tuple of 2 elements
    Tuple passed to axes.xlim()
ylim : tuple of 2 elements
    Tuple passed to axes.ylim()
title : str
    Axes title. Pass None to disable.
xlabel : str
    X axis title label. Pass None to disable.
ylabel : str
    Y axis title label. Pass None to disable. Pass 'auto' to use `metric`.
figsize : tuple of 2 elements
    Figure size
grid : bool
    Whether add grid for axes

Returns
-------
ax : matplotlib Axes

plot_tree(booster, ax=None, tree_index=0, figsize=None, graph_attr=None, node_attr=None, edge_attr=None, show_info=None):

Plot specified tree.

Parameters
----------
booster : Booster, LGBMModel
    Booster or LGBMModel instance.
ax : matplotlib Axes
    Target axes instance. If None, new figure and axes will be created.
tree_index : int, default 0
    Specify tree index of target tree.
figsize : tuple of 2 elements
    Figure size.
graph_attr: dict
    Mapping of (attribute, value) pairs for the graph.
node_attr: dict
    Mapping of (attribute, value) pairs set for all nodes.
edge_attr: dict
    Mapping of (attribute, value) pairs set for all edges.
show_info : list
    Information shows on nodes.
    options: 'split_gain', 'internal_value', 'internal_count' or 'leaf_count'.

Returns
-------
ax : matplotlib Axes

create_tree_digraph(booster, tree_index=0, show_info=None, name=None, comment=None, filename=None, directory=None, format=None, engine=None, encoding=None, graph_attr=None, node_attr=None, edge_attr=None, body=None, strict=False):

Create a digraph of specified tree.

See:
  - http://graphviz.readthedocs.io/en/stable/api.html#digraph

Parameters
----------
booster : Booster, LGBMModel
    Booster or LGBMModel instance.
tree_index : int, default 0
    Specify tree index of target tree.
show_info : list
    Information shows on nodes.
    options: 'split_gain', 'internal_value', 'internal_count' or 'leaf_count'.
name : str
    Graph name used in the source code.
comment : str
    Comment added to the first line of the source.
filename : str
    Filename for saving the source (defaults to name + '.gv').
directory : str
    (Sub)directory for source saving and rendering.
format : str
    Rendering output format ('pdf', 'png', ...).
engine : str
    Layout command used ('dot', 'neato', ...).
encoding : str
    Encoding for saving the source.
graph_attr : dict
    Mapping of (attribute, value) pairs for the graph.
node_attr : dict
    Mapping of (attribute, value) pairs set for all nodes.
edge_attr : dict
    Mapping of (attribute, value) pairs set for all edges.
body : list of str
    Iterable of lines to add to the graph body.
strict : bool
    Iterable of lines to add to the graph body.

Returns
-------
graph : graphviz Digraph