Alink的线性模型（LR、SVM、Softmax、线性回归、LASSO回归等）都提供了增量训练的功能，在使用方式上，相当于提供了初始模型的训练，如下面伪代码所示，逻辑回归训练组件LogisticRegressionTrainBatchOp不仅要接入训练数据train_set，还要接入初始模型init_model。linkFrom方法的第二个参数是可选的，如果没有初始模型，则与一般的逻辑回归训练用法是一样的。
AkSourceBatchOp train_set = new AkSourceBatchOp().setFilePath("...");
AkSourceBatchOp init_model = new AkSourceBatchOp().setFilePath("...");
......
new LogisticRegressionTrainBatchOp()
.setFeatureCols(NUMERICAL_COL_NAMES)
.setLabelCol(LABEL_COL_NAME)
.linkFrom(train_set, init_model)
......我们使用增量训练可以显著减少训练时间，增加模型的实效性。增量训练出来的模型可以用于生成模型流，从而使用模型流的机制，流式预测和LocalPredictor嵌入式预测场景动态更新模型。
下面的实验会进行10次逻辑回归增量训练，第一次是使用原始的初始模型（模型文件路径为：DATA_DIR + INIT_NUMERIC_LR_MODEL_FILE），训练结果添加到模型流文件夹；第二次会从模型流文件夹中找到最新生成的子文件夹，从中读取逻辑回归模型作为第二次训练的初始模型，训练结果添加到模型流文件夹；后面各次的流程与第二次类似。
String current_base_model_path = DATA_DIR + INIT_NUMERIC_LR_MODEL_FILE;
for (int i = 0; i < 10; i++) {
if (i > 0) {
long latest_time = -1L;
for (File subdir : new File(DATA_DIR + FTRL_MODEL_STREAM_DIR).listFiles()) {
if (!subdir.getName().equals("conf") && subdir.lastModified() > latest_time) {
latest_time = subdir.lastModified();
current_base_model_path = subdir.getCanonicalPath();
}
}
}
System.out.println(current_base_model_path);
BatchOperator <?> train_set = new CsvSourceBatchOp()
.setFilePath(DATA_DIR + "avazu-small.csv")
.setSchemaStr(SCHEMA_STRING);
AkSourceBatchOp init_model = new AkSourceBatchOp().setFilePath(current_base_model_path);
new LogisticRegressionTrainBatchOp()
.setFeatureCols(NUMERICAL_COL_NAMES)
.setLabelCol(LABEL_COL_NAME)
.setMaxIter(5)
.linkFrom(train_set, init_model)
.link(
new AppendModelStreamFileSinkBatchOp()
.setFilePath(DATA_DIR + FTRL_MODEL_STREAM_DIR)
.setNumKeepModel(10)
);
BatchOperator.execute();
Thread.sleep(2000);
}运行输出如下，显示的是每次训练所用的初始模型路径，第一次用的是init_numeric_lr_model.ak，后面各次都用的是模型流文件夹中最新的模型路径。
/Users/yangxu/alink/data/ctr_avazu/init_numeric_lr_model.ak /Users/yangxu/alink/data/ctr_avazu/ftrl_model_stream/20211215113020767 /Users/yangxu/alink/data/ctr_avazu/ftrl_model_stream/20211215113045333 /Users/yangxu/alink/data/ctr_avazu/ftrl_model_stream/20211215113101764 /Users/yangxu/alink/data/ctr_avazu/ftrl_model_stream/20211215113116845 /Users/yangxu/alink/data/ctr_avazu/ftrl_model_stream/20211215113131302 /Users/yangxu/alink/data/ctr_avazu/ftrl_model_stream/20211215113145603 /Users/yangxu/alink/data/ctr_avazu/ftrl_model_stream/20211215113159833 /Users/yangxu/alink/data/ctr_avazu/ftrl_model_stream/2021121511321398 /Users/yangxu/alink/data/ctr_avazu/ftrl_model_stream/20211215113228048
上面的代码为函数Chap29.c_5()的内容，同时运行Chap29.c_5()和第29.3.3节介绍的LocalPredictor使用模型流预测的程序Chap29.c_3_3()，即，一边产生新的逻辑回归模型，一边不断更新localPredictor的预测模型。Chap29.c_3_3()的运行的部分结果如下所示，因为每次预测的都是同一个数据，如果模型没有变化，结果也是相同的，我们从运行结果上，可以看出预测结果在发生变化，说明了localPredictor在根据模型流更新预测模型。
18 1,0,{"0":"0.8059634797835652","1":"0.1940365202164348"}
19 1,0,{"0":"0.8059634797835652","1":"0.1940365202164348"}
20 1,0,{"0":"0.8059634797835652","1":"0.1940365202164348"}
21 1,0,{"0":"0.8060413109308956","1":"0.1939586890691044"}
22 1,0,{"0":"0.8060413109308956","1":"0.1939586890691044"}
23 1,0,{"0":"0.8060413109308956","1":"0.1939586890691044"}
24 1,0,{"0":"0.8060413109308956","1":"0.1939586890691044"}
25 1,0,{"0":"0.8060413109308956","1":"0.1939586890691044"}
26 1,0,{"0":"0.8065659127557904","1":"0.19343408724420963"}
27 1,0,{"0":"0.8065659127557904","1":"0.19343408724420963"}
28 1,0,{"0":"0.8065659127557904","1":"0.19343408724420963"}
29 1,0,{"0":"0.8065659127557904","1":"0.19343408724420963"}
30 1,0,{"0":"0.8065659127557904","1":"0.19343408724420963"}
31 1,0,{"0":"0.8065659127557904","1":"0.19343408724420963"}
32 1,0,{"0":"0.8065659127557904","1":"0.19343408724420963"}
33 1,0,{"0":"0.8065659127557904","1":"0.19343408724420963"}
34 1,0,{"0":"0.8065659127557904","1":"0.19343408724420963"}
35 1,0,{"0":"0.8065659127557904","1":"0.19343408724420963"}
36 1,0,{"0":"0.8065599998161146","1":"0.1934400001838854"}
37 1,0,{"0":"0.8065599998161146","1":"0.1934400001838854"}
38 1,0,{"0":"0.8065599998161146","1":"0.1934400001838854"}
39 1,0,{"0":"0.8065599998161146","1":"0.1934400001838854"}
40 1,0,{"0":"0.8065599998161146","1":"0.1934400001838854"}
41 1,0,{"0":"0.8069020847473007","1":"0.19309791525269926"}
42 1,0,{"0":"0.8069020847473007","1":"0.19309791525269926"}
43 1,0,{"0":"0.8069020847473007","1":"0.19309791525269926"}
44 1,0,{"0":"0.8069020847473007","1":"0.19309791525269926"}