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.

Core Data Structure

Core XGBoost Library.

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

Parameters
  • data (os.PathLike/string/numpy.array/scipy.sparse/pd.DataFrame/) – dt.Frame/cudf.DataFrame Data source of DMatrix. When data is string or os.PathLike type, it represents the path libsvm format txt file, csv file (by specifying uri parameter ‘path_to_csv?format=csv’), or binary file that xgboost can read from.

  • label (list, numpy 1-D array or cudf.DataFrame, optional) – Label of the training data.

  • missing (float, optional) – Value in the input data which needs to be present as a missing value. If None, defaults to np.nan.

  • weight (list, numpy 1-D array or cudf.DataFrame , optional) –

    Weight for each instance.

    Note

    For ranking task, weights are per-group.

    In ranking task, one weight is assigned to each group (not each data point). This is because we only care about the relative ordering of data points within each group, so it doesn’t make sense to assign weights to individual data points.

  • silent (boolean, optional) – Whether print messages during construction

  • feature_names (list, optional) – Set names for features.

  • feature_types (list, optional) – Set types for features.

  • nthread (integer, optional) – Number of threads to use for loading data from numpy array. If -1, uses maximum threads available on the system.

property feature_names

Get feature names (column labels).

Returns

feature_names

Return type

list or None

property 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 unsigned integer 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. Saved binary can be later loaded by providing the path to xgboost.DMatrix() as input.

Parameters
  • fname (string or os.PathLike) – 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_float_info_npy2d(field, data)
Set float type property into the DMatrix

for numpy 2d array input

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_interface_info(field, data)

Set info type peoperty into DMatrix.

set_label(label)

Set label of dmatrix

Parameters

label (array like) – The label information to be set into DMatrix

set_label_npy2d(label)

Set label of dmatrix

Parameters

label (array like) – The label information to be set into DMatrix from numpy 2D array

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

Note

For ranking task, weights are per-group.

In ranking task, one weight is assigned to each group (not each data point). This is because we only care about the relative ordering of data points within each group, so it doesn’t make sense to assign weights to individual data points.

set_weight_npy2d(weight)
Set weight of each instance

for numpy 2D array

Parameters

weight (array like) –

Weight for each data point in numpy 2D array

Note

For ranking task, weights are per-group.

In ranking task, one weight is assigned to each group (not each data point). This is because we only care about the relative ordering of data points within each group, so it doesn’t make sense to assign weights to individual data points.

slice(rindex, allow_groups=False)

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

Parameters
  • rindex (list) – List of indices to be selected.

  • allow_groups (boolean) – Allow slicing of a matrix with a groups attribute

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 XGBoost.

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

Parameters
  • params (dict) – Parameters for boosters.

  • cache (list) – List of cache items.

  • model_file (string or os.PathLike) – Path to the model file.

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. Like xgboost.core.Booster.update(), this function should not be called directly by users.

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_format='text')

Dump model into a text or JSON file.

Parameters
  • fout (string or os.PathLike) – Output file name.

  • fmap (string or os.PathLike, optional) – Name of the file containing feature map names.

  • with_stats (bool, optional) – Controls whether the split statistics are output.

  • dump_format (string, optional) – Format of model dump file. Can be ‘text’ or ‘json’.

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 model dump as a list of strings.

Parameters
  • fmap (string or os.PathLike, optional) – Name of the file containing feature map names.

  • with_stats (bool, optional) – Controls whether the split statistics are output.

  • dump_format (string, optional) – Format of model dump. Can be ‘text’, ‘json’ or ‘dot’.

get_fscore(fmap='')

Get feature importance of each feature.

Note

Feature importance is defined only for tree boosters

Feature importance is only defined when the decision tree model is chosen as base learner (booster=gbtree). It is not defined for other base learner types, such as linear learners (booster=gblinear).

Note

Zero-importance features will not be included

Keep in mind that this function does not include zero-importance feature, i.e. those features that have not been used in any split conditions.

Parameters

fmap (str or os.PathLike (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 across all splits the feature is used in.

  • ‘cover’: the average coverage across all splits the feature is used in.

  • ‘total_gain’: the total gain across all splits the feature is used in.

  • ‘total_cover’: the total coverage across all splits the feature is used in.

Note

Feature importance is defined only for tree boosters

Feature importance is only defined when the decision tree model is chosen as base learner (booster=gbtree). It is not defined for other base learner types, such as linear learners (booster=gblinear).

Parameters
  • fmap (str or os.PathLike (optional)) – The name of feature map file.

  • importance_type (str, default 'weight') – One of the importance types defined above.

get_split_value_histogram(feature, fmap='', bins=None, as_pandas=True)

Get split value histogram of a feature

Parameters
  • feature (str) – The name of the feature.

  • fmap (str or os.PathLike (optional)) – The name of feature map file.

  • bin (int, default None) – The maximum number of bins. Number of bins equals number of unique split values n_unique, if bins == None or bins > n_unique.

  • 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.

The model is loaded from an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be loaded. To preserve all attributes, pickle the Booster object.

Parameters

fname (string, os.PathLike, 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, approx_contribs=False, pred_interactions=False, validate_features=True)

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().

Note

Using predict() with DART booster

If the booster object is DART type, predict() will perform dropouts, i.e. only some of the trees will be evaluated. This will produce incorrect results if data is not the training data. To obtain correct results on test sets, set ntree_limit to a nonzero value, e.g.

preds = bst.predict(dtest, ntree_limit=num_round)
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 is True the output will be a matrix of size (nsample, nfeats + 1) with each record indicating the feature contributions (SHAP values) for that prediction. The sum of all feature contributions is equal to the raw untransformed margin value of the prediction. Note the final column is the bias term.

  • approx_contribs (bool) – Approximate the contributions of each feature

  • pred_interactions (bool) – When this is True the output will be a matrix of size (nsample, nfeats + 1, nfeats + 1) indicating the SHAP interaction values for each pair of features. The sum of each row (or column) of the interaction values equals the corresponding SHAP value (from pred_contribs), and the sum of the entire matrix equals the raw untransformed margin value of the prediction. Note the last row and column correspond to the bias term.

  • validate_features (bool) – When this is True, validate that the Booster’s and data’s feature_names are identical. Otherwise, it is assumed that the feature_names are the same.

Returns

prediction

Return type

numpy array

save_model(fname)

Save the model to a file.

The model is saved in an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be saved. To preserve all attributes, pickle the Booster object.

Parameters

fname (string or os.PathLike) – 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 pairs, dict of key to value or simply str key

  • value (optional) – value of the specified parameter, when params is str key

trees_to_dataframe(fmap='')

Parse a boosted tree model text dump into a pandas DataFrame structure.

This feature is only defined when the decision tree model is chosen as base learner (booster in {gbtree, dart}). It is not defined for other base learner types, such as linear learners (booster=gblinear).

Parameters

fmap (str or os.PathLike (optional)) – The name of feature map file.

update(dtrain, iteration, fobj=None)

Update for one iteration, with objective function calculated internally. This function should not be called directly by users.

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 validation sets for which metrics will evaluated during training. Validation metrics will help us track the performance of the model.

  • obj (function) – Customized objective function.

  • feval (function) – Customized evaluation function.

  • maximize (bool) – Whether to maximize feval.

  • early_stopping_rounds (int) – Activates early stopping. Validation metric needs to improve at least once in every early_stopping_rounds round(s) to continue training. Requires at least one item in evals. The method returns the model from the last iteration (not the best one). If there’s more than one item in evals, the last entry will be used for early stopping. If there’s more than one metric in the eval_metric parameter given in params, the last metric will be used for early stopping. 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'), the evals_result 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 Callback API. 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 or list of fold indices) – Sklearn KFolds or StratifiedKFolds object. Alternatively may explicitly pass sample indices for each fold. For n folds, folds should be a length n list of tuples. Each tuple is (in,out) where in is a list of indices to be used as the training samples for the n th fold and out is a list of indices to be used as the testing samples for the n th fold.

  • 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. Cross-Validation metric (average of validation metric computed over CV folds) needs to improve at least once in every early_stopping_rounds round(s) to continue training. The last entry in the evaluation history will represent the best iteration. If there’s more than one metric in the eval_metric parameter given in params, the last metric will be used for early stopping.

  • 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 Callback API. 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, verbosity=1, objective='reg:squarederror', booster='gbtree', tree_method='auto', n_jobs=1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, missing=None, num_parallel_tree=1, importance_type='gain', **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 trees to fit.

  • verbosity (int) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).

  • 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.

  • tree_method (string) – Specify which tree method to use. Default to auto. If this parameter is set to default, XGBoost will choose the most conservative option available. It’s recommended to study this option from parameters document.

  • n_jobs (int) – Number of parallel threads used to run xgboost.

  • 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 level.

  • colsample_bynode (float) – Subsample ratio of columns for each split.

  • 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.

  • random_state (int) –

    Random number seed.

    Note

    Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.

  • missing (float, optional) – Value in the data which needs to be present as a missing value. If None, defaults to np.nan.

  • num_parallel_tree (int) – Used for boosting random forest.

  • importance_type (string, default "gain") – The feature importance type for the feature_importances_ property: either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.

  • **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.rst. Attempting to set a parameter via the constructor args and **kwargs dict simultaneously will result in a TypeError.

    Note

    **kwargs unsupported by scikit-learn

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

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

apply(X, ntree_limit=0)

Return the predicted leaf every tree for each sample.

Parameters
  • X (array_like, shape=[n_samples, n_features]) – Input features matrix.

  • ntree_limit (int) – Limit number of trees in the prediction; defaults to 0 (use all trees).

Returns

X_leaves – For each datapoint x in X and for each tree, return the index of the leaf x ends up in. Leaves are numbered within [0; 2**(self.max_depth+1)), possibly with gaps in the numbering.

Return type

array_like, shape=[n_samples, n_trees]

property coef_

Coefficients property

Note

Coefficients are defined only for linear learners

Coefficients are only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree).

Returns

coef_

Return type

array of shape [n_features] or [n_classes, n_features]

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.XGBModel(**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']}}
property feature_importances_

Feature importances property

Note

Feature importance is defined only for tree boosters

Feature importance is only defined when the decision tree model is chosen as base learner (booster=gbtree). It is not defined for other base learner types, such as linear learners (booster=gblinear).

Returns

feature_importances_

Return type

array of shape [n_features]

fit(X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, callbacks=None)

Fit gradient boosting model

Parameters
  • X (array_like) – Feature matrix

  • y (array_like) – Labels

  • sample_weight (array_like) – instance weights

  • eval_set (list, optional) – A list of (X, y) tuple pairs to use as validation sets, for which metrics will be computed. Validation metrics will help us track the performance of the model.

  • sample_weight_eval_set (list, optional) – A list of the form [L_1, L_2, …, L_n], where each L_i is a list of instance weights on the i-th validation set.

  • eval_metric (str, list of str, or callable, optional) – If a str, should be a built-in evaluation metric to use. See doc/parameter.rst. If a list of str, should be the list of multiple built-in evaluation metrics to use. 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. The callable custom objective is always minimized.

  • early_stopping_rounds (int) – Activates early stopping. Validation metric needs to improve at least once in every early_stopping_rounds round(s) to continue training. Requires at least one item in eval_set. The method returns the model from the last iteration (not the best one). If there’s more than one item in eval_set, the last entry will be used for early stopping. If there’s more than one metric in eval_metric, the last metric will be used for early stopping. If early stopping occurs, the model will have three additional fields: clf.best_score, clf.best_iteration and clf.best_ntree_limit.

  • verbose (bool) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr.

  • xgb_model (str) – file name of stored XGBoost model or ‘Booster’ instance XGBoost 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 Callback API. Example:

    [xgb.callback.reset_learning_rate(custom_rates)]
    

get_booster()

Get the underlying xgboost Booster of this model.

This will raise an exception when fit was not called

Returns

booster

Return type

a xgboost booster of underlying model

get_num_boosting_rounds()

Gets the number of xgboost boosting rounds.

get_params(deep=False)

Get parameters.

get_xgb_params()

Get xgboost type parameters.

property intercept_

Intercept (bias) property

Note

Intercept is defined only for linear learners

Intercept (bias) is only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree).

Returns

intercept_

Return type

array of shape (1,) or [n_classes]

load_model(fname)

Load the model from a file.

The model is loaded from an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded. Label encodings (text labels to numeric labels) will be also lost. If you are using only the Python interface, we recommend pickling the model object for best results.

Parameters

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

predict(data, output_margin=False, ntree_limit=None, validate_features=True)

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 xgb.copy() to make copies of model object and then call predict().

Note

Using predict() with DART booster

If the booster object is DART type, predict() will perform dropouts, i.e. only some of the trees will be evaluated. This will produce incorrect results if data is not the training data. To obtain correct results on test sets, set ntree_limit to a nonzero value, e.g.

preds = bst.predict(dtest, ntree_limit=num_round)
Parameters
  • data (numpy.array/scipy.sparse) – Data to predict with

  • output_margin (bool) – Whether to output the raw untransformed margin value.

  • ntree_limit (int) – Limit number of trees in the prediction; defaults to best_ntree_limit if defined (i.e. it has been trained with early stopping), otherwise 0 (use all trees).

  • validate_features (bool) – When this is True, validate that the Booster’s and data’s feature_names are identical. Otherwise, it is assumed that the feature_names are the same.

Returns

prediction

Return type

numpy array

save_model(fname)

Save the model to a file.

The model is saved in an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded. Label encodings (text labels to numeric labels) will be also lost. If you are using only the Python interface, we recommend pickling the model object for best results.

Parameters

fname (string) – Output file name

set_params(**params)

Set the parameters of this estimator. Modification of the sklearn method to allow unknown kwargs. This allows using the full range of xgboost parameters that are not defined as member variables in sklearn grid search. :returns: :rtype: self

class xgboost.XGBClassifier(max_depth=3, learning_rate=0.1, n_estimators=100, verbosity=1, objective='binary:logistic', booster='gbtree', tree_method='auto', n_jobs=1, gpu_id=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, 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 trees to fit.

  • verbosity (int) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).

  • 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.

  • tree_method (string) – Specify which tree method to use. Default to auto. If this parameter is set to default, XGBoost will choose the most conservative option available. It’s recommended to study this option from parameters document.

  • n_jobs (int) – Number of parallel threads used to run xgboost.

  • 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 level.

  • colsample_bynode (float) – Subsample ratio of columns for each split.

  • 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.

  • random_state (int) –

    Random number seed.

    Note

    Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.

  • missing (float, optional) – Value in the data which needs to be present as a missing value. If None, defaults to np.nan.

  • num_parallel_tree (int) – Used for boosting random forest.

  • importance_type (string, default "gain") – The feature importance type for the feature_importances_ property: either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.

  • **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.rst. Attempting to set a parameter via the constructor args and **kwargs dict simultaneously will result in a TypeError.

    Note

    **kwargs unsupported by scikit-learn

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

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

apply(X, ntree_limit=0)

Return the predicted leaf every tree for each sample.

Parameters
  • X (array_like, shape=[n_samples, n_features]) – Input features matrix.

  • ntree_limit (int) – Limit number of trees in the prediction; defaults to 0 (use all trees).

Returns

X_leaves – For each datapoint x in X and for each tree, return the index of the leaf x ends up in. Leaves are numbered within [0; 2**(self.max_depth+1)), possibly with gaps in the numbering.

Return type

array_like, shape=[n_samples, n_trees]

property coef_

Coefficients property

Note

Coefficients are defined only for linear learners

Coefficients are only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree).

Returns

coef_

Return type

array of shape [n_features] or [n_classes, n_features]

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']}}
property feature_importances_

Feature importances property

Note

Feature importance is defined only for tree boosters

Feature importance is only defined when the decision tree model is chosen as base learner (booster=gbtree). It is not defined for other base learner types, such as linear learners (booster=gblinear).

Returns

feature_importances_

Return type

array of shape [n_features]

fit(X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, callbacks=None)

Fit gradient boosting classifier

Parameters
  • X (array_like) – Feature matrix

  • y (array_like) – Labels

  • sample_weight (array_like) – instance weights

  • eval_set (list, optional) – A list of (X, y) tuple pairs to use as validation sets, for which metrics will be computed. Validation metrics will help us track the performance of the model.

  • sample_weight_eval_set (list, optional) – A list of the form [L_1, L_2, …, L_n], where each L_i is a list of instance weights on the i-th validation set.

  • eval_metric (str, list of str, or callable, optional) – If a str, should be a built-in evaluation metric to use. See doc/parameter.rst. If a list of str, should be the list of multiple built-in evaluation metrics to use. 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. The callable custom objective is always minimized.

  • early_stopping_rounds (int) – Activates early stopping. Validation metric needs to improve at least once in every early_stopping_rounds round(s) to continue training. Requires at least one item in eval_set. The method returns the model from the last iteration (not the best one). If there’s more than one item in eval_set, the last entry will be used for early stopping. If there’s more than one metric in eval_metric, the last metric will be used for early stopping. If early stopping occurs, the model will have three additional fields: clf.best_score, clf.best_iteration and clf.best_ntree_limit.

  • verbose (bool) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr.

  • xgb_model (str) – file name of stored XGBoost model or ‘Booster’ instance XGBoost 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 Callback API. Example:

    [xgb.callback.reset_learning_rate(custom_rates)]
    

get_booster()

Get the underlying xgboost Booster of this model.

This will raise an exception when fit was not called

Returns

booster

Return type

a xgboost booster of underlying model

get_num_boosting_rounds()

Gets the number of xgboost boosting rounds.

get_params(deep=False)

Get parameters.

get_xgb_params()

Get xgboost type parameters.

property intercept_

Intercept (bias) property

Note

Intercept is defined only for linear learners

Intercept (bias) is only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree).

Returns

intercept_

Return type

array of shape (1,) or [n_classes]

load_model(fname)

Load the model from a file.

The model is loaded from an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded. Label encodings (text labels to numeric labels) will be also lost. If you are using only the Python interface, we recommend pickling the model object for best results.

Parameters

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

predict(data, output_margin=False, ntree_limit=None, validate_features=True)

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 xgb.copy() to make copies of model object and then call predict().

Note

Using predict() with DART booster

If the booster object is DART type, predict() will perform dropouts, i.e. only some of the trees will be evaluated. This will produce incorrect results if data is not the training data. To obtain correct results on test sets, set ntree_limit to a nonzero value, e.g.

preds = bst.predict(dtest, ntree_limit=num_round)
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 best_ntree_limit if defined (i.e. it has been trained with early stopping), otherwise 0 (use all trees).

  • validate_features (bool) – When this is True, validate that the Booster’s and data’s feature_names are identical. Otherwise, it is assumed that the feature_names are the same.

Returns

prediction

Return type

numpy array

predict_proba(data, ntree_limit=None, validate_features=True)

Predict the probability of each data example being of a given class.

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 xgb.copy() to make copies of model object and then call predict

Parameters
  • data (DMatrix) – The dmatrix storing the input.

  • ntree_limit (int) – Limit number of trees in the prediction; defaults to best_ntree_limit if defined (i.e. it has been trained with early stopping), otherwise 0 (use all trees).

  • validate_features (bool) – When this is True, validate that the Booster’s and data’s feature_names are identical. Otherwise, it is assumed that the feature_names are the same.

Returns

prediction – a numpy array with the probability of each data example being of a given class.

Return type

numpy array

save_model(fname)

Save the model to a file.

The model is saved in an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded. Label encodings (text labels to numeric labels) will be also lost. If you are using only the Python interface, we recommend pickling the model object for best results.

Parameters

fname (string) – Output file name

set_params(**params)

Set the parameters of this estimator. Modification of the sklearn method to allow unknown kwargs. This allows using the full range of xgboost parameters that are not defined as member variables in sklearn grid search. :returns: :rtype: self

class xgboost.XGBRanker(max_depth=3, learning_rate=0.1, n_estimators=100, verbosity=1, objective='rank:pairwise', booster='gbtree', tree_method='auto', n_jobs=-1, gpu_id=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, missing=None, **kwargs)

Bases: xgboost.sklearn.XGBModel

Implementation of the Scikit-Learn API for XGBoost Ranking.

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.

  • verbosity (int) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).

  • objective (string) – Specify the learning task and the corresponding learning objective. The objective name must start with “rank:”.

  • booster (string) – Specify which booster to use: gbtree, gblinear or dart.

  • n_jobs (int) – Number of parallel threads used to run xgboost.

  • 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 level.

  • colsample_bynode (float) – Subsample ratio of columns for each split.

  • 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.

  • random_state (int) – Random number seed.

:param .. note::: Using gblinear booster with shotgun updater is nondeterministic as

it uses Hogwild algorithm.

Parameters
  • 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.rst. Attempting to set a parameter via the constructor args and **kwargs dict simultaneously will result in a TypeError.

    Note

    **kwargs unsupported by scikit-learn

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

Note

A custom objective function is currently not supported by XGBRanker. Likewise, a custom metric function is not supported either.

Note

Query group information is required for ranking tasks.

Before fitting the model, your data need to be sorted by query group. When fitting the model, you need to provide an additional array that contains the size of each query group.

For example, if your original data look like:

qid

label

features

1

0

x_1

1

1

x_2

1

0

x_3

2

0

x_4

2

1

x_5

2

1

x_6

2

1

x_7

then your group array should be [3, 4].

apply(X, ntree_limit=0)

Return the predicted leaf every tree for each sample.

Parameters
  • X (array_like, shape=[n_samples, n_features]) – Input features matrix.

  • ntree_limit (int) – Limit number of trees in the prediction; defaults to 0 (use all trees).

Returns

X_leaves – For each datapoint x in X and for each tree, return the index of the leaf x ends up in. Leaves are numbered within [0; 2**(self.max_depth+1)), possibly with gaps in the numbering.

Return type

array_like, shape=[n_samples, n_trees]

property coef_

Coefficients property

Note

Coefficients are defined only for linear learners

Coefficients are only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree).

Returns

coef_

Return type

array of shape [n_features] or [n_classes, n_features]

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.XGBModel(**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']}}
property feature_importances_

Feature importances property

Note

Feature importance is defined only for tree boosters

Feature importance is only defined when the decision tree model is chosen as base learner (booster=gbtree). It is not defined for other base learner types, such as linear learners (booster=gblinear).

Returns

feature_importances_

Return type

array of shape [n_features]

fit(X, y, group, sample_weight=None, eval_set=None, sample_weight_eval_set=None, eval_group=None, eval_metric=None, early_stopping_rounds=None, verbose=False, xgb_model=None, callbacks=None)

Fit gradient boosting ranker

Parameters
  • X (array_like) – Feature matrix

  • y (array_like) – Labels

  • group (array_like) – Size of each query group of training data. Should have as many elements as the query groups in the training data

  • sample_weight (array_like) –

    Query group weights

    Note

    Weights are per-group for ranking tasks

    In ranking task, one weight is assigned to each query group (not each data point). This is because we only care about the relative ordering of data points within each group, so it doesn’t make sense to assign weights to individual data points.

  • eval_set (list, optional) – A list of (X, y) tuple pairs to use as validation sets, for which metrics will be computed. Validation metrics will help us track the performance of the model.

  • sample_weight_eval_set (list, optional) –

    A list of the form [L_1, L_2, …, L_n], where each L_i is a list of group weights on the i-th validation set.

    Note

    Weights are per-group for ranking tasks

    In ranking task, one weight is assigned to each query group (not each data point). This is because we only care about the relative ordering of data points within each group, so it doesn’t make sense to assign weights to individual data points.

  • eval_group (list of arrays, optional) – A list in which eval_group[i] is the list containing the sizes of all query groups in the i-th pair in eval_set.

  • eval_metric (str, list of str, optional) – If a str, should be a built-in evaluation metric to use. See doc/parameter.rst. If a list of str, should be the list of multiple built-in evaluation metrics to use. The custom evaluation metric is not yet supported for the ranker.

  • early_stopping_rounds (int) – Activates early stopping. Validation metric needs to improve at least once in every early_stopping_rounds round(s) to continue training. Requires at least one item in eval_set. The method returns the model from the last iteration (not the best one). If there’s more than one item in eval_set, the last entry will be used for early stopping. If there’s more than one metric in eval_metric, the last metric will be used for early stopping. If early stopping occurs, the model will have three additional fields: clf.best_score, clf.best_iteration and clf.best_ntree_limit.

  • verbose (bool) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr.

  • xgb_model (str) – file name of stored XGBoost model or ‘Booster’ instance XGBoost 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 Callback API. Example:

    [xgb.callback.reset_learning_rate(custom_rates)]
    

get_booster()

Get the underlying xgboost Booster of this model.

This will raise an exception when fit was not called

Returns

booster

Return type

a xgboost booster of underlying model

get_num_boosting_rounds()

Gets the number of xgboost boosting rounds.

get_params(deep=False)

Get parameters.

get_xgb_params()

Get xgboost type parameters.

property intercept_

Intercept (bias) property

Note

Intercept is defined only for linear learners

Intercept (bias) is only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree).

Returns

intercept_

Return type

array of shape (1,) or [n_classes]

load_model(fname)

Load the model from a file.

The model is loaded from an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded. Label encodings (text labels to numeric labels) will be also lost. If you are using only the Python interface, we recommend pickling the model object for best results.

Parameters

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

predict(data, output_margin=False, ntree_limit=0, validate_features=True)

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 xgb.copy() to make copies of model object and then call predict().

Note

Using predict() with DART booster

If the booster object is DART type, predict() will perform dropouts, i.e. only some of the trees will be evaluated. This will produce incorrect results if data is not the training data. To obtain correct results on test sets, set ntree_limit to a nonzero value, e.g.

preds = bst.predict(dtest, ntree_limit=num_round)
Parameters
  • data (numpy.array/scipy.sparse) – Data to predict with

  • output_margin (bool) – Whether to output the raw untransformed margin value.

  • ntree_limit (int) – Limit number of trees in the prediction; defaults to best_ntree_limit if defined (i.e. it has been trained with early stopping), otherwise 0 (use all trees).

  • validate_features (bool) – When this is True, validate that the Booster’s and data’s feature_names are identical. Otherwise, it is assumed that the feature_names are the same.

Returns

prediction

Return type

numpy array

save_model(fname)

Save the model to a file.

The model is saved in an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded. Label encodings (text labels to numeric labels) will be also lost. If you are using only the Python interface, we recommend pickling the model object for best results.

Parameters

fname (string) – Output file name

set_params(**params)

Set the parameters of this estimator. Modification of the sklearn method to allow unknown kwargs. This allows using the full range of xgboost parameters that are not defined as member variables in sklearn grid search. :returns: :rtype: self

class xgboost.XGBRFRegressor(max_depth=3, learning_rate=1, n_estimators=100, verbosity=1, objective='reg:squarederror', n_jobs=1, gpu_id=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=0.8, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=0.8, reg_alpha=0, reg_lambda=1e-05, scale_pos_weight=1, base_score=0.5, random_state=0, missing=None, **kwargs)

Bases: xgboost.sklearn.XGBRegressor

scikit-learn API for XGBoost random forest 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 trees to fit.

  • verbosity (int) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).

  • 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.

  • tree_method (string) – Specify which tree method to use. Default to auto. If this parameter is set to default, XGBoost will choose the most conservative option available. It’s recommended to study this option from parameters document.

  • n_jobs (int) – Number of parallel threads used to run xgboost.

  • 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 level.

  • colsample_bynode (float) – Subsample ratio of columns for each split.

  • 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.

  • random_state (int) –

    Random number seed.

    Note

    Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.

  • missing (float, optional) – Value in the data which needs to be present as a missing value. If None, defaults to np.nan.

  • num_parallel_tree (int) – Used for boosting random forest.

  • importance_type (string, default "gain") – The feature importance type for the feature_importances_ property: either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.

  • **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.rst. Attempting to set a parameter via the constructor args and **kwargs dict simultaneously will result in a TypeError.

    Note

    **kwargs unsupported by scikit-learn

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

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

apply(X, ntree_limit=0)

Return the predicted leaf every tree for each sample.

Parameters
  • X (array_like, shape=[n_samples, n_features]) – Input features matrix.

  • ntree_limit (int) – Limit number of trees in the prediction; defaults to 0 (use all trees).

Returns

X_leaves – For each datapoint x in X and for each tree, return the index of the leaf x ends up in. Leaves are numbered within [0; 2**(self.max_depth+1)), possibly with gaps in the numbering.

Return type

array_like, shape=[n_samples, n_trees]

property coef_

Coefficients property

Note

Coefficients are defined only for linear learners

Coefficients are only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree).

Returns

coef_

Return type

array of shape [n_features] or [n_classes, n_features]

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.XGBModel(**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']}}
property feature_importances_

Feature importances property

Note

Feature importance is defined only for tree boosters

Feature importance is only defined when the decision tree model is chosen as base learner (booster=gbtree). It is not defined for other base learner types, such as linear learners (booster=gblinear).

Returns

feature_importances_

Return type

array of shape [n_features]

fit(X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, callbacks=None)

Fit gradient boosting model

Parameters
  • X (array_like) – Feature matrix

  • y (array_like) – Labels

  • sample_weight (array_like) – instance weights

  • eval_set (list, optional) – A list of (X, y) tuple pairs to use as validation sets, for which metrics will be computed. Validation metrics will help us track the performance of the model.

  • sample_weight_eval_set (list, optional) – A list of the form [L_1, L_2, …, L_n], where each L_i is a list of instance weights on the i-th validation set.

  • eval_metric (str, list of str, or callable, optional) – If a str, should be a built-in evaluation metric to use. See doc/parameter.rst. If a list of str, should be the list of multiple built-in evaluation metrics to use. 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. The callable custom objective is always minimized.

  • early_stopping_rounds (int) – Activates early stopping. Validation metric needs to improve at least once in every early_stopping_rounds round(s) to continue training. Requires at least one item in eval_set. The method returns the model from the last iteration (not the best one). If there’s more than one item in eval_set, the last entry will be used for early stopping. If there’s more than one metric in eval_metric, the last metric will be used for early stopping. If early stopping occurs, the model will have three additional fields: clf.best_score, clf.best_iteration and clf.best_ntree_limit.

  • verbose (bool) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr.

  • xgb_model (str) – file name of stored XGBoost model or ‘Booster’ instance XGBoost 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 Callback API. Example:

    [xgb.callback.reset_learning_rate(custom_rates)]
    

get_booster()

Get the underlying xgboost Booster of this model.

This will raise an exception when fit was not called

Returns

booster

Return type

a xgboost booster of underlying model

get_num_boosting_rounds()

Gets the number of xgboost boosting rounds.

get_params(deep=False)

Get parameters.

get_xgb_params()

Get xgboost type parameters.

property intercept_

Intercept (bias) property

Note

Intercept is defined only for linear learners

Intercept (bias) is only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree).

Returns

intercept_

Return type

array of shape (1,) or [n_classes]

load_model(fname)

Load the model from a file.

The model is loaded from an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded. Label encodings (text labels to numeric labels) will be also lost. If you are using only the Python interface, we recommend pickling the model object for best results.

Parameters

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

predict(data, output_margin=False, ntree_limit=None, validate_features=True)

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 xgb.copy() to make copies of model object and then call predict().

Note

Using predict() with DART booster

If the booster object is DART type, predict() will perform dropouts, i.e. only some of the trees will be evaluated. This will produce incorrect results if data is not the training data. To obtain correct results on test sets, set ntree_limit to a nonzero value, e.g.

preds = bst.predict(dtest, ntree_limit=num_round)
Parameters
  • data (numpy.array/scipy.sparse) – Data to predict with

  • output_margin (bool) – Whether to output the raw untransformed margin value.

  • ntree_limit (int) – Limit number of trees in the prediction; defaults to best_ntree_limit if defined (i.e. it has been trained with early stopping), otherwise 0 (use all trees).

  • validate_features (bool) – When this is True, validate that the Booster’s and data’s feature_names are identical. Otherwise, it is assumed that the feature_names are the same.

Returns

prediction

Return type

numpy array

save_model(fname)

Save the model to a file.

The model is saved in an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded. Label encodings (text labels to numeric labels) will be also lost. If you are using only the Python interface, we recommend pickling the model object for best results.

Parameters

fname (string) – Output file name

set_params(**params)

Set the parameters of this estimator. Modification of the sklearn method to allow unknown kwargs. This allows using the full range of xgboost parameters that are not defined as member variables in sklearn grid search. :returns: :rtype: self

class xgboost.XGBRFClassifier(max_depth=3, learning_rate=1, n_estimators=100, verbosity=1, objective='binary:logistic', n_jobs=1, gpu_id=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=0.8, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=0.8, reg_alpha=0, reg_lambda=1e-05, scale_pos_weight=1, base_score=0.5, random_state=0, missing=None, **kwargs)

Bases: xgboost.sklearn.XGBClassifier

scikit-learn API for XGBoost random forest 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 trees to fit.

  • verbosity (int) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).

  • 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.

  • tree_method (string) – Specify which tree method to use. Default to auto. If this parameter is set to default, XGBoost will choose the most conservative option available. It’s recommended to study this option from parameters document.

  • n_jobs (int) – Number of parallel threads used to run xgboost.

  • 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 level.

  • colsample_bynode (float) – Subsample ratio of columns for each split.

  • 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.

  • random_state (int) –

    Random number seed.

    Note

    Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.

  • missing (float, optional) – Value in the data which needs to be present as a missing value. If None, defaults to np.nan.

  • num_parallel_tree (int) – Used for boosting random forest.

  • importance_type (string, default "gain") – The feature importance type for the feature_importances_ property: either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.

  • **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.rst. Attempting to set a parameter via the constructor args and **kwargs dict simultaneously will result in a TypeError.

    Note

    **kwargs unsupported by scikit-learn

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

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

apply(X, ntree_limit=0)

Return the predicted leaf every tree for each sample.

Parameters
  • X (array_like, shape=[n_samples, n_features]) – Input features matrix.

  • ntree_limit (int) – Limit number of trees in the prediction; defaults to 0 (use all trees).

Returns

X_leaves – For each datapoint x in X and for each tree, return the index of the leaf x ends up in. Leaves are numbered within [0; 2**(self.max_depth+1)), possibly with gaps in the numbering.

Return type

array_like, shape=[n_samples, n_trees]

property coef_

Coefficients property

Note

Coefficients are defined only for linear learners

Coefficients are only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree).

Returns

coef_

Return type

array of shape [n_features] or [n_classes, n_features]

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']}}
property feature_importances_

Feature importances property

Note

Feature importance is defined only for tree boosters

Feature importance is only defined when the decision tree model is chosen as base learner (booster=gbtree). It is not defined for other base learner types, such as linear learners (booster=gblinear).

Returns

feature_importances_

Return type

array of shape [n_features]

fit(X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, callbacks=None)

Fit gradient boosting classifier

Parameters
  • X (array_like) – Feature matrix

  • y (array_like) – Labels

  • sample_weight (array_like) – instance weights

  • eval_set (list, optional) – A list of (X, y) tuple pairs to use as validation sets, for which metrics will be computed. Validation metrics will help us track the performance of the model.

  • sample_weight_eval_set (list, optional) – A list of the form [L_1, L_2, …, L_n], where each L_i is a list of instance weights on the i-th validation set.

  • eval_metric (str, list of str, or callable, optional) – If a str, should be a built-in evaluation metric to use. See doc/parameter.rst. If a list of str, should be the list of multiple built-in evaluation metrics to use. 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. The callable custom objective is always minimized.

  • early_stopping_rounds (int) – Activates early stopping. Validation metric needs to improve at least once in every early_stopping_rounds round(s) to continue training. Requires at least one item in eval_set. The method returns the model from the last iteration (not the best one). If there’s more than one item in eval_set, the last entry will be used for early stopping. If there’s more than one metric in eval_metric, the last metric will be used for early stopping. If early stopping occurs, the model will have three additional fields: clf.best_score, clf.best_iteration and clf.best_ntree_limit.

  • verbose (bool) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr.

  • xgb_model (str) – file name of stored XGBoost model or ‘Booster’ instance XGBoost 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 Callback API. Example:

    [xgb.callback.reset_learning_rate(custom_rates)]
    

get_booster()

Get the underlying xgboost Booster of this model.

This will raise an exception when fit was not called

Returns

booster

Return type

a xgboost booster of underlying model

get_num_boosting_rounds()

Gets the number of xgboost boosting rounds.

get_params(deep=False)

Get parameters.

get_xgb_params()

Get xgboost type parameters.

property intercept_

Intercept (bias) property

Note

Intercept is defined only for linear learners

Intercept (bias) is only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree).

Returns

intercept_

Return type

array of shape (1,) or [n_classes]

load_model(fname)

Load the model from a file.

The model is loaded from an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded. Label encodings (text labels to numeric labels) will be also lost. If you are using only the Python interface, we recommend pickling the model object for best results.

Parameters

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

predict(data, output_margin=False, ntree_limit=None, validate_features=True)

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 xgb.copy() to make copies of model object and then call predict().

Note

Using predict() with DART booster

If the booster object is DART type, predict() will perform dropouts, i.e. only some of the trees will be evaluated. This will produce incorrect results if data is not the training data. To obtain correct results on test sets, set ntree_limit to a nonzero value, e.g.

preds = bst.predict(dtest, ntree_limit=num_round)
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 best_ntree_limit if defined (i.e. it has been trained with early stopping), otherwise 0 (use all trees).

  • validate_features (bool) – When this is True, validate that the Booster’s and data’s feature_names are identical. Otherwise, it is assumed that the feature_names are the same.

Returns

prediction

Return type

numpy array

predict_proba(data, ntree_limit=None, validate_features=True)

Predict the probability of each data example being of a given class.

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 xgb.copy() to make copies of model object and then call predict

Parameters
  • data (DMatrix) – The dmatrix storing the input.

  • ntree_limit (int) – Limit number of trees in the prediction; defaults to best_ntree_limit if defined (i.e. it has been trained with early stopping), otherwise 0 (use all trees).

  • validate_features (bool) – When this is True, validate that the Booster’s and data’s feature_names are identical. Otherwise, it is assumed that the feature_names are the same.

Returns

prediction – a numpy array with the probability of each data example being of a given class.

Return type

numpy array

save_model(fname)

Save the model to a file.

The model is saved in an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded. Label encodings (text labels to numeric labels) will be also lost. If you are using only the Python interface, we recommend pickling the model object for best results.

Parameters

fname (string) – Output file name

set_params(**params)

Set the parameters of this estimator. Modification of the sklearn method to allow unknown kwargs. This allows using the full range of xgboost parameters that are not defined as member variables in sklearn grid search. :returns: :rtype: self

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, show_values=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.

  • grid (bool, Turn the axes grids on or off. Default is True (On)) –

  • 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.

  • show_values (bool, default True) – Show values on plot. To disable, pass False.

  • kwargs – Other keywords passed to ax.barh()

Returns

ax

Return type

matplotlib Axes

xgboost.plot_tree(booster, fmap='', num_trees=0, rankdir=None, 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 "TB") – 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=None, yes_color=None, no_color=None, condition_node_params=None, leaf_node_params=None, **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.

  • condition_node_params (dict (optional)) –

    Condition node configuration for for graphviz. Example:

    
    
    {‘shape’: ‘box’,

    ’style’: ‘filled,rounded’, ‘fillcolor’: ‘#78bceb’}

  • leaf_node_params (dict (optional)) –

    Leaf node configuration for graphviz. Example:

    
    
    {‘shape’: ‘box’,

    ’style’: ‘filled’, ‘fillcolor’: ‘#e48038’}

  • kwargs (Other keywords passed to graphviz graph_attr, E.g.:) – graph [ {key} = {value} ]

Returns

graph

Return type

graphviz.Source

Callback API

xgboost.callback.print_evaluation(period=1, show_stdv=True)

Create a callback that print evaluation result.

We print the evaluation results every period iterations and on the first and the last iterations.

Parameters
  • period (int) – The period to log the evaluation results

  • show_stdv (bool, optional) – Whether show stdv if provided

Returns

callback – A callback that print evaluation every period iterations.

Return type

function

xgboost.callback.record_evaluation(eval_result)

Create a call back that records the evaluation history into eval_result.

Parameters

eval_result (dict) – A dictionary to store the evaluation results.

Returns

callback – The requested callback function.

Return type

function

xgboost.callback.reset_learning_rate(learning_rates)

Reset learning rate after iteration 1

NOTE: the initial learning rate will still take in-effect on first iteration.

Parameters

learning_rates (list or function) –

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)

  • list l: eta = l[boosting_round]

  • function f: eta = f(boosting_round, num_boost_round)

Returns

callback – The requested callback function.

Return type

function

xgboost.callback.early_stop(stopping_rounds, maximize=False, verbose=True)

Create a callback that activates early stoppping.

Validation error needs to decrease at least every 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)

Parameters
  • stopp_rounds (int) – The stopping rounds before the trend occur.

  • maximize (bool) – Whether to maximize evaluation metric.

  • verbose (optional, bool) – Whether to print message about early stopping information.

Returns

callback – The requested callback function.

Return type

function

Dask API

Dask extensions for distributed training. See https://xgboost.readthedocs.io/en/latest/tutorials/dask.html for simple tutorial. Also xgboost/demo/dask for some examples.

There are two sets of APIs in this module, one is the functional API including train and predict methods. Another is stateful Scikit-Learner wrapper inherited from single-node Scikit-Learn interface.

The implementation is heavily influenced by dask_xgboost: https://github.com/dask/dask-xgboost

xgboost.dask.DaskDMatrix(client, data, label=None, missing=None, weight=None, feature_names=None, feature_types=None)

DMatrix holding on references to Dask DataFrame or Dask Array.

Parameters
  • client (dask.distributed.Client) – Specify the dask client used for training. Use default client returned from dask if it’s set to None.

  • data (dask.array.Array/dask.dataframe.DataFrame) – data source of DMatrix.

  • label (dask.array.Array/dask.dataframe.DataFrame) – label used for trainin.

  • missing (float, optional) – Value in the input data (e.g. numpy.ndarray) which needs to be present as a missing value. If None, defaults to np.nan.

  • weight (dask.array.Array/dask.dataframe.DataFrame) – Weight for each instance.

  • feature_names (list, optional) – Set names for features.

  • feature_types (list, optional) – Set types for features

xgboost.dask.predict(client, model, data, *args)

Run prediction with a trained booster.

Note

Only default prediction mode is supported right now.

Parameters
  • client (dask.distributed.Client) – Specify the dask client used for training. Use default client returned from dask if it’s set to None.

  • model (A Booster or a dictionary returned by xgboost.dask.train.) – The trained model.

  • data (DaskDMatrix) – Input data used for prediction.

Returns

prediction

Return type

dask.array.Array

xgboost.dask.DaskXGBClassifier(max_depth=3, learning_rate=0.1, n_estimators=100, verbosity=1, objective='reg:squarederror', booster='gbtree', tree_method='auto', n_jobs=1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, missing=None, num_parallel_tree=1, importance_type='gain', **kwargs)

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 trees to fit.

  • verbosity (int) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).

  • 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.

  • tree_method (string) – Specify which tree method to use. Default to auto. If this parameter is set to default, XGBoost will choose the most conservative option available. It’s recommended to study this option from parameters document.

  • n_jobs (int) – Number of parallel threads used to run xgboost.

  • 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 level.

  • colsample_bynode (float) – Subsample ratio of columns for each split.

  • 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.

  • random_state (int) –

    Random number seed.

    Note

    Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.

  • missing (float, optional) – Value in the data which needs to be present as a missing value. If None, defaults to np.nan.

  • num_parallel_tree (int) – Used for boosting random forest.

  • importance_type (string, default "gain") – The feature importance type for the feature_importances_ property: either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.

  • **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.rst. Attempting to set a parameter via the constructor args and **kwargs dict simultaneously will result in a TypeError.

    Note

    **kwargs unsupported by scikit-learn

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

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

xgboost.dask.DaskXGBRegressor(max_depth=3, learning_rate=0.1, n_estimators=100, verbosity=1, objective='reg:squarederror', booster='gbtree', tree_method='auto', n_jobs=1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, missing=None, num_parallel_tree=1, importance_type='gain', **kwargs)

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 trees to fit.

  • verbosity (int) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).

  • 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.

  • tree_method (string) – Specify which tree method to use. Default to auto. If this parameter is set to default, XGBoost will choose the most conservative option available. It’s recommended to study this option from parameters document.

  • n_jobs (int) – Number of parallel threads used to run xgboost.

  • 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 level.

  • colsample_bynode (float) – Subsample ratio of columns for each split.

  • 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.

  • random_state (int) –

    Random number seed.

    Note

    Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.

  • missing (float, optional) – Value in the data which needs to be present as a missing value. If None, defaults to np.nan.

  • num_parallel_tree (int) – Used for boosting random forest.

  • importance_type (string, default "gain") – The feature importance type for the feature_importances_ property: either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.

  • **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.rst. Attempting to set a parameter via the constructor args and **kwargs dict simultaneously will result in a TypeError.

    Note

    **kwargs unsupported by scikit-learn

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

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