TF表模型预测 (TFTableModelPredictBatchOp)

Java 类名：com.alibaba.alink.operator.batch.tensorflow.TFTableModelPredictBatchOp

Python 类名：TFTableModelPredictBatchOp

功能介绍

使用 TFTableModelTrainBatchOp 或者 TF2TableModelTrainBatchOp 训练产生的模型进行预测。

参数说明

名称	中文名称	描述	类型	是否必须？	取值范围	默认值
outputSchemaStr	Schema	Schema。格式为“colname coltype[, colname2, coltype2[, …]]”，例如 “f0 string, f1 bigint, f2 double”	String	✓
graphDefTag	graph标签	graph标签	String			“serve”
inputSignatureDefs	输入 SignatureDef	SavedModel 模型的输入 SignatureDef 名，用逗号分隔，需要与输入列一一对应，默认与选择列相同	String[]			null
intraOpParallelism	Op 间并发度	Op 间并发度	Integer			4
outputSignatureDefs	TF 输出 SignatureDef 名	模型的输出 SignatureDef 名，多个输出时用逗号分隔，并且与输出 Schema 一一对应，默认与输出 Schema 中的列名相同	String[]			null
reservedCols	算法保留列名	算法保留列	String[]			null
selectedCols	选中的列名数组	计算列对应的列名列表	String[]			null
signatureDefKey	signature标签	signature标签	String			“serving_default”

代码示例

** 以下代码仅用于示意，可能需要修改部分代码或者配置环境后才能正常运行！**

Python 代码

import json

source = RandomTableSourceBatchOp() \
    .setNumRows(100) \
    .setNumCols(10)

colNames = source.getColNames()
source = source.select("*, case when RAND() > 0.5 then 1. else 0. end as label")
label = "label"

userParams = {
    'featureCols': json.dumps(colNames),
    'labelCol': label,
    'batch_size': 16,
    'num_epochs': 1
}

tfTableModelTrainBatchOp = TFTableModelTrainBatchOp() \
    .setUserFiles(["https://alink-release.oss-cn-beijing.aliyuncs.com/data-files/tf_dnn_train.py"]) \
    .setMainScriptFile("https://alink-release.oss-cn-beijing.aliyuncs.com/data-files/tf_dnn_train.py") \
    .setUserParams(json.dumps(userParams)) \
    .linkFrom(source)

tfTableModelPredictBatchOp = TFTableModelPredictBatchOp() \
    .setOutputSchemaStr("logits double") \
    .setOutputSignatureDefs(["logits"]) \
    .setSignatureDefKey("predict") \
    .setSelectedCols(colNames) \
    .linkFrom(tfTableModelTrainBatchOp, source)
tfTableModelPredictBatchOp.print()

Java 代码

import com.alibaba.alink.common.utils.JsonConverter;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.source.RandomTableSourceBatchOp;
import com.alibaba.alink.operator.batch.tensorflow.TFTableModelPredictBatchOp;
import com.alibaba.alink.operator.batch.tensorflow.TFTableModelTrainBatchOp;
import org.junit.Test;

import java.util.HashMap;
import java.util.Map;

public class TFTableModelPredictBatchOpTest {
	@Test
	public void testTFTableModelPredictBatchOp() throws Exception {
		BatchOperator<?> source = new RandomTableSourceBatchOp()
			.setNumRows(100L)
			.setNumCols(10);

		String[] colNames = source.getColNames();
		source = source.select("*, case when RAND() > 0.5 then 1. else 0. end as label");
		String label = "label";

		Map <String, Object> userParams = new HashMap <>();
		userParams.put("featureCols", JsonConverter.toJson(colNames));
		userParams.put("labelCol", label);
		userParams.put("batch_size", 16);
		userParams.put("num_epochs", 1);

		TFTableModelTrainBatchOp tfTableModelTrainBatchOp = new TFTableModelTrainBatchOp()
			.setUserFiles(new String[] {"https://alink-release.oss-cn-beijing.aliyuncs.com/data-files/tf_dnn_train.py"})
			.setMainScriptFile("https://alink-release.oss-cn-beijing.aliyuncs.com/data-files/tf_dnn_train.py")
			.setUserParams(JsonConverter.toJson(userParams))
			.linkFrom(source);

		TFTableModelPredictBatchOp tfTableModelPredictBatchOp = new TFTableModelPredictBatchOp()
			.setOutputSchemaStr("logits double")
			.setOutputSignatureDefs(new String[]{"logits"})
			.setSignatureDefKey("predict")
			.setSelectedCols(colNames)
			.linkFrom(tfTableModelTrainBatchOp, source);
		tfTableModelPredictBatchOp.print();
	}
}

运行结果

col0	col1	col2	col3	col4	col5	col6	col7	col8	col9	label	logits
0.7310	0.2405	0.6374	0.5504	0.5975	0.3332	0.3852	0.9848	0.8792	0.9412	0	-0.4253
0.2750	0.1289	0.1466	0.0232	0.5467	0.9645	0.1045	0.6251	0.4108	0.7763	0	-0.4099
0.9907	0.4872	0.7462	0.7332	0.8173	0.8389	0.5267	0.8993	0.1339	0.0831	0	-0.3881
0.9786	0.7224	0.7150	0.1432	0.4630	0.0045	0.0715	0.3484	0.3388	0.8594	0	-0.3044
0.9715	0.8657	0.6126	0.1790	0.2176	0.8545	0.0097	0.6923	0.7713	0.7127	0	-0.4693
…	…	…	…	…	…	…	…	…	…	…	…