Python API Reference

This page gives the Python API reference of xgboost, please also refer to Python Package Introduction for more information about python package.

The document in this page is automatically generated by sphinx. The content do not render at github, you can view it at http://xgboost.readthedocs.org/en/latest/python/python_api.html

Core Data Structure

Core XGBoost Library.

class xgboost.DMatrix(data, label=None, missing=None, weight=None, silent=False, feature_names=None, feature_types=None)

Bases: object

Data Matrix used in XGBoost.

DMatrix is a internal data structure that used by XGBoost which is optimized for both memory efficiency and training speed. You can construct DMatrix from numpy.arrays

feature_names

Get feature names (column labels).

Returns:feature_names
Return type:list or None
feature_types

Get feature types (column types).

Returns:feature_types
Return type:list or None
get_base_margin()

Get the base margin of the DMatrix.

Returns:base_margin
Return type:float
get_float_info(field)

Get float property from the DMatrix.

Parameters:field (str) – The field name of the information
Returns:info – a numpy array of float information of the data
Return type:array
get_label()

Get the label of the DMatrix.

Returns:label
Return type:array
get_uint_info(field)

Get unsigned integer property from the DMatrix.

Parameters:field (str) – The field name of the information
Returns:info – a numpy array of float information of the data
Return type:array
get_weight()

Get the weight of the DMatrix.

Returns:weight
Return type:array
num_col()

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

Returns:number of columns
Return type:int
num_row()

Get the number of rows in the DMatrix.

Returns:number of rows
Return type:int
save_binary(fname, silent=True)

Save DMatrix to an XGBoost buffer.

Parameters:
  • fname (string) – Name of the output buffer file.
  • silent (bool (optional; default: True)) – If set, the output is suppressed.
set_base_margin(margin)

Set base margin of booster to start from.

This can be used to specify a prediction value of existing model to be base_margin However, remember margin is needed, instead of transformed prediction e.g. for logistic regression: need to put in value before logistic transformation see also example/demo.py

Parameters:margin (array like) – Prediction margin of each datapoint
set_float_info(field, data)

Set float type property into the DMatrix.

Parameters:
  • field (str) – The field name of the information
  • data (numpy array) – The array of data to be set
set_group(group)

Set group size of DMatrix (used for ranking).

Parameters:group (array like) – Group size of each group
set_label(label)

Set label of dmatrix

Parameters:label (array like) – The label information to be set into DMatrix
set_uint_info(field, data)

Set uint type property into the DMatrix.

Parameters:
  • field (str) – The field name of the information
  • data (numpy array) – The array of data to be set
set_weight(weight)

Set weight of each instance.

Parameters:weight (array like) – Weight for each data point
slice(rindex)

Slice the DMatrix and return a new DMatrix that only contains rindex.

Parameters:rindex (list) – List of indices to be selected.
Returns:res – A new DMatrix containing only selected indices.
Return type:DMatrix
class xgboost.Booster(params=None, cache=(), model_file=None)

Bases: object

“A Booster of of XGBoost.

Booster is the model of xgboost, that contains low level routines for training, prediction and evaluation.

attr(key)

Get attribute string from the Booster.

Parameters:key (str) – The key to get attribute from.
Returns:value – The attribute value of the key, returns None if attribute do not exist.
Return type:str
attributes()

Get attributes stored in the Booster as a dictionary.

Returns:result – Returns an empty dict if there’s no attributes.
Return type:dictionary of attribute_name: attribute_value pairs of strings.
boost(dtrain, grad, hess)

Boost the booster for one iteration, with customized gradient statistics.

Parameters:
  • dtrain (DMatrix) – The training DMatrix.
  • grad (list) – The first order of gradient.
  • hess (list) – The second order of gradient.
copy()

Copy the booster object.

Returns:booster – a copied booster model
Return type:Booster
dump_model(fout, fmap='', with_stats=False)

Dump model into a text file.

Parameters:
  • foout (string) – Output file name.
  • fmap (string, optional) – Name of the file containing feature map names.
  • with_stats (bool (optional)) – Controls whether the split statistics are output.
eval(data, name='eval', iteration=0)

Evaluate the model on mat.

Parameters:
  • data (DMatrix) – The dmatrix storing the input.
  • name (str, optional) – The name of the dataset.
  • iteration (int, optional) – The current iteration number.
Returns:

result – Evaluation result string.

Return type:

str

eval_set(evals, iteration=0, feval=None)

Evaluate a set of data.

Parameters:
  • evals (list of tuples (DMatrix, string)) – List of items to be evaluated.
  • iteration (int) – Current iteration.
  • feval (function) – Custom evaluation function.
Returns:

result – Evaluation result string.

Return type:

str

get_dump(fmap='', with_stats=False, dump_format='text')

Returns the dump the model as a list of strings.

get_fscore(fmap='')

Get feature importance of each feature.

Parameters:fmap (str (optional)) – The name of feature map file
get_score(fmap='', importance_type='weight')

Get feature importance of each feature. Importance type can be defined as:

‘weight’ - the number of times a feature is used to split the data across all trees. ‘gain’ - the average gain of the feature when it is used in trees ‘cover’ - the average coverage of the feature when it is used in trees
Parameters:fmap (str (optional)) – The name of feature map file
get_split_value_histogram(feature, fmap='', bins=None, as_pandas=True)

Get split value histogram of a feature :param feature: The name of the feature. :type feature: str :param fmap: The name of feature map file. :type fmap: str (optional) :param bin: The maximum number of bins.

Number of bins equals number of unique split values n_unique, if bins == None or bins > n_unique.
Parameters:as_pandas (bool, default True) – Return pd.DataFrame when pandas is installed. If False or pandas is not installed, return numpy ndarray.
Returns:
  • a histogram of used splitting values for the specified feature
  • either as numpy array or pandas DataFrame.
load_model(fname)

Load the model from a file.

Parameters:fname (string or a memory buffer) – Input file name or memory buffer(see also save_raw)
load_rabit_checkpoint()

Initialize the model by load from rabit checkpoint.

Returns:version – The version number of the model.
Return type:integer
predict(data, output_margin=False, ntree_limit=0, pred_leaf=False, pred_contribs=False)

Predict with data.

NOTE: This function is not thread safe.
For each booster object, predict can only be called from one thread. If you want to run prediction using multiple thread, call bst.copy() to make copies of model object and then call predict
Parameters:
  • data (DMatrix) – The dmatrix storing the input.
  • output_margin (bool) – Whether to output the raw untransformed margin value.
  • ntree_limit (int) – Limit number of trees in the prediction; defaults to 0 (use all trees).
  • pred_leaf (bool) – When this option is on, the output will be a matrix of (nsample, ntrees) with each record indicating the predicted leaf index of each sample in each tree. Note that the leaf index of a tree is unique per tree, so you may find leaf 1 in both tree 1 and tree 0.
  • pred_contribs (bool) – When this option is on, the output will be a matrix of (nsample, nfeats+1) with each record indicating the feature contributions of all trees. The sum of all feature contributions is equal to the prediction. Note that the bias is added as the final column, on top of the regular features.
Returns:

prediction

Return type:

numpy array

save_model(fname)

Save the model to a file.

Parameters:fname (string) – Output file name
save_rabit_checkpoint()

Save the current booster to rabit checkpoint.

save_raw()

Save the model to a in memory buffer representation

Returns:
Return type:a in memory buffer representation of the model
set_attr(**kwargs)

Set the attribute of the Booster.

Parameters:**kwargs – The attributes to set. Setting a value to None deletes an attribute.
set_param(params, value=None)

Set parameters into the Booster.

Parameters:
  • params (dict/list/str) – list of key,value paris, dict of key to value or simply str key
  • value (optional) – value of the specified parameter, when params is str key
update(dtrain, iteration, fobj=None)

Update for one iteration, with objective function calculated internally.

Parameters:
  • dtrain (DMatrix) – Training data.
  • iteration (int) – Current iteration number.
  • fobj (function) – Customized objective function.

Learning API

Training Library containing training routines.

xgboost.train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, maximize=False, early_stopping_rounds=None, evals_result=None, verbose_eval=True, xgb_model=None, callbacks=None, learning_rates=None)

Train a booster with given parameters.

Parameters:
  • params (dict) – Booster params.
  • dtrain (DMatrix) – Data to be trained.
  • num_boost_round (int) – Number of boosting iterations.
  • evals (list of pairs (DMatrix, string)) – List of items to be evaluated during training, this allows user to watch performance on the validation set.
  • obj (function) – Customized objective function.
  • feval (function) – Customized evaluation function.
  • maximize (bool) – Whether to maximize feval.
  • early_stopping_rounds (int) – Activates early stopping. Validation error needs to decrease at least every <early_stopping_rounds> round(s) to continue training. Requires at least one item in evals. If there’s more than one, will use the last. Returns the model from the last iteration (not the best one). If early stopping occurs, the model will have three additional fields: bst.best_score, bst.best_iteration and bst.best_ntree_limit. (Use bst.best_ntree_limit to get the correct value if num_parallel_tree and/or num_class appears in the parameters)
  • evals_result (dict) –

    This dictionary stores the evaluation results of all the items in watchlist. Example: with a watchlist containing [(dtest,’eval’), (dtrain,’train’)] and a parameter containing (‘eval_metric’: ‘logloss’) Returns: {‘train’: {‘logloss’: [‘0.48253’, ‘0.35953’]},

    ‘eval’: {‘logloss’: [‘0.480385’, ‘0.357756’]}}
  • verbose_eval (bool or int) – Requires at least one item in evals. If verbose_eval is True then the evaluation metric on the validation set is printed at each boosting stage. If verbose_eval is an integer then the evaluation metric on the validation set is printed at every given verbose_eval boosting stage. The last boosting stage / 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 boosting stages, instead of every boosting stage.
  • learning_rates (list or function (deprecated - use callback API instead)) – List of learning rate for each boosting round or a customized function that calculates eta in terms of current number of round and the total number of boosting round (e.g. yields learning rate decay)
  • xgb_model (file name of stored xgb model or 'Booster' instance) – Xgb model to be loaded before training (allows training continuation).
  • callbacks (list of callback functions) – List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using xgb.callback module. Example: [xgb.callback.reset_learning_rate(custom_rates)]
Returns:

booster

Return type:

a trained booster model

xgboost.cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None, metrics=(), obj=None, feval=None, maximize=False, early_stopping_rounds=None, fpreproc=None, as_pandas=True, verbose_eval=None, show_stdv=True, seed=0, callbacks=None, shuffle=True)

Cross-validation with given parameters.

Parameters:
  • params (dict) – Booster params.
  • dtrain (DMatrix) – Data to be trained.
  • num_boost_round (int) – Number of boosting iterations.
  • nfold (int) – Number of folds in CV.
  • stratified (bool) – Perform stratified sampling.
  • folds (a KFold or StratifiedKFold instance) – Sklearn KFolds or StratifiedKFolds.
  • metrics (string or list of strings) – Evaluation metrics to be watched in CV.
  • obj (function) – Custom objective function.
  • feval (function) – Custom evaluation function.
  • maximize (bool) – Whether to maximize feval.
  • 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.
  • as_pandas (bool, default True) – Return pd.DataFrame when pandas is installed. If False or pandas is not installed, return np.ndarray
  • 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.
    It is possible to use predefined callbacks by using xgb.callback module. Example: [xgb.callback.reset_learning_rate(custom_rates)]
    shuffle : bool
    Shuffle data before creating folds.
Returns:

evaluation history

Return type:

list(string)

Scikit-Learn API

Scikit-Learn Wrapper interface for XGBoost.

class xgboost.XGBRegressor(max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective='reg:linear', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)

Bases: xgboost.sklearn.XGBModel, object

Implementation of the scikit-learn API for XGBoost regression.
Parameters
max_depth : int
Maximum tree depth for base learners.
learning_rate : float
Boosting learning rate (xgb’s “eta”)
n_estimators : int
Number of boosted trees to fit.
silent : boolean
Whether to print messages while running boosting.
objective : string or callable
Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below).
booster: string
Specify which booster to use: gbtree, gblinear or dart.
nthread : int
Number of parallel threads used to run xgboost. (Deprecated, please use n_jobs)
n_jobs : int
Number of parallel threads used to run xgboost. (replaces nthread)
gamma : 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.
max_delta_step : int
Maximum delta step we allow each tree’s weight estimation to be.
subsample : float
Subsample ratio of the training instance.
colsample_bytree : float
Subsample ratio of columns when constructing each tree.
colsample_bylevel : float
Subsample ratio of columns for each split, in each level.
reg_alpha : float (xgb’s alpha)
L1 regularization term on weights
reg_lambda : float (xgb’s lambda)
L2 regularization term on weights
scale_pos_weight : float
Balancing of positive and negative weights.
base_score:
The initial prediction score of all instances, global bias.
seed : int
Random number seed. (Deprecated, please use random_state)
random_state : int
Random number seed. (replaces seed)
missing : float, optional
Value in the data which needs to be present as a missing value. If None, defaults to np.nan.
**kwargs : dict, optional

Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found here: https://github.com/dmlc/xgboost/blob/master/doc/parameter.md. Attempting to set a parameter via the constructor args and **kwargs dict simultaneously will result in a TypeError. Note:

**kwargs is unsupported by Sklearn. We do not guarantee that parameters passed via this argument will interact properly with Sklearn.

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:

y_true: array_like of shape [n_samples]
The target values
y_pred: array_like of shape [n_samples]
The predicted values
grad: array_like of shape [n_samples]
The value of the gradient for each sample point.
hess: array_like of shape [n_samples]
The value of the second derivative for each sample point
class xgboost.XGBClassifier(max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)

Bases: xgboost.sklearn.XGBModel, object

Implementation of the scikit-learn API for XGBoost classification.

Parameters
max_depth : int
Maximum tree depth for base learners.
learning_rate : float
Boosting learning rate (xgb’s “eta”)
n_estimators : int
Number of boosted trees to fit.
silent : boolean
Whether to print messages while running boosting.
objective : string or callable
Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below).
booster: string
Specify which booster to use: gbtree, gblinear or dart.
nthread : int
Number of parallel threads used to run xgboost. (Deprecated, please use n_jobs)
n_jobs : int
Number of parallel threads used to run xgboost. (replaces nthread)
gamma : 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.
max_delta_step : int
Maximum delta step we allow each tree’s weight estimation to be.
subsample : float
Subsample ratio of the training instance.
colsample_bytree : float
Subsample ratio of columns when constructing each tree.
colsample_bylevel : float
Subsample ratio of columns for each split, in each level.
reg_alpha : float (xgb’s alpha)
L1 regularization term on weights
reg_lambda : float (xgb’s lambda)
L2 regularization term on weights
scale_pos_weight : float
Balancing of positive and negative weights.
base_score:
The initial prediction score of all instances, global bias.
seed : int
Random number seed. (Deprecated, please use random_state)
random_state : int
Random number seed. (replaces seed)
missing : float, optional
Value in the data which needs to be present as a missing value. If None, defaults to np.nan.
**kwargs : dict, optional

Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found here: https://github.com/dmlc/xgboost/blob/master/doc/parameter.md. Attempting to set a parameter via the constructor args and **kwargs dict simultaneously will result in a TypeError. Note:

**kwargs is unsupported by Sklearn. We do not guarantee that parameters passed via this argument will interact properly with Sklearn.

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:

y_true: array_like of shape [n_samples]
The target values
y_pred: array_like of shape [n_samples]
The predicted values
grad: array_like of shape [n_samples]
The value of the gradient for each sample point.
hess: array_like of shape [n_samples]
The value of the second derivative for each sample point
evals_result()

Return the evaluation results.

If eval_set is passed to the fit function, you can call evals_result() to get evaluation results for all passed eval_sets. When eval_metric is also passed to the fit function, the evals_result will contain the eval_metrics passed to the fit function

Returns:evals_result
Return type:dictionary

Example

param_dist = {‘objective’:’binary:logistic’, ‘n_estimators’:2}

clf = xgb.XGBClassifier(**param_dist)

clf.fit(X_train, y_train,
eval_set=[(X_train, y_train), (X_test, y_test)], eval_metric=’logloss’, verbose=True)

evals_result = clf.evals_result()

The variable evals_result will contain: {‘validation_0’: {‘logloss’: [‘0.604835’, ‘0.531479’]},

‘validation_1’: {‘logloss’: [‘0.41965’, ‘0.17686’]}}
fit(X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True)

Fit gradient boosting classifier

Parameters:
  • X (array_like) – Feature matrix
  • y (array_like) – Labels
  • sample_weight (array_like) – Weight for each instance
  • eval_set (list, optional) – A list of (X, y) pairs to use as a validation set for early-stopping
  • eval_metric (str, callable, optional) – If a str, should be a built-in evaluation metric to use. See doc/parameter.md. If callable, a custom evaluation metric. The call signature is func(y_predicted, y_true) where y_true will be a DMatrix object such that you may need to call the get_label method. It must return a str, value pair where the str is a name for the evaluation and value is the value of the evaluation function. This objective is always minimized.
  • early_stopping_rounds (int, optional) – Activates early stopping. Validation error needs to decrease at least every <early_stopping_rounds> round(s) to continue training. Requires at least one item in evals. If there’s more than one, will use the last. Returns the model from the last iteration (not the best one). If early stopping occurs, the model will have three additional fields: bst.best_score, bst.best_iteration and bst.best_ntree_limit. (Use bst.best_ntree_limit to get the correct value if num_parallel_tree and/or num_class appears in the parameters)
  • verbose (bool) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr.

Plotting API

Plotting Library.

xgboost.plot_importance(booster, ax=None, height=0.2, xlim=None, ylim=None, title='Feature importance', xlabel='F score', ylabel='Features', importance_type='weight', max_num_features=None, grid=True, **kwargs)

Plot importance based on fitted trees.

Parameters:
  • booster (Booster, XGBModel or dict) – Booster or XGBModel instance, or dict taken by Booster.get_fscore()
  • ax (matplotlib Axes, default None) – Target axes instance. If None, new figure and axes will be created.
  • importance_type (str, default "weight") –

    How the importance is calculated: either “weight”, “gain”, or “cover” “weight” is the number of times a feature appears in a tree “gain” is the average gain of splits which use the feature “cover” is the average coverage of splits which use the feature

    where coverage is defined as the number of samples affected by the split
  • max_num_features (int, default None) – Maximum number of top features displayed on plot. If None, all features will be displayed.
  • height (float, default 0.2) – Bar height, passed to ax.barh()
  • xlim (tuple, default None) – Tuple passed to axes.xlim()
  • ylim (tuple, default None) – Tuple passed to axes.ylim()
  • title (str, default "Feature importance") – Axes title. To disable, pass None.
  • xlabel (str, default "F score") – X axis title label. To disable, pass None.
  • ylabel (str, default "Features") – Y axis title label. To disable, pass None.
  • kwargs – Other keywords passed to ax.barh()
Returns:

ax

Return type:

matplotlib Axes

xgboost.plot_tree(booster, fmap='', num_trees=0, rankdir='UT', ax=None, **kwargs)

Plot specified tree.

Parameters:
  • booster (Booster, XGBModel) – Booster or XGBModel instance
  • fmap (str (optional)) – The name of feature map file
  • num_trees (int, default 0) – Specify the ordinal number of target tree
  • rankdir (str, default "UT") – Passed to graphiz via graph_attr
  • ax (matplotlib Axes, default None) – Target axes instance. If None, new figure and axes will be created.
  • kwargs – Other keywords passed to to_graphviz
Returns:

ax

Return type:

matplotlib Axes

xgboost.to_graphviz(booster, fmap='', num_trees=0, rankdir='UT', yes_color='#0000FF', no_color='#FF0000', **kwargs)

Convert specified tree to graphviz instance. IPython can automatically plot the returned graphiz instance. Otherwise, you should call .render() method of the returned graphiz instance.

Parameters:
  • booster (Booster, XGBModel) – Booster or XGBModel instance
  • fmap (str (optional)) – The name of feature map file
  • num_trees (int, default 0) – Specify the ordinal number of target tree
  • rankdir (str, default "UT") – Passed to graphiz via graph_attr
  • yes_color (str, default '#0000FF') – Edge color when meets the node condition.
  • no_color (str, default '#FF0000') – Edge color when doesn’t meet the node condition.
  • kwargs – Other keywords passed to graphviz graph_attr
Returns:

ax

Return type:

matplotlib Axes