Python Package Introduction

This document gives a basic walkthrough of the xgboost package for Python. The Python package is consisted of 3 different interfaces, including native interface, scikit-learn interface and dask interface. For introduction to dask interface please see Distributed XGBoost with Dask.

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Contents

Install XGBoost

To install XGBoost, follow instructions in Installation Guide.

To verify your installation, run the following in Python:

import xgboost as xgb

Data Interface

The XGBoost Python module is able to load data from many different types of data format including both CPU and GPU data structures. For a complete list of supported data types, please reference the Supported data structures for various XGBoost functions. For a detailed description of text input formats, please visit Text Input Format of DMatrix.

The input data is stored in a DMatrix object. For the sklearn estimator interface, a DMatrix or a QuantileDMatrix is created depending on the chosen algorithm and the input, see the sklearn API reference for details. We will illustrate some of the basic input types with the DMatrix here.

To load a NumPy array into DMatrix:

data = np.random.rand(5, 10)  # 5 entities, each contains 10 features
label = np.random.randint(2, size=5)  # binary target
dtrain = xgb.DMatrix(data, label=label)

To load a scipy.sparse array into DMatrix:

csr = scipy.sparse.csr_matrix((dat, (row, col)))
dtrain = xgb.DMatrix(csr)

To load a Pandas data frame into DMatrix:

data = pandas.DataFrame(np.arange(12).reshape((4,3)), columns=['a', 'b', 'c'])
label = pandas.DataFrame(np.random.randint(2, size=4))
dtrain = xgb.DMatrix(data, label=label)

Saving DMatrix into a XGBoost binary file will make loading faster:

dtrain = xgb.DMatrix('train.svm.txt?format=libsvm')
dtrain.save_binary('train.buffer')

Missing values can be replaced by a default value in the DMatrix constructor:
```
dtrain = xgb.DMatrix(data, label=label, missing=np.NaN)
```

Weights can be set when needed:

w = np.random.rand(5, 1)
dtrain = xgb.DMatrix(data, label=label, missing=np.NaN, weight=w)

When performing ranking tasks, the number of weights should be equal to number of groups.

To load a LIBSVM text file or a XGBoost binary file into DMatrix:
```
dtrain = xgb.DMatrix('train.svm.txt?format=libsvm')
dtest = xgb.DMatrix('test.svm.buffer')
```
The parser in XGBoost has limited functionality. When using Python interface, it’s recommended to use sklearn load_svmlight_file or other similar utilites than XGBoost’s builtin parser.
To load a CSV file into DMatrix:
```
# label_column specifies the index of the column containing the true label
dtrain = xgb.DMatrix('train.csv?format=csv&label_column=0')
dtest = xgb.DMatrix('test.csv?format=csv&label_column=0')
```
The parser in XGBoost has limited functionality. When using Python interface, it’s recommended to use pandas read_csv or other similar utilites than XGBoost’s builtin parser.

Supported data structures for various XGBoost functions

Markers

T: Supported.
F: Not supported.
NE: Invalid type for the use case. For instance, pd.Series can not be multi-target label.
NPA: Support with the help of numpy array.
AT: Support with the help of arrow table.
CPA: Support with the help of cupy array.
SciCSR: Support with the help of scripy sparse CSR. The conversion to scipy CSR may or may not be possible. Raise a type error if conversion fails.
FF: We can look forward to having its support in recent future if requested.
empty: To be filled in.

Table Header

X means predictor matrix.
Meta info: label, weight, etc.
Multi Label: 2-dim label for multi-target.
Others: Anything else that we don’t list here explicitly including formats like lil, dia, bsr. XGBoost will try to convert it into scipy csr.

Support Matrix

Name	DMatrix X	QuantileDMatrix X	Sklearn X	Meta Info	Inplace prediction	Multi Label
numpy.ndarray	T	T	T	T	T	T
scipy.sparse.csr	T	T	T	NE	T	F
scipy.sparse.csc	T	F	T	NE	F	F
scipy.sparse.coo	SciCSR	F	SciCSR	NE	F	F
uri	T	F	F	F	NE	F
list	NPA	NPA	NPA	NPA	NPA	T
tuple	NPA	NPA	NPA	NPA	NPA	T
pandas.DataFrame	NPA	NPA	NPA	NPA	NPA	NPA
pandas.Series	NPA	NPA	NPA	NPA	NPA	NE
cudf.DataFrame	T	T	T	T	T	T
cudf.Series	T	T	T	T	FF	NE
cupy.ndarray	T	T	T	T	T	T
torch.Tensor	T	T	T	T	T	T
dlpack	CPA	CPA		CPA	FF	FF
modin.DataFrame	NPA	FF	NPA	NPA	FF
modin.Series	NPA	FF	NPA	NPA	FF
pyarrow.Table	T	T	T	T	T	T
polars.DataFrame	AT	AT	AT	AT	AT	AT
polars.LazyFrame (WARN)	AT	AT	AT	AT	AT	AT
polars.Series	AT	AT	AT	AT	AT	NE
__array__	NPA	F	NPA	NPA	H
Others	SciCSR	F		F	F

The polars LazyFrame.collect supports many configurations, ranging from the choice of query engine to type coercion. XGBoost simply uses the default parameter. Please run collect to obtain the DataFrame before passing it into XGBoost for finer control over the behaviour.

Setting Parameters

XGBoost can use either a list of pairs or a dictionary to set parameters. For instance:

Booster parameters

param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
param['nthread'] = 4
param['eval_metric'] = 'auc'

You can also specify multiple eval metrics:

param['eval_metric'] = ['auc', 'ams@0']

# alternatively:
# plst = param.items()
# plst += [('eval_metric', 'ams@0')]

Specify validations set to watch performance

evallist = [(dtrain, 'train'), (dtest, 'eval')]

Training

Training a model requires a parameter list and data set.

num_round = 10
bst = xgb.train(param, dtrain, num_round, evallist)

After training, the model can be saved.

bst.save_model('0001.model')

The model and its feature map can also be dumped to a text file.

# dump model
bst.dump_model('dump.raw.txt')
# dump model with feature map
bst.dump_model('dump.raw.txt', 'featmap.txt')

A saved model can be loaded as follows:

bst = xgb.Booster({'nthread': 4})  # init model
bst.load_model('model.bin')  # load model data

Methods including update and boost from xgboost.Booster are designed for internal usage only. The wrapper function xgboost.train does some pre-configuration including setting up caches and some other parameters.

Early Stopping

If you have a validation set, you can use early stopping to find the optimal number of boosting rounds. Early stopping requires at least one set in evals. If there’s more than one, it will use the last.

train(..., evals=evals, early_stopping_rounds=10)

The model will train until the validation score stops improving. Validation error needs to decrease at least every early_stopping_rounds to continue training.

If early stopping occurs, the model will have two additional fields: bst.best_score, bst.best_iteration. Note that xgboost.train() will return a model from the last iteration, not the best one.

This works with both metrics to minimize (RMSE, log loss, etc.) and to maximize (MAP, NDCG, AUC). Note that if you specify more than one evaluation metric the last one in param['eval_metric'] is used for early stopping.

Prediction

A model that has been trained or loaded can perform predictions on data sets.

# 7 entities, each contains 10 features
data = np.random.rand(7, 10)
dtest = xgb.DMatrix(data)
ypred = bst.predict(dtest)

If early stopping is enabled during training, you can get predictions from the best iteration with bst.best_iteration:

ypred = bst.predict(dtest, iteration_range=(0, bst.best_iteration + 1))

Plotting

You can use plotting module to plot importance and output tree.

To plot importance, use xgboost.plot_importance(). This function requires matplotlib to be installed.

xgb.plot_importance(bst)

To plot the output tree via matplotlib, use xgboost.plot_tree(), specifying the ordinal number of the target tree. This function requires graphviz and matplotlib.

xgb.plot_tree(bst, num_trees=2)

When you use IPython, you can use the xgboost.to_graphviz() function, which converts the target tree to a graphviz instance. The graphviz instance is automatically rendered in IPython.

xgb.to_graphviz(bst, num_trees=2)

Scikit-Learn interface

XGBoost provides an easy to use scikit-learn interface for some pre-defined models including regression, classification and ranking. See Using the Scikit-Learn Estimator Interface for more info.

# Use "hist" for training the model.
reg = xgb.XGBRegressor(tree_method="hist", device="cuda")
# Fit the model using predictor X and response y.
reg.fit(X, y)
# Save model into JSON format.
reg.save_model("regressor.json")

User can still access the underlying booster model when needed:

booster: xgb.Booster = reg.get_booster()