java 训练模型的代码
时间: 2024-05-09 20:21:12 浏览: 19
以下是使用Java训练模型的代码示例:
1. 导入所需的库
```java
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.ml.classification.LogisticRegression;
import org.apache.spark.ml.classification.LogisticRegressionModel;
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator;
import org.apache.spark.ml.feature.VectorAssembler;
import org.apache.spark.ml.linalg.Vector;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.SQLContext;
```
2. 设置Spark配置和Spark上下文
```java
SparkConf conf = new SparkConf().setAppName("LogisticRegressionExample").setMaster("local");
JavaSparkContext sc = new JavaSparkContext(conf);
SQLContext sqlContext = new SQLContext(sc);
```
3. 加载数据集并转换为DataFrame
```java
JavaRDD<String> inputData = sc.textFile("data/train.csv");
JavaRDD<Row> rows = inputData.map(line -> {
String[] parts = line.split(",");
double label = Double.parseDouble(parts[0]);
double[] features = new double[parts.length - 1];
for (int i = 1; i < parts.length; i++) {
features[i - 1] = Double.parseDouble(parts[i]);
}
Vector vect = Vectors.dense(features);
return RowFactory.create(label, vect);
});
StructType schema = new StructType(new StructField[]{
new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
new StructField("features", new VectorUDT(), false, Metadata.empty())
});
DataFrame data = sqlContext.createDataFrame(rows, schema);
```
4. 将数据集拆分为训练集和测试集
```java
DataFrame[] splits = data.randomSplit(new double[]{0.7, 0.3});
DataFrame trainingData = splits[0];
DataFrame testData = splits[1];
```
5. 创建特征向量和标签列
```java
VectorAssembler assembler = new VectorAssembler()
.setInputCols(new String[]{"features"})
.setOutputCol("featuresVector");
trainingData = assembler.transform(trainingData).select("label", "featuresVector");
testData = assembler.transform(testData).select("label", "featuresVector");
```
6. 训练逻辑回归模型
```java
LogisticRegression lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(0.01)
.setLabelCol("label")
.setFeaturesCol("featuresVector");
LogisticRegressionModel model = lr.fit(trainingData);
```
7. 对测试集进行预测并评估模型性能
```java
DataFrame predictions = model.transform(testData);
BinaryClassificationEvaluator evaluator = new BinaryClassificationEvaluator()
.setLabelCol("label")
.setRawPredictionCol("rawPrediction");
double accuracy = evaluator.evaluate(predictions);
System.out.println("Test Error = " + (1.0 - accuracy));
```
以上代码示例展示了如何使用Java进行训练和评估逻辑回归模型。
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