如何为xgboost实施增量培训？

Question

问题是，由于列车数据的大小，我的列车数据无法放入RAM。所以我需要一种方法，首先在整个列车数据集上建立一棵树，计算残差建立另一棵树，等等（像梯度增强树那样）。显然，如果我在某个循环中调用model = xgb.train(param, batch_dtrain, 2)，这将没有帮助，因为在这种情况下，它只是为每个批重新构建整个模型。

Answer 1

免责声明：我也是xgboost的新手，但我想我已经弄明白了。

试着在第一批训练后保存你的模型。然后，在连续运行时，为xgb.train方法提供保存的模型的文件路径。

下面是一个小实验，我试着让自己相信它是有效的：

首先，将波士顿数据集分为训练集和测试集。 然后把训练集分成两半。 将模型与上半场匹配，得到一个分数作为基准。 然后用下半部分拟合两个模型；其中一个模型将具有附加参数xgbu model。如果传入额外的参数没有什么区别，那么我们会期望它们的分数是相似的。。 不过，幸运的是，新机型的性能似乎比第一款要好得多。

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

如果有什么不清楚的地方请告诉我！

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

Answer 2

现在有了（0.6版？）可能有帮助的进程更新参数。下面是一个实验：

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

Answer 3

我创建了a gist of jupyter notebook来演示xgboost模型可以增量训练。我用波士顿数据集训练模型。我做了三个实验-一次学习，迭代一次学习，迭代增量学习。在增量训练中，我将波士顿的数据以50号为一批批传递给模型。

要点是，为了使模型收敛到通过一次性（所有数据）学习获得的精度，必须多次迭代数据。

下面是使用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