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Python Package Introduction

This document gives a basic walkthrough of xgboost python package.

List of other Helpful Links

Install XGBoost

To install XGBoost, do the following steps:

  • You need to run make in the root directory of the project
  • In the python-package directory run
python setup.py install
import xgboost as xgb

Data Interface

The XGBoost python module is able to load data from:

  • libsvm txt format file
  • Numpy 2D array, and
  • xgboost binary buffer file.

The data will be store in a DMatrix object.

  • To load a libsvm text file or a XGBoost binary file into DMatrix, the command is:
dtrain = xgb.DMatrix('train.svm.txt')
dtest = xgb.DMatrix('test.svm.buffer')
  • To load a numpy array into DMatrix, the command is:
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 scpiy.sparse array into DMatrix, the command is:
csr = scipy.sparse.csr_matrix((dat, (row, col)))
dtrain = xgb.DMatrix(csr)
  • Saving DMatrix into XGBoost binary file will make loading faster in next time:
dtrain = xgb.DMatrix('train.svm.txt')
dtrain.save_binary("train.buffer")
  • To handle missing value in DMatrix, you can initialize the DMatrix by specifying missing values:
dtrain = xgb.DMatrix(data, label=label, missing = -999.0)
  • Weight can be set when needed:
w = np.random.rand(5, 1)
dtrain = xgb.DMatrix(data, label=label, missing = -999.0, weight=w)

Setting Parameters

XGBoost use list of pair to save parameters. Eg

  • Booster parameters
param = {'bst:max_depth':2, 'bst:eta':1, 'silent':1, 'objective':'binary:logistic' }
param['nthread'] = 4
plst = param.items()
plst += [('eval_metric', 'auc')] # Multiple evals can be handled in this way
plst += [('eval_metric', 'ams@0')]
  • Specify validations set to watch performance
evallist  = [(dtest,'eval'), (dtrain,'train')]

Training

With parameter list and data, you are able to train a model.

  • Training
num_round = 10
bst = xgb.train( plst, dtrain, num_round, evallist )
  • Saving model After training, you can save model and dump it out.
bst.save_model('0001.model')
  • Dump Model and Feature Map You can dump the model to txt and review the meaning of model
# dump model
bst.dump_model('dump.raw.txt')
# dump model with feature map
bst.dump_model('dump.raw.txt','featmap.txt')
  • Loading model After you save your model, you can load model file at anytime by using
bst = xgb.Booster({'nthread':4}) #init model
bst.load_model("model.bin") # load data

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 and bst.best_iteration. Note that 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).

Prediction

After you training/loading a model and preparing the data, you can start to do prediction.

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

If early stopping is enabled during training, you can predict with the best iteration.

ypred = bst.predict(xgmat,ntree_limit=bst.best_iteration)

Plotting

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

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

xgb.plot_importance(bst)

To output tree via matplotlib, use plot_tree specifying 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 to_graphviz function which converts the target tree to graphviz instance. graphviz instance is automatically rendered on IPython.

xgb.to_graphviz(bst, num_trees=2)