Python API Reference
This page gives the Python API reference of xgboost, please also refer to Python Package Introduction for more information about the Python package.
Global Configuration
- xgboost.config_context(**new_config)
Context manager for global XGBoost configuration.
Global configuration consists of a collection of parameters that can be applied in the global scope. See Global Configuration for the full list of parameters supported in the global configuration.
Note
All settings, not just those presently modified, will be returned to their previous values when the context manager is exited. This is not thread-safe.
New in version 1.4.0.
- Parameters
new_config (Dict[str, Any]) – Keyword arguments representing the parameters and their values
Example
import xgboost as xgb # Show all messages, including ones pertaining to debugging xgb.set_config(verbosity=2) # Get current value of global configuration # This is a dict containing all parameters in the global configuration, # including 'verbosity' config = xgb.get_config() assert config['verbosity'] == 2 # Example of using the context manager xgb.config_context(). # The context manager will restore the previous value of the global # configuration upon exiting. with xgb.config_context(verbosity=0): # Suppress warning caused by model generated with XGBoost version < 1.0.0 bst = xgb.Booster(model_file='./old_model.bin') assert xgb.get_config()['verbosity'] == 2 # old value restored
See also
set_config
Set global XGBoost configuration
get_config
Get current values of the global configuration
- xgboost.set_config(**new_config)
Set global configuration.
Global configuration consists of a collection of parameters that can be applied in the global scope. See Global Configuration for the full list of parameters supported in the global configuration.
New in version 1.4.0.
- Parameters
new_config (Dict[str, Any]) – Keyword arguments representing the parameters and their values
Example
import xgboost as xgb # Show all messages, including ones pertaining to debugging xgb.set_config(verbosity=2) # Get current value of global configuration # This is a dict containing all parameters in the global configuration, # including 'verbosity' config = xgb.get_config() assert config['verbosity'] == 2 # Example of using the context manager xgb.config_context(). # The context manager will restore the previous value of the global # configuration upon exiting. with xgb.config_context(verbosity=0): # Suppress warning caused by model generated with XGBoost version < 1.0.0 bst = xgb.Booster(model_file='./old_model.bin') assert xgb.get_config()['verbosity'] == 2 # old value restored
- xgboost.get_config()
Get current values of the global configuration.
Global configuration consists of a collection of parameters that can be applied in the global scope. See Global Configuration for the full list of parameters supported in the global configuration.
New in version 1.4.0.
- Returns
args – The list of global parameters and their values
- Return type
Dict[str, Any]
Example
import xgboost as xgb # Show all messages, including ones pertaining to debugging xgb.set_config(verbosity=2) # Get current value of global configuration # This is a dict containing all parameters in the global configuration, # including 'verbosity' config = xgb.get_config() assert config['verbosity'] == 2 # Example of using the context manager xgb.config_context(). # The context manager will restore the previous value of the global # configuration upon exiting. with xgb.config_context(verbosity=0): # Suppress warning caused by model generated with XGBoost version < 1.0.0 bst = xgb.Booster(model_file='./old_model.bin') assert xgb.get_config()['verbosity'] == 2 # old value restored
Core Data Structure
Core XGBoost Library.
- class xgboost.DMatrix(data, label=None, *, weight=None, base_margin=None, missing=None, silent=False, feature_names=None, feature_types=None, nthread=None, group=None, qid=None, label_lower_bound=None, label_upper_bound=None, feature_weights=None, enable_categorical=False)
Bases:
object
Data Matrix used in XGBoost.
DMatrix is an internal data structure that is used by XGBoost, which is optimized for both memory efficiency and training speed. You can construct DMatrix from multiple different sources of data.
- Parameters
data (os.PathLike/string/numpy.array/scipy.sparse/pd.DataFrame/) – dt.Frame/cudf.DataFrame/cupy.array/dlpack 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 (array_like) – Label of the training data.
weight (array_like) –
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.
base_margin (array_like) – Base margin used for boosting from existing model.
missing (float, optional) – Value in the input data which needs to be present as a missing value. If None, defaults to np.nan.
silent (boolean, optional) – Whether print messages during construction
feature_names (list, optional) – Set names for features.
feature_types (Optional[List[str]]) – Set types for features. When enable_categorical is set to True, string “c” represents categorical data type.
nthread (integer, optional) – Number of threads to use for loading data when parallelization is applicable. If -1, uses maximum threads available on the system.
group (array_like) – Group size for all ranking group.
qid (array_like) – Query ID for data samples, used for ranking.
label_lower_bound (array_like) – Lower bound for survival training.
label_upper_bound (array_like) – Upper bound for survival training.
feature_weights (array_like, optional) – Set feature weights for column sampling.
enable_categorical (boolean, optional) –
New in version 1.3.0.
Experimental support of specializing for categorical features. Do not set to True unless you are interested in development. Currently it’s only available for gpu_hist tree method with 1 vs rest (one hot) categorical split. Also, JSON serialization format is required.
- Return type
None
- property feature_names: Optional[List[str]]
Get feature names (column labels).
- Returns
feature_names
- Return type
list or None
- property feature_types: Optional[List[str]]
Get feature types (column types).
- Returns
feature_types
- Return type
list or None
- get_base_margin()
Get the base margin of the DMatrix.
- Return type
base_margin
- 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_group()
Get the group of the DMatrix.
- Return type
group
- 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
- 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.
- Return type
None
- 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
- Return type
None
- 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
- Return type
None
- 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
- Return type
None
- set_group(group)
Set group size of DMatrix (used for ranking).
- Parameters
group (array like) – Group size of each group
- Return type
None
- set_info(*, label=None, weight=None, base_margin=None, group=None, qid=None, label_lower_bound=None, label_upper_bound=None, feature_names=None, feature_types=None, feature_weights=None)
Set meta info for DMatrix. See doc string for
xgboost.DMatrix
.
- set_label(label)
Set label of dmatrix
- Parameters
label (array like) – The label information to be set into DMatrix
- Return type
None
- 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
- Return type
None
- 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.
- Return type
None
- slice(rindex, allow_groups=False)
Slice the DMatrix and return a new DMatrix that only contains rindex.
- Parameters
rindex (Union[List[int], numpy.ndarray]) – List of indices to be selected.
allow_groups (bool) – Allow slicing of a matrix with a groups attribute
- Returns
A new DMatrix containing only selected indices.
- Return type
res
- class xgboost.DeviceQuantileDMatrix(data, label=None, *, weight=None, base_margin=None, missing=None, silent=False, feature_names=None, feature_types=None, nthread=None, max_bin=256, group=None, qid=None, label_lower_bound=None, label_upper_bound=None, feature_weights=None, enable_categorical=False)
Bases:
xgboost.core.DMatrix
Device memory Data Matrix used in XGBoost for training with tree_method=’gpu_hist’. Do not use this for test/validation tasks as some information may be lost in quantisation. This DMatrix is primarily designed to save memory in training from device memory inputs by avoiding intermediate storage. Set max_bin to control the number of bins during quantisation. See doc string in
xgboost.DMatrix
for documents on meta info.You can construct DeviceQuantileDMatrix from cupy/cudf/dlpack.
New in version 1.1.0.
- Parameters
data (os.PathLike/string/numpy.array/scipy.sparse/pd.DataFrame/) – dt.Frame/cudf.DataFrame/cupy.array/dlpack 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 (array_like) – Label of the training data.
weight (array_like) –
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.
base_margin (array_like) – Base margin used for boosting from existing model.
missing (float, optional) – Value in the input data which needs to be present as a missing value. If None, defaults to np.nan.
silent (boolean, optional) – Whether print messages during construction
feature_names (list, optional) – Set names for features.
feature_types – Set types for features. When enable_categorical is set to True, string “c” represents categorical data type.
nthread (integer, optional) – Number of threads to use for loading data when parallelization is applicable. If -1, uses maximum threads available on the system.
group (array_like) – Group size for all ranking group.
qid (array_like) – Query ID for data samples, used for ranking.
label_lower_bound (array_like) – Lower bound for survival training.
label_upper_bound (array_like) – Upper bound for survival training.
feature_weights (array_like, optional) – Set feature weights for column sampling.
enable_categorical (boolean, optional) –
New in version 1.3.0.
Experimental support of specializing for categorical features. Do not set to True unless you are interested in development. Currently it’s only available for gpu_hist tree method with 1 vs rest (one hot) categorical split. Also, JSON serialization format is required.
max_bin (int) –
- class xgboost.Booster(params=None, cache=None, 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
- Return type
None
- attr(key)
Get attribute string from the Booster.
- 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.Booster.update()
, this function should not be called directly by users.- Parameters
dtrain (xgboost.core.DMatrix) – The training DMatrix.
grad (numpy.ndarray) – The first order of gradient.
hess (numpy.ndarray) – The second order of gradient.
- Return type
None
- 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. Unlike
save_model()
, the output format is primarily used for visualization or interpretation, hence it’s more human readable but cannot be loaded back to XGBoost.- 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 (xgboost.core.DMatrix) – The dmatrix storing the input.
name (str) – The name of the dataset.
iteration (int) – The current iteration number.
- Returns
result – Evaluation result string.
- Return type
- eval_set(evals, iteration=0, feval=None, output_margin=True)
Evaluate a set of data.
- Parameters
evals (Sequence[Tuple[xgboost.core.DMatrix, str]]) – List of items to be evaluated.
iteration (int) – Current iteration.
feval (Optional[Callable[[numpy.ndarray, xgboost.core.DMatrix], Tuple[str, float]]]) – Custom evaluation function.
output_margin (bool) –
- Returns
result – Evaluation result string.
- Return type
- property feature_names: Optional[List[str]]
Feature names for this booster. Can be directly set by input data or by assignment.
- property feature_types: Optional[List[str]]
Feature types for this booster. Can be directly set by input data or by assignment.
- get_dump(fmap='', with_stats=False, dump_format='text')
Returns the model dump as a list of strings. Unlike
save_model()
, the output format is primarily used for visualization or interpretation, hence it’s more human readable but cannot be loaded back to XGBoost.
- get_fscore(fmap='')
Get feature importance of each feature.
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.
- get_score(fmap='', importance_type='weight')
Get feature importance of each feature. For tree model 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
For linear model, only “weight” is defined and it’s the normalized coefficients without bias.
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 (Union[str, os.PathLike]) – The name of feature map file.
importance_type (str) – One of the importance types defined above.
- Returns
A map between feature names and their scores. When gblinear is used for
multi-class classification the scores for each feature is a list with length
n_classes, otherwise they’re scalars.
- Return type
- 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.
- Return type
Union[numpy.ndarray, pandas.core.frame.DataFrame]
- inplace_predict(data, iteration_range=(0, 0), predict_type='value', missing=nan, validate_features=True, base_margin=None, strict_shape=False)
Run prediction in-place, Unlike
predict()
method, inplace prediction does not cache the prediction result.Calling only
inplace_predict
in multiple threads is safe and lock free. But the safety does not hold when used in conjunction with other methods. E.g. you can’t train the booster in one thread and perform prediction in the other.booster.set_param({'predictor': 'gpu_predictor'}) booster.inplace_predict(cupy_array) booster.set_param({'predictor': 'cpu_predictor}) booster.inplace_predict(numpy_array)
New in version 1.1.0.
- Parameters
data (numpy.ndarray/scipy.sparse.csr_matrix/cupy.ndarray/) – cudf.DataFrame/pd.DataFrame The input data, must not be a view for numpy array. Set
predictor
togpu_predictor
for running prediction on CuPy array or CuDF DataFrame.iteration_range (Tuple[int, int]) – See
predict()
for details.predict_type (str) –
value Output model prediction values.
margin Output the raw untransformed margin value.
missing (float) – See
xgboost.DMatrix
for details.validate_features (bool) – See
xgboost.Booster.predict()
for details.See
xgboost.DMatrix
for details.New in version 1.4.0.
strict_shape (bool) –
See
xgboost.Booster.predict()
for details.New in version 1.4.0.
- Returns
prediction – The prediction result. When input data is on GPU, prediction result is stored in a cupy array.
- Return type
numpy.ndarray/cupy.ndarray
- load_config(config)
Load configuration returned by save_config.
New in version 1.0.0.
- Parameters
config (str) –
- Return type
None
- load_model(fname)
Load the model from a file or bytearray. Path to file can be local or as an URI.
The model is loaded from XGBoost format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be loaded when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.load_model("model.json") # or model.load_model("model.ubj")
- Parameters
fname (Union[str, bytearray, os.PathLike]) – Input file name or memory buffer(see also save_raw)
- Return type
None
- num_boosted_rounds()
Get number of boosted rounds. For gblinear this is reset to 0 after serializing the model.
- Return type
- predict(data, output_margin=False, ntree_limit=0, pred_leaf=False, pred_contribs=False, approx_contribs=False, pred_interactions=False, validate_features=True, training=False, iteration_range=(0, 0), strict_shape=False)
Predict with data. The full model will be used unless iteration_range is specified, meaning user have to either slice the model or use the
best_iteration
attribute to get prediction from best model returned from early stopping.Note
See Prediction for issues like thread safety and a summary of outputs from this function.
- Parameters
data (xgboost.core.DMatrix) – The dmatrix storing the input.
output_margin (bool) – Whether to output the raw untransformed margin value.
ntree_limit (int) – Deprecated, use iteration_range instead.
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. Used when
pred_contribs
orpred_interactions
is set to True. Changing the default of this parameter (False) is not recommended.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.
training (bool) –
Whether the prediction value is used for training. This can effect dart booster, which performs dropouts during training iterations but use all trees for inference. If you want to obtain result with dropouts, set this parameter to True. Also, the parameter is set to true when obtaining prediction for custom objective function.
New in version 1.0.0.
iteration_range (Tuple[int, int]) –
Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying iteration_range=(10, 20), then only the forests built during [10, 20) (half open set) rounds are used in this prediction.
New in version 1.4.0.
strict_shape (bool) –
When set to True, output shape is invariant to whether classification is used. For both value and margin prediction, the output shape is (n_samples, n_groups), n_groups == 1 when multi-class is not used. Default to False, in which case the output shape can be (n_samples, ) if multi-class is not used.
New in version 1.4.0.
- Returns
prediction
- Return type
numpy array
- save_config()
Output internal parameter configuration of Booster as a JSON string.
New in version 1.0.0.
- Return type
- save_model(fname)
Save the model to a file.
The model is saved in an XGBoost internal format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be saved when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.save_model("model.json") # or model.save_model("model.ubj")
- Parameters
fname (string or os.PathLike) – Output file name
- Return type
None
- save_raw(raw_format='deprecated')
Save the model to a in memory buffer representation instead of file.
- Parameters
raw_format (str) – Format of output buffer. Can be json, ubj or deprecated. Right now the default is deprecated but it will be changed to ubj (univeral binary json) in the future.
- Return type
An in memory buffer representation of the model
- set_attr(**kwargs)
Set the attribute of the Booster.
- 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.
- Return type
pandas.core.frame.DataFrame
- update(dtrain, iteration, fobj=None)
Update for one iteration, with objective function calculated internally. This function should not be called directly by users.
Learning API
Training Library containing training routines.
- xgboost.train(params, dtrain, num_boost_round=10, *, evals=None, obj=None, feval=None, maximize=None, early_stopping_rounds=None, evals_result=None, verbose_eval=True, xgb_model=None, callbacks=None, custom_metric=None)
Train a booster with given parameters.
- Parameters
dtrain (xgboost.core.DMatrix) – Data to be trained.
num_boost_round (int) – Number of boosting iterations.
evals (Optional[Sequence[Tuple[xgboost.core.DMatrix, str]]]) – List of validation sets for which metrics will evaluated during training. Validation metrics will help us track the performance of the model.
obj (Optional[Callable[[numpy.ndarray, xgboost.core.DMatrix], Tuple[numpy.ndarray, numpy.ndarray]]]) – Custom objective function. See Custom Objective for details.
feval (Optional[Callable[[numpy.ndarray, xgboost.core.DMatrix], Tuple[str, float]]]) –
Deprecated since version 1.6.0: Use custom_metric instead.
maximize (bool) – Whether to maximize feval.
early_stopping_rounds (Optional[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). Use custom callback or model slicing if the best model is desired. 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 two additional fields:
bst.best_score
,bst.best_iteration
.evals_result (Dict[str, Dict[str, Union[List[float], List[Tuple[float, float]]]]]) –
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 (Optional[Union[bool, 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.xgb_model (Optional[Union[str, os.PathLike, xgboost.core.Booster, bytearray]]) – Xgb model to be loaded before training (allows training continuation).
callbacks (Optional[Sequence[xgboost.callback.TrainingCallback]]) –
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.LearningRateScheduler(custom_rates)]
custom_metric (Optional[Callable[[numpy.ndarray, xgboost.core.DMatrix], Tuple[str, float]]]) –
Custom metric function. See Custom Metric for details.
- 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=None, early_stopping_rounds=None, fpreproc=None, as_pandas=True, verbose_eval=None, show_stdv=True, seed=0, callbacks=None, shuffle=True, custom_metric=None)
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 lengthn
list of tuples. Each tuple is(in,out)
wherein
is a list of indices to be used as the training samples for then
th fold andout
is a list of indices to be used as the testing samples for then
th fold.metrics (string or list of strings) – Evaluation metrics to be watched in CV.
obj (Optional[Callable[[numpy.ndarray, xgboost.core.DMatrix], Tuple[numpy.ndarray, numpy.ndarray]]]) – Custom objective function. See Custom Objective for details.
feval (function) –
Deprecated since version 1.6.0: Use custom_metric instead.
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.LearningRateScheduler(custom_rates)]
shuffle (bool) – Shuffle data before creating folds.
custom_metric (Optional[Callable[[numpy.ndarray, xgboost.core.DMatrix], Tuple[str, float]]]) –
Custom metric function. See Custom Metric for details.
- Returns
evaluation history
- Return type
list(string)
Scikit-Learn API
Scikit-Learn Wrapper interface for XGBoost.
- class xgboost.XGBRegressor(*, objective='reg:squarederror', **kwargs)
Bases:
xgboost.sklearn.XGBModel
,object
Implementation of the scikit-learn API for XGBoost regression.
- Parameters
n_estimators (int) – Number of gradient boosted trees. Equivalent to number of boosting rounds.
max_depth (Optional[int]) – Maximum tree depth for base learners.
learning_rate (Optional[float]) – Boosting learning rate (xgb’s “eta”)
verbosity (Optional[int]) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
objective (Union[str, Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]], NoneType]) – Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below).
booster (Optional[str]) – Specify which booster to use: gbtree, gblinear or dart.
tree_method (Optional[str]) – 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 the parameters document tree method
n_jobs (Optional[int]) – Number of parallel threads used to run xgboost. When used with other Scikit-Learn algorithms like grid search, you may choose which algorithm to parallelize and balance the threads. Creating thread contention will significantly slow down both algorithms.
gamma (Optional[float]) – Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight (Optional[float]) – Minimum sum of instance weight(hessian) needed in a child.
max_delta_step (Optional[float]) – Maximum delta step we allow each tree’s weight estimation to be.
subsample (Optional[float]) – Subsample ratio of the training instance.
colsample_bytree (Optional[float]) – Subsample ratio of columns when constructing each tree.
colsample_bylevel (Optional[float]) – Subsample ratio of columns for each level.
colsample_bynode (Optional[float]) – Subsample ratio of columns for each split.
reg_alpha (Optional[float]) – L1 regularization term on weights (xgb’s alpha).
reg_lambda (Optional[float]) – L2 regularization term on weights (xgb’s lambda).
scale_pos_weight (Optional[float]) – Balancing of positive and negative weights.
base_score (Optional[float]) – The initial prediction score of all instances, global bias.
random_state (Optional[Union[numpy.random.RandomState, int]]) –
Random number seed.
Note
Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.
missing (float, default np.nan) – Value in the data which needs to be present as a missing value.
num_parallel_tree (Optional[int]) – Used for boosting random forest.
monotone_constraints (Optional[Union[Dict[str, int], str]]) – Constraint of variable monotonicity. See tutorial for more information.
interaction_constraints (Optional[Union[str, List[Tuple[str]]]]) – Constraints for interaction representing permitted interactions. The constraints must be specified in the form of a nested list, e.g.
[[0, 1], [2, 3, 4]]
, where each inner list is a group of indices of features that are allowed to interact with each other. See tutorial for more informationimportance_type (Optional[str]) –
The feature importance type for the feature_importances_ property:
For tree model, it’s either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.
For linear model, only “weight” is defined and it’s the normalized coefficients without bias.
gpu_id (Optional[int]) – Device ordinal.
validate_parameters (Optional[bool]) – Give warnings for unknown parameter.
predictor (Optional[str]) – Force XGBoost to use specific predictor, available choices are [cpu_predictor, gpu_predictor].
enable_categorical (bool) –
New in version 1.5.0.
Experimental support for categorical data. Do not set to true unless you are interested in development. Only valid when gpu_hist and dataframe are used.
max_cat_to_onehot (Optional[int]) –
New in version 1.6.0.
Note
This parameter is experimental
A threshold for deciding whether XGBoost should use one-hot encoding based split for categorical data. When number of categories is lesser than the threshold then one-hot encoding is chosen, otherwise the categories will be partitioned into children nodes. Only relevant for regression and binary classification and approx tree method.
eval_metric (Optional[Union[str, List[str], Callable]]) –
New in version 1.6.0.
Metric used for monitoring the training result and early stopping. It can be a string or list of strings as names of predefined metric in XGBoost (See doc/parameter.rst), one of the metrics in
sklearn.metrics
, or any other user defined metric that looks like sklearn.metrics.If custom objective is also provided, then custom metric should implement the corresponding reverse link function.
Unlike the scoring parameter commonly used in scikit-learn, when a callable object is provided, it’s assumed to be a cost function and by default XGBoost will minimize the result during early stopping.
For advanced usage on Early stopping like directly choosing to maximize instead of minimize, see
xgboost.callback.EarlyStopping
.See Custom Objective and Evaluation Metric for more.
Note
This parameter replaces eval_metric in
fit()
method. The old one receives un-transformed prediction regardless of whether custom objective is being used.from sklearn.datasets import load_diabetes from sklearn.metrics import mean_absolute_error X, y = load_diabetes(return_X_y=True) reg = xgb.XGBRegressor( tree_method="hist", eval_metric=mean_absolute_error, ) reg.fit(X, y, eval_set=[(X, y)])
early_stopping_rounds (Optional[int]) –
New in version 1.6.0.
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 in
fit()
.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:
best_score
,best_iteration
andbest_ntree_limit
.Note
This parameter replaces early_stopping_rounds in
fit()
method.callbacks (Optional[List[TrainingCallback]]) –
List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using Callback API. Example:
callbacks = [xgb.callback.EarlyStopping(rounds=early_stopping_rounds, save_best=True)]
kwargs (dict, optional) –
Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found here. 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
Custom objective function
A custom objective function can be provided for the
objective
parameter. In this case, it should have the signatureobjective(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
- Return type
None
- apply(X, ntree_limit=0, iteration_range=None)
Return the predicted leaf every tree for each sample. If the model is trained with early stopping, then best_iteration is used automatically.
- Parameters
- 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 best_iteration: int
The best iteration obtained by early stopping. This attribute is 0-based, for instance if the best iteration is the first round, then best_iteration is 0.
- property coef_: numpy.ndarray
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 callevals_result()
to get evaluation results for all passed eval_sets. When eval_metric is also passed to thefit()
function, the evals_result will contain the eval_metrics passed to thefit()
function.The returned evaluation result is a dictionary:
{'validation_0': {'logloss': ['0.604835', '0.531479']}, 'validation_1': {'logloss': ['0.41965', '0.17686']}}
- Return type
evals_result
- property feature_importances_: numpy.ndarray
Feature importances property, return depends on importance_type parameter.
- Returns
feature_importances_ (array of shape
[n_features]
except for multi-class)linear model, which returns an array with shape (n_features, n_classes)
- property feature_names_in_: numpy.ndarray
Names of features seen during
fit()
. Defined only when X has feature names that are all strings.
- fit(X, y, *, sample_weight=None, base_margin=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, base_margin_eval_set=None, feature_weights=None, callbacks=None)
Fit gradient boosting model.
Note that calling
fit()
multiple times will cause the model object to be re-fit from scratch. To resume training from a previous checkpoint, explicitly passxgb_model
argument.- Parameters
X (Any) – Feature matrix
y (Any) – Labels
base_margin (Optional[Any]) – global bias for each instance.
eval_set (Optional[Sequence[Tuple[Any, Any]]]) – 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.
eval_metric (str, list of str, or callable, optional) –
Deprecated since version 1.6.0: Use eval_metric in
__init__()
orset_params()
instead.early_stopping_rounds (int) –
Deprecated since version 1.6.0: Use early_stopping_rounds in
__init__()
orset_params()
instead.verbose (Optional[bool]) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr.
xgb_model (Optional[Union[xgboost.core.Booster, xgboost.sklearn.XGBModel, str]]) – file name of stored XGBoost model or ‘Booster’ instance XGBoost model to be loaded before training (allows training continuation).
sample_weight_eval_set (Optional[Sequence[Any]]) – A list of the form [L_1, L_2, …, L_n], where each L_i is an array like object storing instance weights for the i-th validation set.
base_margin_eval_set (Optional[Sequence[Any]]) – A list of the form [M_1, M_2, …, M_n], where each M_i is an array like object storing base margin for the i-th validation set.
feature_weights (Optional[Any]) – Weight for each feature, defines the probability of each feature being selected when colsample is being used. All values must be greater than 0, otherwise a ValueError is thrown.
callbacks (Optional[Sequence[xgboost.callback.TrainingCallback]]) –
- Return type
xgboost.sklearn.XGBModel
- 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
- property intercept_: numpy.ndarray
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 or bytearray. Path to file can be local or as an URI.
The model is loaded from XGBoost format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be loaded when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.load_model("model.json") # or model.load_model("model.ubj")
- Parameters
fname (Union[str, bytearray, os.PathLike]) – Input file name or memory buffer(see also save_raw)
- Return type
None
- predict(X, output_margin=False, ntree_limit=None, validate_features=True, base_margin=None, iteration_range=None)
Predict with X. If the model is trained with early stopping, then best_iteration is used automatically. For tree models, when data is on GPU, like cupy array or cuDF dataframe and predictor is not specified, the prediction is run on GPU automatically, otherwise it will run on CPU.
Note
This function is only thread safe for gbtree and dart.
- Parameters
X (Any) – Data to predict with.
output_margin (bool) – Whether to output the raw untransformed margin value.
ntree_limit (Optional[int]) – Deprecated, use iteration_range instead.
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.
iteration_range (Optional[Tuple[int, int]]) –
Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying
iteration_range=(10, 20)
, then only the forests built during [10, 20) (half open set) rounds are used in this prediction.New in version 1.4.0.
- Return type
prediction
- save_model(fname)
Save the model to a file.
The model is saved in an XGBoost internal format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be saved when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.save_model("model.json") # or model.save_model("model.ubj")
- Parameters
fname (string or os.PathLike) – Output file name
- Return type
None
- class xgboost.XGBClassifier(*, objective='binary:logistic', use_label_encoder=False, **kwargs)
Bases:
xgboost.sklearn.XGBModel
,object
Implementation of the scikit-learn API for XGBoost classification.
- Parameters
n_estimators (int) – Number of boosting rounds.
max_depth (Optional[int]) – Maximum tree depth for base learners.
learning_rate (Optional[float]) – Boosting learning rate (xgb’s “eta”)
verbosity (Optional[int]) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
objective (Union[str, Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]], NoneType]) – Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below).
booster (Optional[str]) – Specify which booster to use: gbtree, gblinear or dart.
tree_method (Optional[str]) – 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 the parameters document tree method
n_jobs (Optional[int]) – Number of parallel threads used to run xgboost. When used with other Scikit-Learn algorithms like grid search, you may choose which algorithm to parallelize and balance the threads. Creating thread contention will significantly slow down both algorithms.
gamma (Optional[float]) – Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight (Optional[float]) – Minimum sum of instance weight(hessian) needed in a child.
max_delta_step (Optional[float]) – Maximum delta step we allow each tree’s weight estimation to be.
subsample (Optional[float]) – Subsample ratio of the training instance.
colsample_bytree (Optional[float]) – Subsample ratio of columns when constructing each tree.
colsample_bylevel (Optional[float]) – Subsample ratio of columns for each level.
colsample_bynode (Optional[float]) – Subsample ratio of columns for each split.
reg_alpha (Optional[float]) – L1 regularization term on weights (xgb’s alpha).
reg_lambda (Optional[float]) – L2 regularization term on weights (xgb’s lambda).
scale_pos_weight (Optional[float]) – Balancing of positive and negative weights.
base_score (Optional[float]) – The initial prediction score of all instances, global bias.
random_state (Optional[Union[numpy.random.RandomState, int]]) –
Random number seed.
Note
Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.
missing (float, default np.nan) – Value in the data which needs to be present as a missing value.
num_parallel_tree (Optional[int]) – Used for boosting random forest.
monotone_constraints (Optional[Union[Dict[str, int], str]]) – Constraint of variable monotonicity. See tutorial for more information.
interaction_constraints (Optional[Union[str, List[Tuple[str]]]]) – Constraints for interaction representing permitted interactions. The constraints must be specified in the form of a nested list, e.g.
[[0, 1], [2, 3, 4]]
, where each inner list is a group of indices of features that are allowed to interact with each other. See tutorial for more informationimportance_type (Optional[str]) –
The feature importance type for the feature_importances_ property:
For tree model, it’s either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.
For linear model, only “weight” is defined and it’s the normalized coefficients without bias.
gpu_id (Optional[int]) – Device ordinal.
validate_parameters (Optional[bool]) – Give warnings for unknown parameter.
predictor (Optional[str]) – Force XGBoost to use specific predictor, available choices are [cpu_predictor, gpu_predictor].
enable_categorical (bool) –
New in version 1.5.0.
Experimental support for categorical data. Do not set to true unless you are interested in development. Only valid when gpu_hist and dataframe are used.
max_cat_to_onehot (Optional[int]) –
New in version 1.6.0.
Note
This parameter is experimental
A threshold for deciding whether XGBoost should use one-hot encoding based split for categorical data. When number of categories is lesser than the threshold then one-hot encoding is chosen, otherwise the categories will be partitioned into children nodes. Only relevant for regression and binary classification and approx tree method.
eval_metric (Optional[Union[str, List[str], Callable]]) –
New in version 1.6.0.
Metric used for monitoring the training result and early stopping. It can be a string or list of strings as names of predefined metric in XGBoost (See doc/parameter.rst), one of the metrics in
sklearn.metrics
, or any other user defined metric that looks like sklearn.metrics.If custom objective is also provided, then custom metric should implement the corresponding reverse link function.
Unlike the scoring parameter commonly used in scikit-learn, when a callable object is provided, it’s assumed to be a cost function and by default XGBoost will minimize the result during early stopping.
For advanced usage on Early stopping like directly choosing to maximize instead of minimize, see
xgboost.callback.EarlyStopping
.See Custom Objective and Evaluation Metric for more.
Note
This parameter replaces eval_metric in
fit()
method. The old one receives un-transformed prediction regardless of whether custom objective is being used.from sklearn.datasets import load_diabetes from sklearn.metrics import mean_absolute_error X, y = load_diabetes(return_X_y=True) reg = xgb.XGBRegressor( tree_method="hist", eval_metric=mean_absolute_error, ) reg.fit(X, y, eval_set=[(X, y)])
early_stopping_rounds (Optional[int]) –
New in version 1.6.0.
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 in
fit()
.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:
best_score
,best_iteration
andbest_ntree_limit
.Note
This parameter replaces early_stopping_rounds in
fit()
method.callbacks (Optional[List[TrainingCallback]]) –
List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using Callback API. Example:
callbacks = [xgb.callback.EarlyStopping(rounds=early_stopping_rounds, save_best=True)]
kwargs (dict, optional) –
Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found here. 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
Custom objective function
A custom objective function can be provided for the
objective
parameter. In this case, it should have the signatureobjective(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
use_label_encoder (bool) –
- Return type
None
- apply(X, ntree_limit=0, iteration_range=None)
Return the predicted leaf every tree for each sample. If the model is trained with early stopping, then best_iteration is used automatically.
- Parameters
- 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 best_iteration: int
The best iteration obtained by early stopping. This attribute is 0-based, for instance if the best iteration is the first round, then best_iteration is 0.
- property coef_: numpy.ndarray
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 callevals_result()
to get evaluation results for all passed eval_sets. When eval_metric is also passed to thefit()
function, the evals_result will contain the eval_metrics passed to thefit()
function.The returned evaluation result is a dictionary:
{'validation_0': {'logloss': ['0.604835', '0.531479']}, 'validation_1': {'logloss': ['0.41965', '0.17686']}}
- Return type
evals_result
- property feature_importances_: numpy.ndarray
Feature importances property, return depends on importance_type parameter.
- Returns
feature_importances_ (array of shape
[n_features]
except for multi-class)linear model, which returns an array with shape (n_features, n_classes)
- property feature_names_in_: numpy.ndarray
Names of features seen during
fit()
. Defined only when X has feature names that are all strings.
- fit(X, y, *, sample_weight=None, base_margin=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, base_margin_eval_set=None, feature_weights=None, callbacks=None)
Fit gradient boosting classifier.
Note that calling
fit()
multiple times will cause the model object to be re-fit from scratch. To resume training from a previous checkpoint, explicitly passxgb_model
argument.- Parameters
X (Any) – Feature matrix
y (Any) – Labels
base_margin (Optional[Any]) – global bias for each instance.
eval_set (Optional[Sequence[Tuple[Any, Any]]]) – 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.
eval_metric (str, list of str, or callable, optional) –
Deprecated since version 1.6.0: Use eval_metric in
__init__()
orset_params()
instead.early_stopping_rounds (int) –
Deprecated since version 1.6.0: Use early_stopping_rounds in
__init__()
orset_params()
instead.verbose (Optional[bool]) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr.
xgb_model (Optional[Union[xgboost.core.Booster, str, xgboost.sklearn.XGBModel]]) – file name of stored XGBoost model or ‘Booster’ instance XGBoost model to be loaded before training (allows training continuation).
sample_weight_eval_set (Optional[Sequence[Any]]) – A list of the form [L_1, L_2, …, L_n], where each L_i is an array like object storing instance weights for the i-th validation set.
base_margin_eval_set (Optional[Sequence[Any]]) – A list of the form [M_1, M_2, …, M_n], where each M_i is an array like object storing base margin for the i-th validation set.
feature_weights (Optional[Any]) – Weight for each feature, defines the probability of each feature being selected when colsample is being used. All values must be greater than 0, otherwise a ValueError is thrown.
callbacks (Optional[Sequence[xgboost.callback.TrainingCallback]]) –
- Return type
- 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
- property intercept_: numpy.ndarray
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 or bytearray. Path to file can be local or as an URI.
The model is loaded from XGBoost format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be loaded when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.load_model("model.json") # or model.load_model("model.ubj")
- Parameters
fname (Union[str, bytearray, os.PathLike]) – Input file name or memory buffer(see also save_raw)
- Return type
None
- predict(X, output_margin=False, ntree_limit=None, validate_features=True, base_margin=None, iteration_range=None)
Predict with X. If the model is trained with early stopping, then best_iteration is used automatically. For tree models, when data is on GPU, like cupy array or cuDF dataframe and predictor is not specified, the prediction is run on GPU automatically, otherwise it will run on CPU.
Note
This function is only thread safe for gbtree and dart.
- Parameters
X (Any) – Data to predict with.
output_margin (bool) – Whether to output the raw untransformed margin value.
ntree_limit (Optional[int]) – Deprecated, use iteration_range instead.
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.
iteration_range (Optional[Tuple[int, int]]) –
Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying
iteration_range=(10, 20)
, then only the forests built during [10, 20) (half open set) rounds are used in this prediction.New in version 1.4.0.
- Return type
prediction
- predict_proba(X, ntree_limit=None, validate_features=True, base_margin=None, iteration_range=None)
Predict the probability of each X example being of a given class.
Note
This function is only thread safe for gbtree and dart.
- Parameters
X (array_like) – Feature matrix.
ntree_limit (int) – Deprecated, use iteration_range instead.
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.
base_margin (array_like) – Margin added to prediction.
iteration_range (Optional[Tuple[int, int]]) – Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying iteration_range=(10, 20), then only the forests built during [10, 20) (half open set) rounds are used in this prediction.
- Returns
a numpy array of shape array-like of shape (n_samples, n_classes) with the probability of each data example being of a given class.
- Return type
prediction
- save_model(fname)
Save the model to a file.
The model is saved in an XGBoost internal format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be saved when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.save_model("model.json") # or model.save_model("model.ubj")
- Parameters
fname (string or os.PathLike) – Output file name
- Return type
None
- class xgboost.XGBRanker(*, objective='rank:pairwise', **kwargs)
Bases:
xgboost.sklearn.XGBModel
,xgboost.sklearn.XGBRankerMixIn
Implementation of the Scikit-Learn API for XGBoost Ranking.
- Parameters
n_estimators (int) – Number of gradient boosted trees. Equivalent to number of boosting rounds.
max_depth (Optional[int]) – Maximum tree depth for base learners.
learning_rate (Optional[float]) – Boosting learning rate (xgb’s “eta”)
verbosity (Optional[int]) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
objective (Union[str, Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]], NoneType]) – Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below).
booster (Optional[str]) – Specify which booster to use: gbtree, gblinear or dart.
tree_method (Optional[str]) – 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 the parameters document tree method
n_jobs (Optional[int]) – Number of parallel threads used to run xgboost. When used with other Scikit-Learn algorithms like grid search, you may choose which algorithm to parallelize and balance the threads. Creating thread contention will significantly slow down both algorithms.
gamma (Optional[float]) – Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight (Optional[float]) – Minimum sum of instance weight(hessian) needed in a child.
max_delta_step (Optional[float]) – Maximum delta step we allow each tree’s weight estimation to be.
subsample (Optional[float]) – Subsample ratio of the training instance.
colsample_bytree (Optional[float]) – Subsample ratio of columns when constructing each tree.
colsample_bylevel (Optional[float]) – Subsample ratio of columns for each level.
colsample_bynode (Optional[float]) – Subsample ratio of columns for each split.
reg_alpha (Optional[float]) – L1 regularization term on weights (xgb’s alpha).
reg_lambda (Optional[float]) – L2 regularization term on weights (xgb’s lambda).
scale_pos_weight (Optional[float]) – Balancing of positive and negative weights.
base_score (Optional[float]) – The initial prediction score of all instances, global bias.
random_state (Optional[Union[numpy.random.RandomState, int]]) –
Random number seed.
Note
Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.
missing (float, default np.nan) – Value in the data which needs to be present as a missing value.
num_parallel_tree (Optional[int]) – Used for boosting random forest.
monotone_constraints (Optional[Union[Dict[str, int], str]]) – Constraint of variable monotonicity. See tutorial for more information.
interaction_constraints (Optional[Union[str, List[Tuple[str]]]]) – Constraints for interaction representing permitted interactions. The constraints must be specified in the form of a nested list, e.g.
[[0, 1], [2, 3, 4]]
, where each inner list is a group of indices of features that are allowed to interact with each other. See tutorial for more informationimportance_type (Optional[str]) –
The feature importance type for the feature_importances_ property:
For tree model, it’s either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.
For linear model, only “weight” is defined and it’s the normalized coefficients without bias.
gpu_id (Optional[int]) – Device ordinal.
validate_parameters (Optional[bool]) – Give warnings for unknown parameter.
predictor (Optional[str]) – Force XGBoost to use specific predictor, available choices are [cpu_predictor, gpu_predictor].
enable_categorical (bool) –
New in version 1.5.0.
Experimental support for categorical data. Do not set to true unless you are interested in development. Only valid when gpu_hist and dataframe are used.
max_cat_to_onehot (Optional[int]) –
New in version 1.6.0.
Note
This parameter is experimental
A threshold for deciding whether XGBoost should use one-hot encoding based split for categorical data. When number of categories is lesser than the threshold then one-hot encoding is chosen, otherwise the categories will be partitioned into children nodes. Only relevant for regression and binary classification and approx tree method.
eval_metric (Optional[Union[str, List[str], Callable]]) –
New in version 1.6.0.
Metric used for monitoring the training result and early stopping. It can be a string or list of strings as names of predefined metric in XGBoost (See doc/parameter.rst), one of the metrics in
sklearn.metrics
, or any other user defined metric that looks like sklearn.metrics.If custom objective is also provided, then custom metric should implement the corresponding reverse link function.
Unlike the scoring parameter commonly used in scikit-learn, when a callable object is provided, it’s assumed to be a cost function and by default XGBoost will minimize the result during early stopping.
For advanced usage on Early stopping like directly choosing to maximize instead of minimize, see
xgboost.callback.EarlyStopping
.See Custom Objective and Evaluation Metric for more.
Note
This parameter replaces eval_metric in
fit()
method. The old one receives un-transformed prediction regardless of whether custom objective is being used.from sklearn.datasets import load_diabetes from sklearn.metrics import mean_absolute_error X, y = load_diabetes(return_X_y=True) reg = xgb.XGBRegressor( tree_method="hist", eval_metric=mean_absolute_error, ) reg.fit(X, y, eval_set=[(X, y)])
early_stopping_rounds (Optional[int]) –
New in version 1.6.0.
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 in
fit()
.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:
best_score
,best_iteration
andbest_ntree_limit
.Note
This parameter replaces early_stopping_rounds in
fit()
method.callbacks (Optional[List[TrainingCallback]]) –
List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using Callback API. Example:
callbacks = [xgb.callback.EarlyStopping(rounds=early_stopping_rounds, save_best=True)]
kwargs (dict, optional) –
Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found here. 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 by either using the group parameter or qid parameter in fit method.
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]
. Sometimes using query id (qid) instead of group can be more convenient.
- apply(X, ntree_limit=0, iteration_range=None)
Return the predicted leaf every tree for each sample. If the model is trained with early stopping, then best_iteration is used automatically.
- Parameters
- 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 best_iteration: int
The best iteration obtained by early stopping. This attribute is 0-based, for instance if the best iteration is the first round, then best_iteration is 0.
- property coef_: numpy.ndarray
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 callevals_result()
to get evaluation results for all passed eval_sets. When eval_metric is also passed to thefit()
function, the evals_result will contain the eval_metrics passed to thefit()
function.The returned evaluation result is a dictionary:
{'validation_0': {'logloss': ['0.604835', '0.531479']}, 'validation_1': {'logloss': ['0.41965', '0.17686']}}
- Return type
evals_result
- property feature_importances_: numpy.ndarray
Feature importances property, return depends on importance_type parameter.
- Returns
feature_importances_ (array of shape
[n_features]
except for multi-class)linear model, which returns an array with shape (n_features, n_classes)
- property feature_names_in_: numpy.ndarray
Names of features seen during
fit()
. Defined only when X has feature names that are all strings.
- fit(X, y, *, group=None, qid=None, sample_weight=None, base_margin=None, eval_set=None, eval_group=None, eval_qid=None, eval_metric=None, early_stopping_rounds=None, verbose=False, xgb_model=None, sample_weight_eval_set=None, base_margin_eval_set=None, feature_weights=None, callbacks=None)
Fit gradient boosting ranker
Note that calling
fit()
multiple times will cause the model object to be re-fit from scratch. To resume training from a previous checkpoint, explicitly passxgb_model
argument.- Parameters
X (Any) – Feature matrix
y (Any) – Labels
group (Optional[Any]) – Size of each query group of training data. Should have as many elements as the query groups in the training data. If this is set to None, then user must provide qid.
qid (Optional[Any]) – Query ID for each training sample. Should have the size of n_samples. If this is set to None, then user must provide group.
sample_weight (Optional[Any]) –
Query group weights
Note
Weights are per-group for ranking tasks
In ranking task, one weight is assigned to each query group/id (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.
base_margin (Optional[Any]) – Global bias for each instance.
eval_set (Optional[Sequence[Tuple[Any, Any]]]) – 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.
eval_group (Optional[Sequence[Any]]) – A list in which
eval_group[i]
is the list containing the sizes of all query groups in thei
-th pair in eval_set.eval_qid (Optional[Sequence[Any]]) – A list in which
eval_qid[i]
is the array containing query ID ofi
-th pair in eval_set.eval_metric (str, list of str, optional) –
Deprecated since version 1.6.0: use eval_metric in
__init__()
orset_params()
instead.early_stopping_rounds (int) –
Deprecated since version 1.6.0: use early_stopping_rounds in
__init__()
orset_params()
instead.verbose (Optional[bool]) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr.
xgb_model (Optional[Union[xgboost.core.Booster, str, xgboost.sklearn.XGBModel]]) – file name of stored XGBoost model or ‘Booster’ instance XGBoost model to be loaded before training (allows training continuation).
sample_weight_eval_set (Optional[Sequence[Any]]) –
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.
base_margin_eval_set (Optional[Sequence[Any]]) – A list of the form [M_1, M_2, …, M_n], where each M_i is an array like object storing base margin for the i-th validation set.
feature_weights (Optional[Any]) – Weight for each feature, defines the probability of each feature being selected when colsample is being used. All values must be greater than 0, otherwise a ValueError is thrown.
callbacks (Optional[Sequence[xgboost.callback.TrainingCallback]]) –
- Return type
- 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
- property intercept_: numpy.ndarray
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 or bytearray. Path to file can be local or as an URI.
The model is loaded from XGBoost format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be loaded when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.load_model("model.json") # or model.load_model("model.ubj")
- Parameters
fname (Union[str, bytearray, os.PathLike]) – Input file name or memory buffer(see also save_raw)
- Return type
None
- predict(X, output_margin=False, ntree_limit=None, validate_features=True, base_margin=None, iteration_range=None)
Predict with X. If the model is trained with early stopping, then best_iteration is used automatically. For tree models, when data is on GPU, like cupy array or cuDF dataframe and predictor is not specified, the prediction is run on GPU automatically, otherwise it will run on CPU.
Note
This function is only thread safe for gbtree and dart.
- Parameters
X (Any) – Data to predict with.
output_margin (bool) – Whether to output the raw untransformed margin value.
ntree_limit (Optional[int]) – Deprecated, use iteration_range instead.
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.
iteration_range (Optional[Tuple[int, int]]) –
Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying
iteration_range=(10, 20)
, then only the forests built during [10, 20) (half open set) rounds are used in this prediction.New in version 1.4.0.
- Return type
prediction
- save_model(fname)
Save the model to a file.
The model is saved in an XGBoost internal format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be saved when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.save_model("model.json") # or model.save_model("model.ubj")
- Parameters
fname (string or os.PathLike) – Output file name
- Return type
None
- class xgboost.XGBRFRegressor(*, learning_rate=1.0, subsample=0.8, colsample_bynode=0.8, reg_lambda=1e-05, **kwargs)
Bases:
xgboost.sklearn.XGBRegressor
scikit-learn API for XGBoost random forest regression.
- Parameters
n_estimators (int) – Number of trees in random forest to fit.
max_depth (Optional[int]) – Maximum tree depth for base learners.
learning_rate (Optional[float]) – Boosting learning rate (xgb’s “eta”)
verbosity (Optional[int]) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
objective (Union[str, Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]], NoneType]) – Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below).
booster (Optional[str]) – Specify which booster to use: gbtree, gblinear or dart.
tree_method (Optional[str]) – 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 the parameters document tree method
n_jobs (Optional[int]) – Number of parallel threads used to run xgboost. When used with other Scikit-Learn algorithms like grid search, you may choose which algorithm to parallelize and balance the threads. Creating thread contention will significantly slow down both algorithms.
gamma (Optional[float]) – Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight (Optional[float]) – Minimum sum of instance weight(hessian) needed in a child.
max_delta_step (Optional[float]) – Maximum delta step we allow each tree’s weight estimation to be.
subsample (Optional[float]) – Subsample ratio of the training instance.
colsample_bytree (Optional[float]) – Subsample ratio of columns when constructing each tree.
colsample_bylevel (Optional[float]) – Subsample ratio of columns for each level.
colsample_bynode (Optional[float]) – Subsample ratio of columns for each split.
reg_alpha (Optional[float]) – L1 regularization term on weights (xgb’s alpha).
reg_lambda (Optional[float]) – L2 regularization term on weights (xgb’s lambda).
scale_pos_weight (Optional[float]) – Balancing of positive and negative weights.
base_score (Optional[float]) – The initial prediction score of all instances, global bias.
random_state (Optional[Union[numpy.random.RandomState, int]]) –
Random number seed.
Note
Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.
missing (float, default np.nan) – Value in the data which needs to be present as a missing value.
num_parallel_tree (Optional[int]) – Used for boosting random forest.
monotone_constraints (Optional[Union[Dict[str, int], str]]) – Constraint of variable monotonicity. See tutorial for more information.
interaction_constraints (Optional[Union[str, List[Tuple[str]]]]) – Constraints for interaction representing permitted interactions. The constraints must be specified in the form of a nested list, e.g.
[[0, 1], [2, 3, 4]]
, where each inner list is a group of indices of features that are allowed to interact with each other. See tutorial for more informationimportance_type (Optional[str]) –
The feature importance type for the feature_importances_ property:
For tree model, it’s either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.
For linear model, only “weight” is defined and it’s the normalized coefficients without bias.
gpu_id (Optional[int]) – Device ordinal.
validate_parameters (Optional[bool]) – Give warnings for unknown parameter.
predictor (Optional[str]) – Force XGBoost to use specific predictor, available choices are [cpu_predictor, gpu_predictor].
enable_categorical (bool) –
New in version 1.5.0.
Experimental support for categorical data. Do not set to true unless you are interested in development. Only valid when gpu_hist and dataframe are used.
max_cat_to_onehot (Optional[int]) –
New in version 1.6.0.
Note
This parameter is experimental
A threshold for deciding whether XGBoost should use one-hot encoding based split for categorical data. When number of categories is lesser than the threshold then one-hot encoding is chosen, otherwise the categories will be partitioned into children nodes. Only relevant for regression and binary classification and approx tree method.
eval_metric (Optional[Union[str, List[str], Callable]]) –
New in version 1.6.0.
Metric used for monitoring the training result and early stopping. It can be a string or list of strings as names of predefined metric in XGBoost (See doc/parameter.rst), one of the metrics in
sklearn.metrics
, or any other user defined metric that looks like sklearn.metrics.If custom objective is also provided, then custom metric should implement the corresponding reverse link function.
Unlike the scoring parameter commonly used in scikit-learn, when a callable object is provided, it’s assumed to be a cost function and by default XGBoost will minimize the result during early stopping.
For advanced usage on Early stopping like directly choosing to maximize instead of minimize, see
xgboost.callback.EarlyStopping
.See Custom Objective and Evaluation Metric for more.
Note
This parameter replaces eval_metric in
fit()
method. The old one receives un-transformed prediction regardless of whether custom objective is being used.from sklearn.datasets import load_diabetes from sklearn.metrics import mean_absolute_error X, y = load_diabetes(return_X_y=True) reg = xgb.XGBRegressor( tree_method="hist", eval_metric=mean_absolute_error, ) reg.fit(X, y, eval_set=[(X, y)])
early_stopping_rounds (Optional[int]) –
New in version 1.6.0.
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 in
fit()
.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:
best_score
,best_iteration
andbest_ntree_limit
.Note
This parameter replaces early_stopping_rounds in
fit()
method.callbacks (Optional[List[TrainingCallback]]) –
List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using Callback API. Example:
callbacks = [xgb.callback.EarlyStopping(rounds=early_stopping_rounds, save_best=True)]
kwargs (dict, optional) –
Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found here. 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
Custom objective function
A custom objective function can be provided for the
objective
parameter. In this case, it should have the signatureobjective(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
- Return type
None
- apply(X, ntree_limit=0, iteration_range=None)
Return the predicted leaf every tree for each sample. If the model is trained with early stopping, then best_iteration is used automatically.
- Parameters
- 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 best_iteration: int
The best iteration obtained by early stopping. This attribute is 0-based, for instance if the best iteration is the first round, then best_iteration is 0.
- property coef_: numpy.ndarray
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 callevals_result()
to get evaluation results for all passed eval_sets. When eval_metric is also passed to thefit()
function, the evals_result will contain the eval_metrics passed to thefit()
function.The returned evaluation result is a dictionary:
{'validation_0': {'logloss': ['0.604835', '0.531479']}, 'validation_1': {'logloss': ['0.41965', '0.17686']}}
- Return type
evals_result
- property feature_importances_: numpy.ndarray
Feature importances property, return depends on importance_type parameter.
- Returns
feature_importances_ (array of shape
[n_features]
except for multi-class)linear model, which returns an array with shape (n_features, n_classes)
- property feature_names_in_: numpy.ndarray
Names of features seen during
fit()
. Defined only when X has feature names that are all strings.
- fit(X, y, *, sample_weight=None, base_margin=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, base_margin_eval_set=None, feature_weights=None, callbacks=None)
Fit gradient boosting model.
Note that calling
fit()
multiple times will cause the model object to be re-fit from scratch. To resume training from a previous checkpoint, explicitly passxgb_model
argument.- Parameters
X (Any) – Feature matrix
y (Any) – Labels
base_margin (Optional[Any]) – global bias for each instance.
eval_set (Optional[Sequence[Tuple[Any, Any]]]) – 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.
eval_metric (str, list of str, or callable, optional) –
Deprecated since version 1.6.0: Use eval_metric in
__init__()
orset_params()
instead.early_stopping_rounds (int) –
Deprecated since version 1.6.0: Use early_stopping_rounds in
__init__()
orset_params()
instead.verbose (Optional[bool]) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr.
xgb_model (Optional[Union[xgboost.core.Booster, str, xgboost.sklearn.XGBModel]]) – file name of stored XGBoost model or ‘Booster’ instance XGBoost model to be loaded before training (allows training continuation).
sample_weight_eval_set (Optional[Sequence[Any]]) – A list of the form [L_1, L_2, …, L_n], where each L_i is an array like object storing instance weights for the i-th validation set.
base_margin_eval_set (Optional[Sequence[Any]]) – A list of the form [M_1, M_2, …, M_n], where each M_i is an array like object storing base margin for the i-th validation set.
feature_weights (Optional[Any]) – Weight for each feature, defines the probability of each feature being selected when colsample is being used. All values must be greater than 0, otherwise a ValueError is thrown.
callbacks (Optional[Sequence[xgboost.callback.TrainingCallback]]) –
- Return type
- 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
- property intercept_: numpy.ndarray
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 or bytearray. Path to file can be local or as an URI.
The model is loaded from XGBoost format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be loaded when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.load_model("model.json") # or model.load_model("model.ubj")
- Parameters
fname (Union[str, bytearray, os.PathLike]) – Input file name or memory buffer(see also save_raw)
- Return type
None
- predict(X, output_margin=False, ntree_limit=None, validate_features=True, base_margin=None, iteration_range=None)
Predict with X. If the model is trained with early stopping, then best_iteration is used automatically. For tree models, when data is on GPU, like cupy array or cuDF dataframe and predictor is not specified, the prediction is run on GPU automatically, otherwise it will run on CPU.
Note
This function is only thread safe for gbtree and dart.
- Parameters
X (Any) – Data to predict with.
output_margin (bool) – Whether to output the raw untransformed margin value.
ntree_limit (Optional[int]) – Deprecated, use iteration_range instead.
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.
iteration_range (Optional[Tuple[int, int]]) –
Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying
iteration_range=(10, 20)
, then only the forests built during [10, 20) (half open set) rounds are used in this prediction.New in version 1.4.0.
- Return type
prediction
- save_model(fname)
Save the model to a file.
The model is saved in an XGBoost internal format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be saved when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.save_model("model.json") # or model.save_model("model.ubj")
- Parameters
fname (string or os.PathLike) – Output file name
- Return type
None
- class xgboost.XGBRFClassifier(*, learning_rate=1.0, subsample=0.8, colsample_bynode=0.8, reg_lambda=1e-05, **kwargs)
Bases:
xgboost.sklearn.XGBClassifier
scikit-learn API for XGBoost random forest classification.
- Parameters
n_estimators (int) – Number of trees in random forest to fit.
max_depth (Optional[int]) – Maximum tree depth for base learners.
learning_rate (Optional[float]) – Boosting learning rate (xgb’s “eta”)
verbosity (Optional[int]) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
objective (Union[str, Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]], NoneType]) – Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below).
booster (Optional[str]) – Specify which booster to use: gbtree, gblinear or dart.
tree_method (Optional[str]) – 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 the parameters document tree method
n_jobs (Optional[int]) – Number of parallel threads used to run xgboost. When used with other Scikit-Learn algorithms like grid search, you may choose which algorithm to parallelize and balance the threads. Creating thread contention will significantly slow down both algorithms.
gamma (Optional[float]) – Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight (Optional[float]) – Minimum sum of instance weight(hessian) needed in a child.
max_delta_step (Optional[float]) – Maximum delta step we allow each tree’s weight estimation to be.
subsample (Optional[float]) – Subsample ratio of the training instance.
colsample_bytree (Optional[float]) – Subsample ratio of columns when constructing each tree.
colsample_bylevel (Optional[float]) – Subsample ratio of columns for each level.
colsample_bynode (Optional[float]) – Subsample ratio of columns for each split.
reg_alpha (Optional[float]) – L1 regularization term on weights (xgb’s alpha).
reg_lambda (Optional[float]) – L2 regularization term on weights (xgb’s lambda).
scale_pos_weight (Optional[float]) – Balancing of positive and negative weights.
base_score (Optional[float]) – The initial prediction score of all instances, global bias.
random_state (Optional[Union[numpy.random.RandomState, int]]) –
Random number seed.
Note
Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.
missing (float, default np.nan) – Value in the data which needs to be present as a missing value.
num_parallel_tree (Optional[int]) – Used for boosting random forest.
monotone_constraints (Optional[Union[Dict[str, int], str]]) – Constraint of variable monotonicity. See tutorial for more information.
interaction_constraints (Optional[Union[str, List[Tuple[str]]]]) – Constraints for interaction representing permitted interactions. The constraints must be specified in the form of a nested list, e.g.
[[0, 1], [2, 3, 4]]
, where each inner list is a group of indices of features that are allowed to interact with each other. See tutorial for more informationimportance_type (Optional[str]) –
The feature importance type for the feature_importances_ property:
For tree model, it’s either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.
For linear model, only “weight” is defined and it’s the normalized coefficients without bias.
gpu_id (Optional[int]) – Device ordinal.
validate_parameters (Optional[bool]) – Give warnings for unknown parameter.
predictor (Optional[str]) – Force XGBoost to use specific predictor, available choices are [cpu_predictor, gpu_predictor].
enable_categorical (bool) –
New in version 1.5.0.
Experimental support for categorical data. Do not set to true unless you are interested in development. Only valid when gpu_hist and dataframe are used.
max_cat_to_onehot (Optional[int]) –
New in version 1.6.0.
Note
This parameter is experimental
A threshold for deciding whether XGBoost should use one-hot encoding based split for categorical data. When number of categories is lesser than the threshold then one-hot encoding is chosen, otherwise the categories will be partitioned into children nodes. Only relevant for regression and binary classification and approx tree method.
eval_metric (Optional[Union[str, List[str], Callable]]) –
New in version 1.6.0.
Metric used for monitoring the training result and early stopping. It can be a string or list of strings as names of predefined metric in XGBoost (See doc/parameter.rst), one of the metrics in
sklearn.metrics
, or any other user defined metric that looks like sklearn.metrics.If custom objective is also provided, then custom metric should implement the corresponding reverse link function.
Unlike the scoring parameter commonly used in scikit-learn, when a callable object is provided, it’s assumed to be a cost function and by default XGBoost will minimize the result during early stopping.
For advanced usage on Early stopping like directly choosing to maximize instead of minimize, see
xgboost.callback.EarlyStopping
.See Custom Objective and Evaluation Metric for more.
Note
This parameter replaces eval_metric in
fit()
method. The old one receives un-transformed prediction regardless of whether custom objective is being used.from sklearn.datasets import load_diabetes from sklearn.metrics import mean_absolute_error X, y = load_diabetes(return_X_y=True) reg = xgb.XGBRegressor( tree_method="hist", eval_metric=mean_absolute_error, ) reg.fit(X, y, eval_set=[(X, y)])
early_stopping_rounds (Optional[int]) –
New in version 1.6.0.
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 in
fit()
.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:
best_score
,best_iteration
andbest_ntree_limit
.Note
This parameter replaces early_stopping_rounds in
fit()
method.callbacks (Optional[List[TrainingCallback]]) –
List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using Callback API. Example:
callbacks = [xgb.callback.EarlyStopping(rounds=early_stopping_rounds, save_best=True)]
kwargs (dict, optional) –
Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found here. 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
Custom objective function
A custom objective function can be provided for the
objective
parameter. In this case, it should have the signatureobjective(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, iteration_range=None)
Return the predicted leaf every tree for each sample. If the model is trained with early stopping, then best_iteration is used automatically.
- Parameters
- 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 best_iteration: int
The best iteration obtained by early stopping. This attribute is 0-based, for instance if the best iteration is the first round, then best_iteration is 0.
- property coef_: numpy.ndarray
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 callevals_result()
to get evaluation results for all passed eval_sets. When eval_metric is also passed to thefit()
function, the evals_result will contain the eval_metrics passed to thefit()
function.The returned evaluation result is a dictionary:
{'validation_0': {'logloss': ['0.604835', '0.531479']}, 'validation_1': {'logloss': ['0.41965', '0.17686']}}
- Return type
evals_result
- property feature_importances_: numpy.ndarray
Feature importances property, return depends on importance_type parameter.
- Returns
feature_importances_ (array of shape
[n_features]
except for multi-class)linear model, which returns an array with shape (n_features, n_classes)
- property feature_names_in_: numpy.ndarray
Names of features seen during
fit()
. Defined only when X has feature names that are all strings.
- fit(X, y, *, sample_weight=None, base_margin=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, base_margin_eval_set=None, feature_weights=None, callbacks=None)
Fit gradient boosting classifier.
Note that calling
fit()
multiple times will cause the model object to be re-fit from scratch. To resume training from a previous checkpoint, explicitly passxgb_model
argument.- Parameters
X (Any) – Feature matrix
y (Any) – Labels
base_margin (Optional[Any]) – global bias for each instance.
eval_set (Optional[Sequence[Tuple[Any, Any]]]) – 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.
eval_metric (str, list of str, or callable, optional) –
Deprecated since version 1.6.0: Use eval_metric in
__init__()
orset_params()
instead.early_stopping_rounds (int) –
Deprecated since version 1.6.0: Use early_stopping_rounds in
__init__()
orset_params()
instead.verbose (Optional[bool]) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr.
xgb_model (Optional[Union[xgboost.core.Booster, str, xgboost.sklearn.XGBModel]]) – file name of stored XGBoost model or ‘Booster’ instance XGBoost model to be loaded before training (allows training continuation).
sample_weight_eval_set (Optional[Sequence[Any]]) – A list of the form [L_1, L_2, …, L_n], where each L_i is an array like object storing instance weights for the i-th validation set.
base_margin_eval_set (Optional[Sequence[Any]]) – A list of the form [M_1, M_2, …, M_n], where each M_i is an array like object storing base margin for the i-th validation set.
feature_weights (Optional[Any]) – Weight for each feature, defines the probability of each feature being selected when colsample is being used. All values must be greater than 0, otherwise a ValueError is thrown.
callbacks (Optional[Sequence[xgboost.callback.TrainingCallback]]) –
- Return type
- 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
- property intercept_: numpy.ndarray
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 or bytearray. Path to file can be local or as an URI.
The model is loaded from XGBoost format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be loaded when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.load_model("model.json") # or model.load_model("model.ubj")
- Parameters
fname (Union[str, bytearray, os.PathLike]) – Input file name or memory buffer(see also save_raw)
- Return type
None
- predict(X, output_margin=False, ntree_limit=None, validate_features=True, base_margin=None, iteration_range=None)
Predict with X. If the model is trained with early stopping, then best_iteration is used automatically. For tree models, when data is on GPU, like cupy array or cuDF dataframe and predictor is not specified, the prediction is run on GPU automatically, otherwise it will run on CPU.
Note
This function is only thread safe for gbtree and dart.
- Parameters
X (Any) – Data to predict with.
output_margin (bool) – Whether to output the raw untransformed margin value.
ntree_limit (Optional[int]) – Deprecated, use iteration_range instead.
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.
iteration_range (Optional[Tuple[int, int]]) –
Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying
iteration_range=(10, 20)
, then only the forests built during [10, 20) (half open set) rounds are used in this prediction.New in version 1.4.0.
- Return type
prediction
- predict_proba(X, ntree_limit=None, validate_features=True, base_margin=None, iteration_range=None)
Predict the probability of each X example being of a given class.
Note
This function is only thread safe for gbtree and dart.
- Parameters
X (array_like) – Feature matrix.
ntree_limit (int) – Deprecated, use iteration_range instead.
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.
base_margin (array_like) – Margin added to prediction.
iteration_range (Optional[Tuple[int, int]]) – Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying iteration_range=(10, 20), then only the forests built during [10, 20) (half open set) rounds are used in this prediction.
- Returns
a numpy array of shape array-like of shape (n_samples, n_classes) with the probability of each data example being of a given class.
- Return type
prediction
- save_model(fname)
Save the model to a file.
The model is saved in an XGBoost internal format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be saved when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.save_model("model.json") # or model.save_model("model.ubj")
- Parameters
fname (string or os.PathLike) – Output file name
- Return type
None
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', fmap='', 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.
fmap (str or os.PathLike (optional)) – The name of feature map file.
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 (dict, optional) – Other keywords passed to graphviz graph_attr, e.g.
graph [ {key} = {value} ]
- Returns
graph
- Return type
graphviz.Source
Callback API
Callback library containing training routines. See Callback Functions for a quick introduction.
- class xgboost.callback.TrainingCallback
Interface for training callback.
New in version 1.3.0.
- Return type
None
- after_iteration(model, epoch, evals_log)
Run after each iteration. Return True when training should stop.
- after_training(model)
Run after training is finished.
- before_iteration(model, epoch, evals_log)
Run before each iteration. Return True when training should stop.
- before_training(model)
Run before training starts.
- class xgboost.callback.EvaluationMonitor(rank=0, period=1, show_stdv=False)
Bases:
xgboost.callback.TrainingCallback
Print the evaluation result at each iteration.
New in version 1.3.0.
- Parameters
- Return type
None
- after_iteration(model, epoch, evals_log)
Run after each iteration. Return True when training should stop.
- after_training(model)
Run after training is finished.
- before_iteration(model, epoch, evals_log)
Run before each iteration. Return True when training should stop.
- before_training(model)
Run before training starts.
- class xgboost.callback.EarlyStopping(rounds, metric_name=None, data_name=None, maximize=None, save_best=False, min_delta=0.0)
Bases:
xgboost.callback.TrainingCallback
Callback function for early stopping
New in version 1.3.0.
- Parameters
rounds (int) – Early stopping rounds.
metric_name (Optional[str]) – Name of metric that is used for early stopping.
data_name (Optional[str]) – Name of dataset that is used for early stopping.
maximize (Optional[bool]) – Whether to maximize evaluation metric. None means auto (discouraged).
save_best (Optional[bool]) – Whether training should return the best model or the last model.
min_delta (float) –
Minimum absolute change in score to be qualified as an improvement.
New in version 1.5.0.
clf = xgboost.XGBClassifier(tree_method="gpu_hist") es = xgboost.callback.EarlyStopping( rounds=2, abs_tol=1e-3, save_best=True, maximize=False, data_name="validation_0", metric_name="mlogloss", ) X, y = load_digits(return_X_y=True) clf.fit(X, y, eval_set=[(X, y)], callbacks=[es])
- Return type
None
- after_iteration(model, epoch, evals_log)
Run after each iteration. Return True when training should stop.
- after_training(model)
Run after training is finished.
- before_iteration(model, epoch, evals_log)
Run before each iteration. Return True when training should stop.
- before_training(model)
Run before training starts.
- class xgboost.callback.LearningRateScheduler(learning_rates)
Bases:
xgboost.callback.TrainingCallback
Callback function for scheduling learning rate.
New in version 1.3.0.
- Parameters
learning_rates (callable/collections.Sequence) – If it’s a callable object, then it should accept an integer parameter epoch and returns the corresponding learning rate. Otherwise it should be a sequence like list or tuple with the same size of boosting rounds.
- Return type
None
- after_iteration(model, epoch, evals_log)
Run after each iteration. Return True when training should stop.
- after_training(model)
Run after training is finished.
- before_iteration(model, epoch, evals_log)
Run before each iteration. Return True when training should stop.
- before_training(model)
Run before training starts.
- class xgboost.callback.TrainingCheckPoint(directory, name='model', as_pickle=False, iterations=100)
Bases:
xgboost.callback.TrainingCallback
Checkpointing operation.
New in version 1.3.0.
- Parameters
directory (Union[str, os.PathLike]) – Output model directory.
name (str) – pattern of output model file. Models will be saved as name_0.json, name_1.json, name_2.json ….
as_pickle (bool) – When set to True, all training parameters will be saved in pickle format, instead of saving only the model.
iterations (int) – Interval of checkpointing. Checkpointing is slow so setting a larger number can reduce performance hit.
- Return type
None
- after_iteration(model, epoch, evals_log)
Run after each iteration. Return True when training should stop.
- after_training(model)
Run after training is finished.
- before_iteration(model, epoch, evals_log)
Run before each iteration. Return True when training should stop.
- before_training(model)
Run before training starts.
Dask API
Dask extensions for distributed training
See Distributed XGBoost with Dask for simple tutorial. Also XGBoost Dask Feature Walkthrough 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
Optional dask configuration
xgboost.scheduler_address: Specify the scheduler address, see Tracker Host IP.
New in version 1.6.0.
dask.config.set({"xgboost.scheduler_address": "192.0.0.100"})
- class xgboost.dask.DaskDMatrix(client, data, label=None, *, weight=None, base_margin=None, missing=None, silent=False, feature_names=None, feature_types=None, group=None, qid=None, label_lower_bound=None, label_upper_bound=None, feature_weights=None, enable_categorical=False)
Bases:
object
DMatrix holding on references to Dask DataFrame or Dask Array. Constructing a DaskDMatrix forces all lazy computation to be carried out. Wait for the input data explicitly if you want to see actual computation of constructing DaskDMatrix.
See doc for
xgboost.DMatrix
constructor for other parameters. DaskDMatrix accepts only dask collection.Note
DaskDMatrix does not repartition or move data between workers. It’s the caller’s responsibility to balance the data.
New in version 1.0.0.
- Parameters
client (distributed.Client) – Specify the dask client used for training. Use default client returned from dask if it’s set to None.
data (Union[da.Array, dd.DataFrame, dd.Series]) –
label (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) –
weight (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) –
base_margin (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) –
missing (float) –
silent (bool) –
group (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) –
label_lower_bound (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) –
label_upper_bound (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) –
feature_weights (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) –
enable_categorical (bool) –
- Return type
None
- class xgboost.dask.DaskDeviceQuantileDMatrix(client, data, label=None, *, weight=None, base_margin=None, missing=None, silent=False, feature_names=None, feature_types=None, max_bin=256, group=None, qid=None, label_lower_bound=None, label_upper_bound=None, feature_weights=None, enable_categorical=False)
Bases:
xgboost.dask.DaskDMatrix
Specialized data type for gpu_hist tree method. This class is used to reduce the memory usage by eliminating data copies. Internally the all partitions/chunks of data are merged by weighted GK sketching. So the number of partitions from dask may affect training accuracy as GK generates bounded error for each merge. See doc string for
xgboost.DeviceQuantileDMatrix
andxgboost.DMatrix
for other parameters.New in version 1.2.0.
- Parameters
max_bin (Number of bins for histogram construction.) –
client (distributed.Client) –
data (Union[da.Array, dd.DataFrame, dd.Series]) –
label (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) –
weight (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) –
base_margin (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) –
missing (float) –
silent (bool) –
group (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) –
label_lower_bound (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) –
label_upper_bound (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) –
feature_weights (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) –
enable_categorical (bool) –
- Return type
None
- xgboost.dask.train(client, params, dtrain, num_boost_round=10, *, evals=None, obj=None, feval=None, early_stopping_rounds=None, xgb_model=None, verbose_eval=True, callbacks=None, custom_metric=None)
Train XGBoost model.
New in version 1.0.0.
Note
Other parameters are the same as
xgboost.train()
except for evals_result, which is returned as part of function return value instead of argument.- Parameters
client (distributed.Client) – Specify the dask client used for training. Use default client returned from dask if it’s set to None.
dtrain (xgboost.dask.DaskDMatrix) –
num_boost_round (int) –
evals (Optional[Sequence[Tuple[xgboost.dask.DaskDMatrix, str]]]) –
obj (Optional[Callable[[numpy.ndarray, xgboost.core.DMatrix], Tuple[numpy.ndarray, numpy.ndarray]]]) –
feval (Optional[Callable[[numpy.ndarray, xgboost.core.DMatrix], Tuple[str, float]]]) –
xgb_model (Optional[xgboost.core.Booster]) –
callbacks (Optional[Sequence[xgboost.callback.TrainingCallback]]) –
custom_metric (Optional[Callable[[numpy.ndarray, xgboost.core.DMatrix], Tuple[str, float]]]) –
- Returns
results – A dictionary containing trained booster and evaluation history. history field is the same as eval_result from xgboost.train.
{'booster': xgboost.Booster, 'history': {'train': {'logloss': ['0.48253', '0.35953']}, 'eval': {'logloss': ['0.480385', '0.357756']}}}
- Return type
- xgboost.dask.predict(client, model, data, output_margin=False, missing=nan, pred_leaf=False, pred_contribs=False, approx_contribs=False, pred_interactions=False, validate_features=True, iteration_range=(0, 0), strict_shape=False)
Run prediction with a trained booster.
Note
Using
inplace_predict
might be faster when some features are not needed. Seexgboost.Booster.predict()
for details on various parameters. When output has more than 2 dimensions (shap value, leaf with strict_shape), input should beda.Array
orDaskDMatrix
.New in version 1.0.0.
- Parameters
client (distributed.Client) – Specify the dask client used for training. Use default client returned from dask if it’s set to None.
model (Union[Dict[str, Any], xgboost.core.Booster, distributed.Future]) – The trained model. It can be a distributed.Future so user can pre-scatter it onto all workers.
data (Union[xgboost.dask.DaskDMatrix, da.Array, dd.DataFrame, dd.Series]) – Input data used for prediction. When input is a dataframe object, prediction output is a series.
missing (float) – Used when input data is not DaskDMatrix. Specify the value considered as missing.
output_margin (bool) –
pred_leaf (bool) –
pred_contribs (bool) –
approx_contribs (bool) –
pred_interactions (bool) –
validate_features (bool) –
strict_shape (bool) –
- Returns
prediction – When input data is
dask.array.Array
orDaskDMatrix
, the return value is an array, when input data isdask.dataframe.DataFrame
, return value can bedask.dataframe.Series
,dask.dataframe.DataFrame
, depending on the output shape.- Return type
dask.array.Array/dask.dataframe.Series
- xgboost.dask.inplace_predict(client, model, data, iteration_range=(0, 0), predict_type='value', missing=nan, validate_features=True, base_margin=None, strict_shape=False)
Inplace prediction. See doc in
xgboost.Booster.inplace_predict()
for details.New in version 1.1.0.
- Parameters
client (distributed.Client) – Specify the dask client used for training. Use default client returned from dask if it’s set to None.
model (Union[Dict[str, Any], xgboost.core.Booster, distributed.Future]) – See
xgboost.dask.predict()
for details.data (Union[da.Array, dd.DataFrame, dd.Series]) – dask collection.
iteration_range (Tuple[int, int]) – See
xgboost.Booster.predict()
for details.predict_type (str) – See
xgboost.Booster.inplace_predict()
for details.missing (float) – Value in the input data which needs to be present as a missing value. If None, defaults to np.nan.
base_margin (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) –
See
xgboost.DMatrix
for details.New in version 1.4.0.
strict_shape (bool) –
See
xgboost.Booster.predict()
for details.New in version 1.4.0.
validate_features (bool) –
- Returns
When input data is
dask.array.Array
, the return value is an array, when input data isdask.dataframe.DataFrame
, return value can bedask.dataframe.Series
,dask.dataframe.DataFrame
, depending on the output shape.- Return type
prediction
- class xgboost.dask.DaskXGBClassifier(max_depth=None, learning_rate=None, n_estimators=100, verbosity=None, objective=None, booster=None, tree_method=None, n_jobs=None, gamma=None, min_child_weight=None, max_delta_step=None, subsample=None, colsample_bytree=None, colsample_bylevel=None, colsample_bynode=None, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, base_score=None, random_state=None, missing=nan, num_parallel_tree=None, monotone_constraints=None, interaction_constraints=None, importance_type=None, gpu_id=None, validate_parameters=None, predictor=None, enable_categorical=False, max_cat_to_onehot=None, eval_metric=None, early_stopping_rounds=None, callbacks=None, **kwargs)
Bases:
xgboost.dask.DaskScikitLearnBase
,object
Implementation of the scikit-learn API for XGBoost classification.
- Parameters
n_estimators (int) – Number of gradient boosted trees. Equivalent to number of boosting rounds.
max_depth (Optional[int]) – Maximum tree depth for base learners.
learning_rate (Optional[float]) – Boosting learning rate (xgb’s “eta”)
verbosity (Optional[int]) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
objective (Union[str, Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]], NoneType]) – Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below).
booster (Optional[str]) – Specify which booster to use: gbtree, gblinear or dart.
tree_method (Optional[str]) – 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 the parameters document tree method
n_jobs (Optional[int]) – Number of parallel threads used to run xgboost. When used with other Scikit-Learn algorithms like grid search, you may choose which algorithm to parallelize and balance the threads. Creating thread contention will significantly slow down both algorithms.
gamma (Optional[float]) – Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight (Optional[float]) – Minimum sum of instance weight(hessian) needed in a child.
max_delta_step (Optional[float]) – Maximum delta step we allow each tree’s weight estimation to be.
subsample (Optional[float]) – Subsample ratio of the training instance.
colsample_bytree (Optional[float]) – Subsample ratio of columns when constructing each tree.
colsample_bylevel (Optional[float]) – Subsample ratio of columns for each level.
colsample_bynode (Optional[float]) – Subsample ratio of columns for each split.
reg_alpha (Optional[float]) – L1 regularization term on weights (xgb’s alpha).
reg_lambda (Optional[float]) – L2 regularization term on weights (xgb’s lambda).
scale_pos_weight (Optional[float]) – Balancing of positive and negative weights.
base_score (Optional[float]) – The initial prediction score of all instances, global bias.
random_state (Optional[Union[numpy.random.RandomState, int]]) –
Random number seed.
Note
Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.
missing (float, default np.nan) – Value in the data which needs to be present as a missing value.
num_parallel_tree (Optional[int]) – Used for boosting random forest.
monotone_constraints (Optional[Union[Dict[str, int], str]]) – Constraint of variable monotonicity. See tutorial for more information.
interaction_constraints (Optional[Union[str, List[Tuple[str]]]]) – Constraints for interaction representing permitted interactions. The constraints must be specified in the form of a nested list, e.g.
[[0, 1], [2, 3, 4]]
, where each inner list is a group of indices of features that are allowed to interact with each other. See tutorial for more informationimportance_type (Optional[str]) –
The feature importance type for the feature_importances_ property:
For tree model, it’s either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.
For linear model, only “weight” is defined and it’s the normalized coefficients without bias.
gpu_id (Optional[int]) – Device ordinal.
validate_parameters (Optional[bool]) – Give warnings for unknown parameter.
predictor (Optional[str]) – Force XGBoost to use specific predictor, available choices are [cpu_predictor, gpu_predictor].
enable_categorical (bool) –
New in version 1.5.0.
Experimental support for categorical data. Do not set to true unless you are interested in development. Only valid when gpu_hist and dataframe are used.
max_cat_to_onehot (Optional[int]) –
New in version 1.6.0.
Note
This parameter is experimental
A threshold for deciding whether XGBoost should use one-hot encoding based split for categorical data. When number of categories is lesser than the threshold then one-hot encoding is chosen, otherwise the categories will be partitioned into children nodes. Only relevant for regression and binary classification and approx tree method.
eval_metric (Optional[Union[str, List[str], Callable]]) –
New in version 1.6.0.
Metric used for monitoring the training result and early stopping. It can be a string or list of strings as names of predefined metric in XGBoost (See doc/parameter.rst), one of the metrics in
sklearn.metrics
, or any other user defined metric that looks like sklearn.metrics.If custom objective is also provided, then custom metric should implement the corresponding reverse link function.
Unlike the scoring parameter commonly used in scikit-learn, when a callable object is provided, it’s assumed to be a cost function and by default XGBoost will minimize the result during early stopping.
For advanced usage on Early stopping like directly choosing to maximize instead of minimize, see
xgboost.callback.EarlyStopping
.See Custom Objective and Evaluation Metric for more.
Note
This parameter replaces eval_metric in
fit()
method. The old one receives un-transformed prediction regardless of whether custom objective is being used.from sklearn.datasets import load_diabetes from sklearn.metrics import mean_absolute_error X, y = load_diabetes(return_X_y=True) reg = xgb.XGBRegressor( tree_method="hist", eval_metric=mean_absolute_error, ) reg.fit(X, y, eval_set=[(X, y)])
early_stopping_rounds (Optional[int]) –
New in version 1.6.0.
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 in
fit()
.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:
best_score
,best_iteration
andbest_ntree_limit
.Note
This parameter replaces early_stopping_rounds in
fit()
method.callbacks (Optional[List[TrainingCallback]]) –
List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using Callback API. Example:
callbacks = [xgb.callback.EarlyStopping(rounds=early_stopping_rounds, save_best=True)]
kwargs (dict, optional) –
Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found here. 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.
- Return type
None
- apply(X, ntree_limit=None, iteration_range=None)
Return the predicted leaf every tree for each sample. If the model is trained with early stopping, then best_iteration is used automatically.
- Parameters
- 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 best_iteration: int
The best iteration obtained by early stopping. This attribute is 0-based, for instance if the best iteration is the first round, then best_iteration is 0.
- property client: distributed.Client
The dask client used in this model. The Client object can not be serialized for transmission, so if task is launched from a worker instead of directly from the client process, this attribute needs to be set at that worker.
- property coef_: numpy.ndarray
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 callevals_result()
to get evaluation results for all passed eval_sets. When eval_metric is also passed to thefit()
function, the evals_result will contain the eval_metrics passed to thefit()
function.The returned evaluation result is a dictionary:
{'validation_0': {'logloss': ['0.604835', '0.531479']}, 'validation_1': {'logloss': ['0.41965', '0.17686']}}
- Return type
evals_result
- property feature_importances_: numpy.ndarray
Feature importances property, return depends on importance_type parameter.
- Returns
feature_importances_ (array of shape
[n_features]
except for multi-class)linear model, which returns an array with shape (n_features, n_classes)
- property feature_names_in_: numpy.ndarray
Names of features seen during
fit()
. Defined only when X has feature names that are all strings.
- fit(X, y, *, sample_weight=None, base_margin=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, base_margin_eval_set=None, feature_weights=None, callbacks=None)
Fit gradient boosting model.
Note that calling
fit()
multiple times will cause the model object to be re-fit from scratch. To resume training from a previous checkpoint, explicitly passxgb_model
argument.- Parameters
X (Union[da.Array, dd.DataFrame, dd.Series]) – Feature matrix
y (Union[da.Array, dd.DataFrame, dd.Series]) – Labels
sample_weight (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – instance weights
base_margin (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – global bias for each instance.
eval_set (Optional[Sequence[Tuple[Union[da.Array, dd.DataFrame, dd.Series], Union[da.Array, dd.DataFrame, dd.Series]]]]) – 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.
eval_metric (str, list of str, or callable, optional) –
Deprecated since version 1.6.0: Use eval_metric in
__init__()
orset_params()
instead.early_stopping_rounds (int) –
Deprecated since version 1.6.0: Use early_stopping_rounds in
__init__()
orset_params()
instead.verbose (bool) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr.
xgb_model (Optional[Union[xgboost.core.Booster, xgboost.sklearn.XGBModel]]) – file name of stored XGBoost model or ‘Booster’ instance XGBoost model to be loaded before training (allows training continuation).
sample_weight_eval_set (Optional[Sequence[Union[da.Array, dd.DataFrame, dd.Series]]]) – A list of the form [L_1, L_2, …, L_n], where each L_i is an array like object storing instance weights for the i-th validation set.
base_margin_eval_set (Optional[Sequence[Union[da.Array, dd.DataFrame, dd.Series]]]) – A list of the form [M_1, M_2, …, M_n], where each M_i is an array like object storing base margin for the i-th validation set.
feature_weights (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – Weight for each feature, defines the probability of each feature being selected when colsample is being used. All values must be greater than 0, otherwise a ValueError is thrown.
callbacks (Optional[Sequence[xgboost.callback.TrainingCallback]]) –
- Return type
- 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
- property intercept_: numpy.ndarray
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 or bytearray. Path to file can be local or as an URI.
The model is loaded from XGBoost format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be loaded when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.load_model("model.json") # or model.load_model("model.ubj")
- Parameters
fname (Union[str, bytearray, os.PathLike]) – Input file name or memory buffer(see also save_raw)
- Return type
None
- predict(X, output_margin=False, ntree_limit=None, validate_features=True, base_margin=None, iteration_range=None)
Predict with X. If the model is trained with early stopping, then best_iteration is used automatically. For tree models, when data is on GPU, like cupy array or cuDF dataframe and predictor is not specified, the prediction is run on GPU automatically, otherwise it will run on CPU.
Note
This function is only thread safe for gbtree and dart.
- Parameters
X (Union[da.Array, dd.DataFrame, dd.Series]) – Data to predict with.
output_margin (bool) – Whether to output the raw untransformed margin value.
ntree_limit (Optional[int]) – Deprecated, use iteration_range instead.
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.
base_margin (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – Margin added to prediction.
iteration_range (Optional[Tuple[int, int]]) –
Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying
iteration_range=(10, 20)
, then only the forests built during [10, 20) (half open set) rounds are used in this prediction.New in version 1.4.0.
- Return type
prediction
- predict_proba(X, ntree_limit=None, validate_features=True, base_margin=None, iteration_range=None)
Predict the probability of each X example being of a given class.
Note
This function is only thread safe for gbtree and dart.
- Parameters
X (array_like) – Feature matrix.
ntree_limit (int) – Deprecated, use iteration_range instead.
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.
base_margin (array_like) – Margin added to prediction.
iteration_range (Optional[Tuple[int, int]]) – Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying iteration_range=(10, 20), then only the forests built during [10, 20) (half open set) rounds are used in this prediction.
- Returns
a numpy array of shape array-like of shape (n_samples, n_classes) with the probability of each data example being of a given class.
- Return type
prediction
- save_model(fname)
Save the model to a file.
The model is saved in an XGBoost internal format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be saved when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.save_model("model.json") # or model.save_model("model.ubj")
- Parameters
fname (string or os.PathLike) – Output file name
- Return type
None
- class xgboost.dask.DaskXGBRegressor(max_depth=None, learning_rate=None, n_estimators=100, verbosity=None, objective=None, booster=None, tree_method=None, n_jobs=None, gamma=None, min_child_weight=None, max_delta_step=None, subsample=None, colsample_bytree=None, colsample_bylevel=None, colsample_bynode=None, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, base_score=None, random_state=None, missing=nan, num_parallel_tree=None, monotone_constraints=None, interaction_constraints=None, importance_type=None, gpu_id=None, validate_parameters=None, predictor=None, enable_categorical=False, max_cat_to_onehot=None, eval_metric=None, early_stopping_rounds=None, callbacks=None, **kwargs)
Bases:
xgboost.dask.DaskScikitLearnBase
,object
Implementation of the Scikit-Learn API for XGBoost.
- Parameters
n_estimators (int) – Number of gradient boosted trees. Equivalent to number of boosting rounds.
max_depth (Optional[int]) – Maximum tree depth for base learners.
learning_rate (Optional[float]) – Boosting learning rate (xgb’s “eta”)
verbosity (Optional[int]) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
objective (Union[str, Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]], NoneType]) – Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below).
booster (Optional[str]) – Specify which booster to use: gbtree, gblinear or dart.
tree_method (Optional[str]) – 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 the parameters document tree method
n_jobs (Optional[int]) – Number of parallel threads used to run xgboost. When used with other Scikit-Learn algorithms like grid search, you may choose which algorithm to parallelize and balance the threads. Creating thread contention will significantly slow down both algorithms.
gamma (Optional[float]) – Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight (Optional[float]) – Minimum sum of instance weight(hessian) needed in a child.
max_delta_step (Optional[float]) – Maximum delta step we allow each tree’s weight estimation to be.
subsample (Optional[float]) – Subsample ratio of the training instance.
colsample_bytree (Optional[float]) – Subsample ratio of columns when constructing each tree.
colsample_bylevel (Optional[float]) – Subsample ratio of columns for each level.
colsample_bynode (Optional[float]) – Subsample ratio of columns for each split.
reg_alpha (Optional[float]) – L1 regularization term on weights (xgb’s alpha).
reg_lambda (Optional[float]) – L2 regularization term on weights (xgb’s lambda).
scale_pos_weight (Optional[float]) – Balancing of positive and negative weights.
base_score (Optional[float]) – The initial prediction score of all instances, global bias.
random_state (Optional[Union[numpy.random.RandomState, int]]) –
Random number seed.
Note
Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.
missing (float, default np.nan) – Value in the data which needs to be present as a missing value.
num_parallel_tree (Optional[int]) – Used for boosting random forest.
monotone_constraints (Optional[Union[Dict[str, int], str]]) – Constraint of variable monotonicity. See tutorial for more information.
interaction_constraints (Optional[Union[str, List[Tuple[str]]]]) – Constraints for interaction representing permitted interactions. The constraints must be specified in the form of a nested list, e.g.
[[0, 1], [2, 3, 4]]
, where each inner list is a group of indices of features that are allowed to interact with each other. See tutorial for more informationimportance_type (Optional[str]) –
The feature importance type for the feature_importances_ property:
For tree model, it’s either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.
For linear model, only “weight” is defined and it’s the normalized coefficients without bias.
gpu_id (Optional[int]) – Device ordinal.
validate_parameters (Optional[bool]) – Give warnings for unknown parameter.
predictor (Optional[str]) – Force XGBoost to use specific predictor, available choices are [cpu_predictor, gpu_predictor].
enable_categorical (bool) –
New in version 1.5.0.
Experimental support for categorical data. Do not set to true unless you are interested in development. Only valid when gpu_hist and dataframe are used.
max_cat_to_onehot (Optional[int]) –
New in version 1.6.0.
Note
This parameter is experimental
A threshold for deciding whether XGBoost should use one-hot encoding based split for categorical data. When number of categories is lesser than the threshold then one-hot encoding is chosen, otherwise the categories will be partitioned into children nodes. Only relevant for regression and binary classification and approx tree method.
eval_metric (Optional[Union[str, List[str], Callable]]) –
New in version 1.6.0.
Metric used for monitoring the training result and early stopping. It can be a string or list of strings as names of predefined metric in XGBoost (See doc/parameter.rst), one of the metrics in
sklearn.metrics
, or any other user defined metric that looks like sklearn.metrics.If custom objective is also provided, then custom metric should implement the corresponding reverse link function.
Unlike the scoring parameter commonly used in scikit-learn, when a callable object is provided, it’s assumed to be a cost function and by default XGBoost will minimize the result during early stopping.
For advanced usage on Early stopping like directly choosing to maximize instead of minimize, see
xgboost.callback.EarlyStopping
.See Custom Objective and Evaluation Metric for more.
Note
This parameter replaces eval_metric in
fit()
method. The old one receives un-transformed prediction regardless of whether custom objective is being used.from sklearn.datasets import load_diabetes from sklearn.metrics import mean_absolute_error X, y = load_diabetes(return_X_y=True) reg = xgb.XGBRegressor( tree_method="hist", eval_metric=mean_absolute_error, ) reg.fit(X, y, eval_set=[(X, y)])
early_stopping_rounds (Optional[int]) –
New in version 1.6.0.
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 in
fit()
.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:
best_score
,best_iteration
andbest_ntree_limit
.Note
This parameter replaces early_stopping_rounds in
fit()
method.callbacks (Optional[List[TrainingCallback]]) –
List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using Callback API. Example:
callbacks = [xgb.callback.EarlyStopping(rounds=early_stopping_rounds, save_best=True)]
kwargs (dict, optional) –
Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found here. 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.
- Return type
None
- apply(X, ntree_limit=None, iteration_range=None)
Return the predicted leaf every tree for each sample. If the model is trained with early stopping, then best_iteration is used automatically.
- Parameters
- 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 best_iteration: int
The best iteration obtained by early stopping. This attribute is 0-based, for instance if the best iteration is the first round, then best_iteration is 0.
- property client: distributed.Client
The dask client used in this model. The Client object can not be serialized for transmission, so if task is launched from a worker instead of directly from the client process, this attribute needs to be set at that worker.
- property coef_: numpy.ndarray
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 callevals_result()
to get evaluation results for all passed eval_sets. When eval_metric is also passed to thefit()
function, the evals_result will contain the eval_metrics passed to thefit()
function.The returned evaluation result is a dictionary:
{'validation_0': {'logloss': ['0.604835', '0.531479']}, 'validation_1': {'logloss': ['0.41965', '0.17686']}}
- Return type
evals_result
- property feature_importances_: numpy.ndarray
Feature importances property, return depends on importance_type parameter.
- Returns
feature_importances_ (array of shape
[n_features]
except for multi-class)linear model, which returns an array with shape (n_features, n_classes)
- property feature_names_in_: numpy.ndarray
Names of features seen during
fit()
. Defined only when X has feature names that are all strings.
- fit(X, y, *, sample_weight=None, base_margin=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, base_margin_eval_set=None, feature_weights=None, callbacks=None)
Fit gradient boosting model.
Note that calling
fit()
multiple times will cause the model object to be re-fit from scratch. To resume training from a previous checkpoint, explicitly passxgb_model
argument.- Parameters
X (Union[da.Array, dd.DataFrame, dd.Series]) – Feature matrix
y (Union[da.Array, dd.DataFrame, dd.Series]) – Labels
sample_weight (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – instance weights
base_margin (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – global bias for each instance.
eval_set (Optional[Sequence[Tuple[Union[da.Array, dd.DataFrame, dd.Series], Union[da.Array, dd.DataFrame, dd.Series]]]]) – 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.
eval_metric (str, list of str, or callable, optional) –
Deprecated since version 1.6.0: Use eval_metric in
__init__()
orset_params()
instead.early_stopping_rounds (int) –
Deprecated since version 1.6.0: Use early_stopping_rounds in
__init__()
orset_params()
instead.verbose (bool) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr.
xgb_model (Optional[Union[xgboost.core.Booster, xgboost.sklearn.XGBModel]]) – file name of stored XGBoost model or ‘Booster’ instance XGBoost model to be loaded before training (allows training continuation).
sample_weight_eval_set (Optional[Sequence[Union[da.Array, dd.DataFrame, dd.Series]]]) – A list of the form [L_1, L_2, …, L_n], where each L_i is an array like object storing instance weights for the i-th validation set.
base_margin_eval_set (Optional[Sequence[Union[da.Array, dd.DataFrame, dd.Series]]]) – A list of the form [M_1, M_2, …, M_n], where each M_i is an array like object storing base margin for the i-th validation set.
feature_weights (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – Weight for each feature, defines the probability of each feature being selected when colsample is being used. All values must be greater than 0, otherwise a ValueError is thrown.
callbacks (Optional[Sequence[xgboost.callback.TrainingCallback]]) –
- Return type
- 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
- property intercept_: numpy.ndarray
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 or bytearray. Path to file can be local or as an URI.
The model is loaded from XGBoost format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be loaded when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.load_model("model.json") # or model.load_model("model.ubj")
- Parameters
fname (Union[str, bytearray, os.PathLike]) – Input file name or memory buffer(see also save_raw)
- Return type
None
- predict(X, output_margin=False, ntree_limit=None, validate_features=True, base_margin=None, iteration_range=None)
Predict with X. If the model is trained with early stopping, then best_iteration is used automatically. For tree models, when data is on GPU, like cupy array or cuDF dataframe and predictor is not specified, the prediction is run on GPU automatically, otherwise it will run on CPU.
Note
This function is only thread safe for gbtree and dart.
- Parameters
X (Union[da.Array, dd.DataFrame, dd.Series]) – Data to predict with.
output_margin (bool) – Whether to output the raw untransformed margin value.
ntree_limit (Optional[int]) – Deprecated, use iteration_range instead.
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.
base_margin (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – Margin added to prediction.
iteration_range (Optional[Tuple[int, int]]) –
Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying
iteration_range=(10, 20)
, then only the forests built during [10, 20) (half open set) rounds are used in this prediction.New in version 1.4.0.
- Return type
prediction
- save_model(fname)
Save the model to a file.
The model is saved in an XGBoost internal format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be saved when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.save_model("model.json") # or model.save_model("model.ubj")
- Parameters
fname (string or os.PathLike) – Output file name
- Return type
None
- class xgboost.dask.DaskXGBRanker(*, objective='rank:pairwise', **kwargs)
Bases:
xgboost.dask.DaskScikitLearnBase
,xgboost.sklearn.XGBRankerMixIn
Implementation of the Scikit-Learn API for XGBoost Ranking.
New in version 1.4.0.
- Parameters
n_estimators (int) – Number of gradient boosted trees. Equivalent to number of boosting rounds.
max_depth (Optional[int]) – Maximum tree depth for base learners.
learning_rate (Optional[float]) – Boosting learning rate (xgb’s “eta”)
verbosity (Optional[int]) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
objective (Union[str, Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]], NoneType]) – Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below).
booster (Optional[str]) – Specify which booster to use: gbtree, gblinear or dart.
tree_method (Optional[str]) – 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 the parameters document tree method
n_jobs (Optional[int]) – Number of parallel threads used to run xgboost. When used with other Scikit-Learn algorithms like grid search, you may choose which algorithm to parallelize and balance the threads. Creating thread contention will significantly slow down both algorithms.
gamma (Optional[float]) – Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight (Optional[float]) – Minimum sum of instance weight(hessian) needed in a child.
max_delta_step (Optional[float]) – Maximum delta step we allow each tree’s weight estimation to be.
subsample (Optional[float]) – Subsample ratio of the training instance.
colsample_bytree (Optional[float]) – Subsample ratio of columns when constructing each tree.
colsample_bylevel (Optional[float]) – Subsample ratio of columns for each level.
colsample_bynode (Optional[float]) – Subsample ratio of columns for each split.
reg_alpha (Optional[float]) – L1 regularization term on weights (xgb’s alpha).
reg_lambda (Optional[float]) – L2 regularization term on weights (xgb’s lambda).
scale_pos_weight (Optional[float]) – Balancing of positive and negative weights.
base_score (Optional[float]) – The initial prediction score of all instances, global bias.
random_state (Optional[Union[numpy.random.RandomState, int]]) –
Random number seed.
Note
Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.
missing (float, default np.nan) – Value in the data which needs to be present as a missing value.
num_parallel_tree (Optional[int]) – Used for boosting random forest.
monotone_constraints (Optional[Union[Dict[str, int], str]]) – Constraint of variable monotonicity. See tutorial for more information.
interaction_constraints (Optional[Union[str, List[Tuple[str]]]]) – Constraints for interaction representing permitted interactions. The constraints must be specified in the form of a nested list, e.g.
[[0, 1], [2, 3, 4]]
, where each inner list is a group of indices of features that are allowed to interact with each other. See tutorial for more informationimportance_type (Optional[str]) –
The feature importance type for the feature_importances_ property:
For tree model, it’s either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.
For linear model, only “weight” is defined and it’s the normalized coefficients without bias.
gpu_id (Optional[int]) – Device ordinal.
validate_parameters (Optional[bool]) – Give warnings for unknown parameter.
predictor (Optional[str]) – Force XGBoost to use specific predictor, available choices are [cpu_predictor, gpu_predictor].
enable_categorical (bool) –
New in version 1.5.0.
Experimental support for categorical data. Do not set to true unless you are interested in development. Only valid when gpu_hist and dataframe are used.
max_cat_to_onehot (Optional[int]) –
New in version 1.6.0.
Note
This parameter is experimental
A threshold for deciding whether XGBoost should use one-hot encoding based split for categorical data. When number of categories is lesser than the threshold then one-hot encoding is chosen, otherwise the categories will be partitioned into children nodes. Only relevant for regression and binary classification and approx tree method.
eval_metric (Optional[Union[str, List[str], Callable]]) –
New in version 1.6.0.
Metric used for monitoring the training result and early stopping. It can be a string or list of strings as names of predefined metric in XGBoost (See doc/parameter.rst), one of the metrics in
sklearn.metrics
, or any other user defined metric that looks like sklearn.metrics.If custom objective is also provided, then custom metric should implement the corresponding reverse link function.
Unlike the scoring parameter commonly used in scikit-learn, when a callable object is provided, it’s assumed to be a cost function and by default XGBoost will minimize the result during early stopping.
For advanced usage on Early stopping like directly choosing to maximize instead of minimize, see
xgboost.callback.EarlyStopping
.See Custom Objective and Evaluation Metric for more.
Note
This parameter replaces eval_metric in
fit()
method. The old one receives un-transformed prediction regardless of whether custom objective is being used.from sklearn.datasets import load_diabetes from sklearn.metrics import mean_absolute_error X, y = load_diabetes(return_X_y=True) reg = xgb.XGBRegressor( tree_method="hist", eval_metric=mean_absolute_error, ) reg.fit(X, y, eval_set=[(X, y)])
early_stopping_rounds (Optional[int]) –
New in version 1.6.0.
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 in
fit()
.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:
best_score
,best_iteration
andbest_ntree_limit
.Note
This parameter replaces early_stopping_rounds in
fit()
method.callbacks (Optional[List[TrainingCallback]]) –
List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using Callback API. Example:
callbacks = [xgb.callback.EarlyStopping(rounds=early_stopping_rounds, save_best=True)]
kwargs (dict, optional) –
Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found here. 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
For dask implementation, group is not supported, use qid instead.
- apply(X, ntree_limit=None, iteration_range=None)
Return the predicted leaf every tree for each sample. If the model is trained with early stopping, then best_iteration is used automatically.
- Parameters
- 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 best_iteration: int
The best iteration obtained by early stopping. This attribute is 0-based, for instance if the best iteration is the first round, then best_iteration is 0.
- property client: distributed.Client
The dask client used in this model. The Client object can not be serialized for transmission, so if task is launched from a worker instead of directly from the client process, this attribute needs to be set at that worker.
- property coef_: numpy.ndarray
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 callevals_result()
to get evaluation results for all passed eval_sets. When eval_metric is also passed to thefit()
function, the evals_result will contain the eval_metrics passed to thefit()
function.The returned evaluation result is a dictionary:
{'validation_0': {'logloss': ['0.604835', '0.531479']}, 'validation_1': {'logloss': ['0.41965', '0.17686']}}
- Return type
evals_result
- property feature_importances_: numpy.ndarray
Feature importances property, return depends on importance_type parameter.
- Returns
feature_importances_ (array of shape
[n_features]
except for multi-class)linear model, which returns an array with shape (n_features, n_classes)
- property feature_names_in_: numpy.ndarray
Names of features seen during
fit()
. Defined only when X has feature names that are all strings.
- fit(X, y, *, group=None, qid=None, sample_weight=None, base_margin=None, eval_set=None, eval_group=None, eval_qid=None, eval_metric=None, early_stopping_rounds=None, verbose=False, xgb_model=None, sample_weight_eval_set=None, base_margin_eval_set=None, feature_weights=None, callbacks=None)
Fit gradient boosting ranker
Note that calling
fit()
multiple times will cause the model object to be re-fit from scratch. To resume training from a previous checkpoint, explicitly passxgb_model
argument.- Parameters
X (Union[da.Array, dd.DataFrame, dd.Series]) – Feature matrix
y (Union[da.Array, dd.DataFrame, dd.Series]) – Labels
group (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – Size of each query group of training data. Should have as many elements as the query groups in the training data. If this is set to None, then user must provide qid.
qid (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – Query ID for each training sample. Should have the size of n_samples. If this is set to None, then user must provide group.
sample_weight (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) –
Query group weights
Note
Weights are per-group for ranking tasks
In ranking task, one weight is assigned to each query group/id (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.
base_margin (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – Global bias for each instance.
eval_set (Optional[Sequence[Tuple[Union[da.Array, dd.DataFrame, dd.Series], Union[da.Array, dd.DataFrame, dd.Series]]]]) – 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.
eval_group (Optional[Sequence[Union[da.Array, dd.DataFrame, dd.Series]]]) – A list in which
eval_group[i]
is the list containing the sizes of all query groups in thei
-th pair in eval_set.eval_qid (Optional[Sequence[Union[da.Array, dd.DataFrame, dd.Series]]]) – A list in which
eval_qid[i]
is the array containing query ID ofi
-th pair in eval_set.eval_metric (str, list of str, optional) –
Deprecated since version 1.6.0: use eval_metric in
__init__()
orset_params()
instead.early_stopping_rounds (int) –
Deprecated since version 1.6.0: use early_stopping_rounds in
__init__()
orset_params()
instead.verbose (bool) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr.
xgb_model (Optional[Union[xgboost.core.Booster, xgboost.sklearn.XGBModel]]) – file name of stored XGBoost model or ‘Booster’ instance XGBoost model to be loaded before training (allows training continuation).
sample_weight_eval_set (Optional[Sequence[Union[da.Array, dd.DataFrame, dd.Series]]]) –
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.
base_margin_eval_set (Optional[Sequence[Union[da.Array, dd.DataFrame, dd.Series]]]) – A list of the form [M_1, M_2, …, M_n], where each M_i is an array like object storing base margin for the i-th validation set.
feature_weights (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – Weight for each feature, defines the probability of each feature being selected when colsample is being used. All values must be greater than 0, otherwise a ValueError is thrown.
callbacks (Optional[Sequence[xgboost.callback.TrainingCallback]]) –
- Return type
- 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
- property intercept_: numpy.ndarray
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 or bytearray. Path to file can be local or as an URI.
The model is loaded from XGBoost format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be loaded when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.load_model("model.json") # or model.load_model("model.ubj")
- Parameters
fname (Union[str, bytearray, os.PathLike]) – Input file name or memory buffer(see also save_raw)
- Return type
None
- predict(X, output_margin=False, ntree_limit=None, validate_features=True, base_margin=None, iteration_range=None)
Predict with X. If the model is trained with early stopping, then best_iteration is used automatically. For tree models, when data is on GPU, like cupy array or cuDF dataframe and predictor is not specified, the prediction is run on GPU automatically, otherwise it will run on CPU.
Note
This function is only thread safe for gbtree and dart.
- Parameters
X (Union[da.Array, dd.DataFrame, dd.Series]) – Data to predict with.
output_margin (bool) – Whether to output the raw untransformed margin value.
ntree_limit (Optional[int]) – Deprecated, use iteration_range instead.
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.
base_margin (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – Margin added to prediction.
iteration_range (Optional[Tuple[int, int]]) –
Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying
iteration_range=(10, 20)
, then only the forests built during [10, 20) (half open set) rounds are used in this prediction.New in version 1.4.0.
- Return type
prediction
- save_model(fname)
Save the model to a file.
The model is saved in an XGBoost internal format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be saved when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.save_model("model.json") # or model.save_model("model.ubj")
- Parameters
fname (string or os.PathLike) – Output file name
- Return type
None
- class xgboost.dask.DaskXGBRFRegressor(*, learning_rate=1, subsample=0.8, colsample_bynode=0.8, reg_lambda=1e-05, **kwargs)
Bases:
xgboost.dask.DaskXGBRegressor
Implementation of the Scikit-Learn API for XGBoost Random Forest Regressor.
New in version 1.4.0.
- Parameters
n_estimators (int) – Number of trees in random forest to fit.
max_depth (Optional[int]) – Maximum tree depth for base learners.
learning_rate (Optional[float]) – Boosting learning rate (xgb’s “eta”)
verbosity (Optional[int]) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
objective (Union[str, Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]], NoneType]) – Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below).
booster (Optional[str]) – Specify which booster to use: gbtree, gblinear or dart.
tree_method (Optional[str]) – 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 the parameters document tree method
n_jobs (Optional[int]) – Number of parallel threads used to run xgboost. When used with other Scikit-Learn algorithms like grid search, you may choose which algorithm to parallelize and balance the threads. Creating thread contention will significantly slow down both algorithms.
gamma (Optional[float]) – Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight (Optional[float]) – Minimum sum of instance weight(hessian) needed in a child.
max_delta_step (Optional[float]) – Maximum delta step we allow each tree’s weight estimation to be.
subsample (Optional[float]) – Subsample ratio of the training instance.
colsample_bytree (Optional[float]) – Subsample ratio of columns when constructing each tree.
colsample_bylevel (Optional[float]) – Subsample ratio of columns for each level.
colsample_bynode (Optional[float]) – Subsample ratio of columns for each split.
reg_alpha (Optional[float]) – L1 regularization term on weights (xgb’s alpha).
reg_lambda (Optional[float]) – L2 regularization term on weights (xgb’s lambda).
scale_pos_weight (Optional[float]) – Balancing of positive and negative weights.
base_score (Optional[float]) – The initial prediction score of all instances, global bias.
random_state (Optional[Union[numpy.random.RandomState, int]]) –
Random number seed.
Note
Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.
missing (float, default np.nan) – Value in the data which needs to be present as a missing value.
num_parallel_tree (Optional[int]) – Used for boosting random forest.
monotone_constraints (Optional[Union[Dict[str, int], str]]) – Constraint of variable monotonicity. See tutorial for more information.
interaction_constraints (Optional[Union[str, List[Tuple[str]]]]) – Constraints for interaction representing permitted interactions. The constraints must be specified in the form of a nested list, e.g.
[[0, 1], [2, 3, 4]]
, where each inner list is a group of indices of features that are allowed to interact with each other. See tutorial for more informationimportance_type (Optional[str]) –
The feature importance type for the feature_importances_ property:
For tree model, it’s either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.
For linear model, only “weight” is defined and it’s the normalized coefficients without bias.
gpu_id (Optional[int]) – Device ordinal.
validate_parameters (Optional[bool]) – Give warnings for unknown parameter.
predictor (Optional[str]) – Force XGBoost to use specific predictor, available choices are [cpu_predictor, gpu_predictor].
enable_categorical (bool) –
New in version 1.5.0.
Experimental support for categorical data. Do not set to true unless you are interested in development. Only valid when gpu_hist and dataframe are used.
max_cat_to_onehot (Optional[int]) –
New in version 1.6.0.
Note
This parameter is experimental
A threshold for deciding whether XGBoost should use one-hot encoding based split for categorical data. When number of categories is lesser than the threshold then one-hot encoding is chosen, otherwise the categories will be partitioned into children nodes. Only relevant for regression and binary classification and approx tree method.
eval_metric (Optional[Union[str, List[str], Callable]]) –
New in version 1.6.0.
Metric used for monitoring the training result and early stopping. It can be a string or list of strings as names of predefined metric in XGBoost (See doc/parameter.rst), one of the metrics in
sklearn.metrics
, or any other user defined metric that looks like sklearn.metrics.If custom objective is also provided, then custom metric should implement the corresponding reverse link function.
Unlike the scoring parameter commonly used in scikit-learn, when a callable object is provided, it’s assumed to be a cost function and by default XGBoost will minimize the result during early stopping.
For advanced usage on Early stopping like directly choosing to maximize instead of minimize, see
xgboost.callback.EarlyStopping
.See Custom Objective and Evaluation Metric for more.
Note
This parameter replaces eval_metric in
fit()
method. The old one receives un-transformed prediction regardless of whether custom objective is being used.from sklearn.datasets import load_diabetes from sklearn.metrics import mean_absolute_error X, y = load_diabetes(return_X_y=True) reg = xgb.XGBRegressor( tree_method="hist", eval_metric=mean_absolute_error, ) reg.fit(X, y, eval_set=[(X, y)])
early_stopping_rounds (Optional[int]) –
New in version 1.6.0.
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 in
fit()
.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:
best_score
,best_iteration
andbest_ntree_limit
.Note
This parameter replaces early_stopping_rounds in
fit()
method.callbacks (Optional[List[TrainingCallback]]) –
List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using Callback API. Example:
callbacks = [xgb.callback.EarlyStopping(rounds=early_stopping_rounds, save_best=True)]
kwargs (dict, optional) –
Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found here. 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
Custom objective function
A custom objective function can be provided for the
objective
parameter. In this case, it should have the signatureobjective(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
- Return type
None
- apply(X, ntree_limit=None, iteration_range=None)
Return the predicted leaf every tree for each sample. If the model is trained with early stopping, then best_iteration is used automatically.
- Parameters
- 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 best_iteration: int
The best iteration obtained by early stopping. This attribute is 0-based, for instance if the best iteration is the first round, then best_iteration is 0.
- property client: distributed.Client
The dask client used in this model. The Client object can not be serialized for transmission, so if task is launched from a worker instead of directly from the client process, this attribute needs to be set at that worker.
- property coef_: numpy.ndarray
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 callevals_result()
to get evaluation results for all passed eval_sets. When eval_metric is also passed to thefit()
function, the evals_result will contain the eval_metrics passed to thefit()
function.The returned evaluation result is a dictionary:
{'validation_0': {'logloss': ['0.604835', '0.531479']}, 'validation_1': {'logloss': ['0.41965', '0.17686']}}
- Return type
evals_result
- property feature_importances_: numpy.ndarray
Feature importances property, return depends on importance_type parameter.
- Returns
feature_importances_ (array of shape
[n_features]
except for multi-class)linear model, which returns an array with shape (n_features, n_classes)
- property feature_names_in_: numpy.ndarray
Names of features seen during
fit()
. Defined only when X has feature names that are all strings.
- fit(X, y, *, sample_weight=None, base_margin=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, base_margin_eval_set=None, feature_weights=None, callbacks=None)
Fit gradient boosting model.
Note that calling
fit()
multiple times will cause the model object to be re-fit from scratch. To resume training from a previous checkpoint, explicitly passxgb_model
argument.- Parameters
X (Union[da.Array, dd.DataFrame, dd.Series]) – Feature matrix
y (Union[da.Array, dd.DataFrame, dd.Series]) – Labels
sample_weight (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – instance weights
base_margin (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – global bias for each instance.
eval_set (Optional[Sequence[Tuple[Union[da.Array, dd.DataFrame, dd.Series], Union[da.Array, dd.DataFrame, dd.Series]]]]) – 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.
eval_metric (str, list of str, or callable, optional) –
Deprecated since version 1.6.0: Use eval_metric in
__init__()
orset_params()
instead.early_stopping_rounds (int) –
Deprecated since version 1.6.0: Use early_stopping_rounds in
__init__()
orset_params()
instead.verbose (bool) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr.
xgb_model (Optional[Union[xgboost.core.Booster, xgboost.sklearn.XGBModel]]) – file name of stored XGBoost model or ‘Booster’ instance XGBoost model to be loaded before training (allows training continuation).
sample_weight_eval_set (Optional[Sequence[Union[da.Array, dd.DataFrame, dd.Series]]]) – A list of the form [L_1, L_2, …, L_n], where each L_i is an array like object storing instance weights for the i-th validation set.
base_margin_eval_set (Optional[Sequence[Union[da.Array, dd.DataFrame, dd.Series]]]) – A list of the form [M_1, M_2, …, M_n], where each M_i is an array like object storing base margin for the i-th validation set.
feature_weights (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – Weight for each feature, defines the probability of each feature being selected when colsample is being used. All values must be greater than 0, otherwise a ValueError is thrown.
callbacks (Optional[Sequence[xgboost.callback.TrainingCallback]]) –
- Return type
- 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
- property intercept_: numpy.ndarray
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 or bytearray. Path to file can be local or as an URI.
The model is loaded from XGBoost format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be loaded when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.load_model("model.json") # or model.load_model("model.ubj")
- Parameters
fname (Union[str, bytearray, os.PathLike]) – Input file name or memory buffer(see also save_raw)
- Return type
None
- predict(X, output_margin=False, ntree_limit=None, validate_features=True, base_margin=None, iteration_range=None)
Predict with X. If the model is trained with early stopping, then best_iteration is used automatically. For tree models, when data is on GPU, like cupy array or cuDF dataframe and predictor is not specified, the prediction is run on GPU automatically, otherwise it will run on CPU.
Note
This function is only thread safe for gbtree and dart.
- Parameters
X (Union[da.Array, dd.DataFrame, dd.Series]) – Data to predict with.
output_margin (bool) – Whether to output the raw untransformed margin value.
ntree_limit (Optional[int]) – Deprecated, use iteration_range instead.
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.
base_margin (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – Margin added to prediction.
iteration_range (Optional[Tuple[int, int]]) –
Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying
iteration_range=(10, 20)
, then only the forests built during [10, 20) (half open set) rounds are used in this prediction.New in version 1.4.0.
- Return type
prediction
- save_model(fname)
Save the model to a file.
The model is saved in an XGBoost internal format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be saved when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.save_model("model.json") # or model.save_model("model.ubj")
- Parameters
fname (string or os.PathLike) – Output file name
- Return type
None
- class xgboost.dask.DaskXGBRFClassifier(*, learning_rate=1, subsample=0.8, colsample_bynode=0.8, reg_lambda=1e-05, **kwargs)
Bases:
xgboost.dask.DaskXGBClassifier
Implementation of the Scikit-Learn API for XGBoost Random Forest Classifier.
New in version 1.4.0.
- Parameters
n_estimators (int) – Number of trees in random forest to fit.
max_depth (Optional[int]) – Maximum tree depth for base learners.
learning_rate (Optional[float]) – Boosting learning rate (xgb’s “eta”)
verbosity (Optional[int]) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
objective (Union[str, Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]], NoneType]) – Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below).
booster (Optional[str]) – Specify which booster to use: gbtree, gblinear or dart.
tree_method (Optional[str]) – 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 the parameters document tree method
n_jobs (Optional[int]) – Number of parallel threads used to run xgboost. When used with other Scikit-Learn algorithms like grid search, you may choose which algorithm to parallelize and balance the threads. Creating thread contention will significantly slow down both algorithms.
gamma (Optional[float]) – Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight (Optional[float]) – Minimum sum of instance weight(hessian) needed in a child.
max_delta_step (Optional[float]) – Maximum delta step we allow each tree’s weight estimation to be.
subsample (Optional[float]) – Subsample ratio of the training instance.
colsample_bytree (Optional[float]) – Subsample ratio of columns when constructing each tree.
colsample_bylevel (Optional[float]) – Subsample ratio of columns for each level.
colsample_bynode (Optional[float]) – Subsample ratio of columns for each split.
reg_alpha (Optional[float]) – L1 regularization term on weights (xgb’s alpha).
reg_lambda (Optional[float]) – L2 regularization term on weights (xgb’s lambda).
scale_pos_weight (Optional[float]) – Balancing of positive and negative weights.
base_score (Optional[float]) – The initial prediction score of all instances, global bias.
random_state (Optional[Union[numpy.random.RandomState, int]]) –
Random number seed.
Note
Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.
missing (float, default np.nan) – Value in the data which needs to be present as a missing value.
num_parallel_tree (Optional[int]) – Used for boosting random forest.
monotone_constraints (Optional[Union[Dict[str, int], str]]) – Constraint of variable monotonicity. See tutorial for more information.
interaction_constraints (Optional[Union[str, List[Tuple[str]]]]) – Constraints for interaction representing permitted interactions. The constraints must be specified in the form of a nested list, e.g.
[[0, 1], [2, 3, 4]]
, where each inner list is a group of indices of features that are allowed to interact with each other. See tutorial for more informationimportance_type (Optional[str]) –
The feature importance type for the feature_importances_ property:
For tree model, it’s either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.
For linear model, only “weight” is defined and it’s the normalized coefficients without bias.
gpu_id (Optional[int]) – Device ordinal.
validate_parameters (Optional[bool]) – Give warnings for unknown parameter.
predictor (Optional[str]) – Force XGBoost to use specific predictor, available choices are [cpu_predictor, gpu_predictor].
enable_categorical (bool) –
New in version 1.5.0.
Experimental support for categorical data. Do not set to true unless you are interested in development. Only valid when gpu_hist and dataframe are used.
max_cat_to_onehot (Optional[int]) –
New in version 1.6.0.
Note
This parameter is experimental
A threshold for deciding whether XGBoost should use one-hot encoding based split for categorical data. When number of categories is lesser than the threshold then one-hot encoding is chosen, otherwise the categories will be partitioned into children nodes. Only relevant for regression and binary classification and approx tree method.
eval_metric (Optional[Union[str, List[str], Callable]]) –
New in version 1.6.0.
Metric used for monitoring the training result and early stopping. It can be a string or list of strings as names of predefined metric in XGBoost (See doc/parameter.rst), one of the metrics in
sklearn.metrics
, or any other user defined metric that looks like sklearn.metrics.If custom objective is also provided, then custom metric should implement the corresponding reverse link function.
Unlike the scoring parameter commonly used in scikit-learn, when a callable object is provided, it’s assumed to be a cost function and by default XGBoost will minimize the result during early stopping.
For advanced usage on Early stopping like directly choosing to maximize instead of minimize, see
xgboost.callback.EarlyStopping
.See Custom Objective and Evaluation Metric for more.
Note
This parameter replaces eval_metric in
fit()
method. The old one receives un-transformed prediction regardless of whether custom objective is being used.from sklearn.datasets import load_diabetes from sklearn.metrics import mean_absolute_error X, y = load_diabetes(return_X_y=True) reg = xgb.XGBRegressor( tree_method="hist", eval_metric=mean_absolute_error, ) reg.fit(X, y, eval_set=[(X, y)])
early_stopping_rounds (Optional[int]) –
New in version 1.6.0.
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 in
fit()
.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:
best_score
,best_iteration
andbest_ntree_limit
.Note
This parameter replaces early_stopping_rounds in
fit()
method.callbacks (Optional[List[TrainingCallback]]) –
List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using Callback API. Example:
callbacks = [xgb.callback.EarlyStopping(rounds=early_stopping_rounds, save_best=True)]
kwargs (dict, optional) –
Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found here. 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
Custom objective function
A custom objective function can be provided for the
objective
parameter. In this case, it should have the signatureobjective(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
- Return type
None
- apply(X, ntree_limit=None, iteration_range=None)
Return the predicted leaf every tree for each sample. If the model is trained with early stopping, then best_iteration is used automatically.
- Parameters
- 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 best_iteration: int
The best iteration obtained by early stopping. This attribute is 0-based, for instance if the best iteration is the first round, then best_iteration is 0.
- property client: distributed.Client
The dask client used in this model. The Client object can not be serialized for transmission, so if task is launched from a worker instead of directly from the client process, this attribute needs to be set at that worker.
- property coef_: numpy.ndarray
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 callevals_result()
to get evaluation results for all passed eval_sets. When eval_metric is also passed to thefit()
function, the evals_result will contain the eval_metrics passed to thefit()
function.The returned evaluation result is a dictionary:
{'validation_0': {'logloss': ['0.604835', '0.531479']}, 'validation_1': {'logloss': ['0.41965', '0.17686']}}
- Return type
evals_result
- property feature_importances_: numpy.ndarray
Feature importances property, return depends on importance_type parameter.
- Returns
feature_importances_ (array of shape
[n_features]
except for multi-class)linear model, which returns an array with shape (n_features, n_classes)
- property feature_names_in_: numpy.ndarray
Names of features seen during
fit()
. Defined only when X has feature names that are all strings.
- fit(X, y, *, sample_weight=None, base_margin=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, base_margin_eval_set=None, feature_weights=None, callbacks=None)
Fit gradient boosting model.
Note that calling
fit()
multiple times will cause the model object to be re-fit from scratch. To resume training from a previous checkpoint, explicitly passxgb_model
argument.- Parameters
X (Union[da.Array, dd.DataFrame, dd.Series]) – Feature matrix
y (Union[da.Array, dd.DataFrame, dd.Series]) – Labels
sample_weight (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – instance weights
base_margin (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – global bias for each instance.
eval_set (Optional[Sequence[Tuple[Union[da.Array, dd.DataFrame, dd.Series], Union[da.Array, dd.DataFrame, dd.Series]]]]) – 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.
eval_metric (str, list of str, or callable, optional) –
Deprecated since version 1.6.0: Use eval_metric in
__init__()
orset_params()
instead.early_stopping_rounds (int) –
Deprecated since version 1.6.0: Use early_stopping_rounds in
__init__()
orset_params()
instead.verbose (bool) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr.
xgb_model (Optional[Union[xgboost.core.Booster, xgboost.sklearn.XGBModel]]) – file name of stored XGBoost model or ‘Booster’ instance XGBoost model to be loaded before training (allows training continuation).
sample_weight_eval_set (Optional[Sequence[Union[da.Array, dd.DataFrame, dd.Series]]]) – A list of the form [L_1, L_2, …, L_n], where each L_i is an array like object storing instance weights for the i-th validation set.
base_margin_eval_set (Optional[Sequence[Union[da.Array, dd.DataFrame, dd.Series]]]) – A list of the form [M_1, M_2, …, M_n], where each M_i is an array like object storing base margin for the i-th validation set.
feature_weights (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – Weight for each feature, defines the probability of each feature being selected when colsample is being used. All values must be greater than 0, otherwise a ValueError is thrown.
callbacks (Optional[Sequence[xgboost.callback.TrainingCallback]]) –
- Return type
- 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
- property intercept_: numpy.ndarray
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 or bytearray. Path to file can be local or as an URI.
The model is loaded from XGBoost format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be loaded when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.load_model("model.json") # or model.load_model("model.ubj")
- Parameters
fname (Union[str, bytearray, os.PathLike]) – Input file name or memory buffer(see also save_raw)
- Return type
None
- predict(X, output_margin=False, ntree_limit=None, validate_features=True, base_margin=None, iteration_range=None)
Predict with X. If the model is trained with early stopping, then best_iteration is used automatically. For tree models, when data is on GPU, like cupy array or cuDF dataframe and predictor is not specified, the prediction is run on GPU automatically, otherwise it will run on CPU.
Note
This function is only thread safe for gbtree and dart.
- Parameters
X (Union[da.Array, dd.DataFrame, dd.Series]) – Data to predict with.
output_margin (bool) – Whether to output the raw untransformed margin value.
ntree_limit (Optional[int]) – Deprecated, use iteration_range instead.
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.
base_margin (Optional[Union[da.Array, dd.DataFrame, dd.Series]]) – Margin added to prediction.
iteration_range (Optional[Tuple[int, int]]) –
Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying
iteration_range=(10, 20)
, then only the forests built during [10, 20) (half open set) rounds are used in this prediction.New in version 1.4.0.
- Return type
prediction
- predict_proba(X, ntree_limit=None, validate_features=True, base_margin=None, iteration_range=None)
Predict the probability of each X example being of a given class.
Note
This function is only thread safe for gbtree and dart.
- Parameters
X (array_like) – Feature matrix.
ntree_limit (int) – Deprecated, use iteration_range instead.
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.
base_margin (array_like) – Margin added to prediction.
iteration_range (Optional[Tuple[int, int]]) – Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying iteration_range=(10, 20), then only the forests built during [10, 20) (half open set) rounds are used in this prediction.
- Returns
a numpy array of shape array-like of shape (n_samples, n_classes) with the probability of each data example being of a given class.
- Return type
prediction
- save_model(fname)
Save the model to a file.
The model is saved in an XGBoost internal format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be saved when using binary format. To save those attributes, use JSON/UBJ instead. See Model IO for more info.
model.save_model("model.json") # or model.save_model("model.ubj")
- Parameters
fname (string or os.PathLike) – Output file name
- Return type
None