Marat Zakirov Marat Zakirov - 1 year ago 897
Python Question

How can I implement incremental training for xgboost?

The problem is that my train data could not be placed into RAM due to train data size. So I need a method which first builds one tree on whole train data set, calculate residuals build another tree and so on (like gradient boosted tree do). Obviously if I call

model = xgb.train(param, batch_dtrain, 2)
in some loop - it will not help, because in such case it just rebuilds whole model for each batch.

Answer Source

Disclaimer: I'm new to xgboost as well, but I think I figured this out.

Try saving your model after you train on the first batch. Then, on successive runs, provide the xgb.train method with the filepath of the saved model.

Here's a small experiment that I ran to convince myself that it works:

First, split the boston dataset into training and testing sets. Then split the training set into halves. Fit a model with the first half and get a score that will serve as a benchmark. Then fit two models with the second half; one model will have the additional parameter xgb_model. If passing in the extra parameter didn't make a difference, then we would expect their scores to be similar.. But, fortunately, the new model seems to perform much better than the first.

import xgboost as xgb
from sklearn.cross_validation import train_test_split as ttsplit
from sklearn.datasets import load_boston
from sklearn.metrics import mean_squared_error as mse

X = load_boston()['data']
y = load_boston()['target']

# split data into training and testing sets
# then split training set in half
X_train, X_test, y_train, y_test = ttsplit(X, y, test_size=0.1, random_state=0)
X_train_1, X_train_2, y_train_1, y_train_2 = ttsplit(X_train, 

xg_train_1 = xgb.DMatrix(X_train_1, label=y_train_1)
xg_train_2 = xgb.DMatrix(X_train_2, label=y_train_2)
xg_test = xgb.DMatrix(X_test, label=y_test)

params = {'objective': 'reg:linear', 'verbose': False}
model_1 = xgb.train(params, xg_train_1, 30)

# ================= train two versions of the model =====================#
model_2_v1 = xgb.train(params, xg_train_2, 30)
model_2_v2 = xgb.train(params, xg_train_2, 30, xgb_model='model_1.model')

print(mse(model_1.predict(xg_test), y_test))     # benchmark
print(mse(model_2_v1.predict(xg_test), y_test))  # "before"
print(mse(model_2_v2.predict(xg_test), y_test))  # "after"

# 23.0475232194
# 39.6776876084
# 27.2053239482

Let me know if anything is unclear!


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