我创建了jupyter笔记本的要点，以演示可以逐步训练xgboost模型。我使用波士顿数据集来训练模型。我做了3个实验-一枪学习，迭代一枪学习，迭代增量学习。在增量训练中，我将波士顿数据分批传递给模型，大小为50。

要点是，您必须多次遍历数据才能使模型收敛到一次射击（所有数据）学习所获得的精度。

这是用于使用xgboost进行迭代增量学习的相应代码。

batch_size = 50
iterations = 25
model = None
for i in range(iterations):
    for start in range(0, len(x_tr), batch_size):
        model = xgb.train({
            'learning_rate': 0.007,
            'update':'refresh',
            'process_type': 'update',
            'refresh_leaf': True,
            #'reg_lambda': 3,  # L2
            'reg_alpha': 3,  # L1
            'silent': False,
        }, dtrain=xgb.DMatrix(x_tr[start:start+batch_size], y_tr[start:start+batch_size]), xgb_model=model)

        y_pr = model.predict(xgb.DMatrix(x_te))
        #print('    MSE itr@{}: {}'.format(int(start/batch_size), sklearn.metrics.mean_squared_error(y_te, y_pr)))
    print('MSE itr@{}: {}'.format(i, sklearn.metrics.mean_squared_error(y_te, y_pr)))

y_pr = model.predict(xgb.DMatrix(x_te))
print('MSE at the end: {}'.format(sklearn.metrics.mean_squared_error(y_te, y_pr)))

XGBoost版本：0.6

看起来除了再次调用xgb.train(....)之外，您不需要其他任何东西，但可以提供前一批的模型结果：

# python
params = {} # your params here
ith_batch = 0
n_batches = 100
model = None
while ith_batch < n_batches:
    d_train = getBatchData(ith_batch)
    model = xgb.train(params, d_train, xgb_model=model)
    ith_batch += 1

这是基于https://xgboost.readthedocs.io/en/latest/python/python_api.html

如果您的问题与数据集大小有关，而您实际上并不需要增量学习（例如，您没有在使用Streaming应用程序），则应查看Spark或Flink。

这两个框架可以利用磁盘内存在具有较小RAM的超大型数据集上进行训练。这两个框架都在内部处理内存问题。尽管Flink首先解决了问题，但Spark赶上了最新版本。

看一眼：

• “ XGBoost4J：Spark，Flink和数据流中的便携式分布式XGBoost”： http ://dmlc.ml/2016/03/14/xgboost4j-portable-distributed-xgboost-in-spark-flink-and-dataflow.html
• Spark集成： http ： //dmlc.ml/2016/10/26/a-full-integration-of-xgboost-and-spark.html

现在有一个（版本0.6？）可能有用的process_update参数。这是一个实验：

import pandas as pd
import xgboost as xgb
from sklearn.model_selection import ShuffleSplit
from sklearn.datasets import load_boston
from sklearn.metrics import mean_squared_error as mse

boston = load_boston()
features = boston.feature_names
X = boston.data
y = boston.target

X=pd.DataFrame(X,columns=features)
y = pd.Series(y,index=X.index)

# split data into training and testing sets
rs = ShuffleSplit(test_size=0.3, n_splits=1, random_state=0)
for train_idx,test_idx in rs.split(X):  # this looks silly
    pass

train_split = round(len(train_idx) / 2)
train1_idx = train_idx[:train_split]
train2_idx = train_idx[train_split:]
X_train = X.loc[train_idx]
X_train_1 = X.loc[train1_idx]
X_train_2 = X.loc[train2_idx]
X_test = X.loc[test_idx]
y_train = y.loc[train_idx]
y_train_1 = y.loc[train1_idx]
y_train_2 = y.loc[train2_idx]
y_test = y.loc[test_idx]

xg_train_0 = xgb.DMatrix(X_train, label=y_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_0 = xgb.train(params, xg_train_0, 30)
model_1 = xgb.train(params, xg_train_1, 30)
model_1.save_model('model_1.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)

params.update({'process_type': 'update',
               'updater'     : 'refresh',
               'refresh_leaf': True})
model_2_v2_update = xgb.train(params, xg_train_2, 30, xgb_model=model_1)

print('full train\t',mse(model_0.predict(xg_test), y_test)) # benchmark
print('model 1 \t',mse(model_1.predict(xg_test), y_test))  
print('model 2 \t',mse(model_2_v1.predict(xg_test), y_test))  # "before"
print('model 1+2\t',mse(model_2_v2.predict(xg_test), y_test))  # "after"
print('model 1+update2\t',mse(model_2_v2_update.predict(xg_test), y_test))  # "after"

输出：

full train   17.8364309709
model 1      24.2542132108
model 2      25.6967017352
model 1+2    22.8846455135
model 1+update2  14.2816257268

免责声明：我也是xgboost的新手，但我想我已经明白了。

训练第一批后，尝试保存模型。然后，在连续运行中，向xgb.train方法提供已保存模型的文件路径。

这是我进行的一个小实验，目的是让自己确信它是可行的：

首先，将波士顿数据集分为训练和测试集。然后将训练集分成两半。将模型与前半部分拟合，并得到一个分数作为基准。然后在后半部分安装两个模型；一个模型将具有附加参数xgb_model 。如果传入多余的参数没有影响，那么我们希望他们的得分是相似的。但是，幸运的是，新模型的性能似乎比第一个模型好得多。

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, 
                                                     y_train, 
                                                     test_size=0.5,
                                                     random_state=0)

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)
model_1.save_model('model_1.model')

# ================= 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

让我知道是否有任何不清楚的地方！

参考： https : //github.com/dmlc/xgboost/blob/master/python-package/xgboost/training.py