本章节介绍了分类和回归的算法。它还包括讨论特定类别的算法部分，如：线性方法，树和集成。
下面是整个API Doc中的内容框架，这里不会每个都详细介绍，主要会把用到的介绍出来，后续用到的再陆续添加。（下面的链接都是指向官网文档而不是本笔记中的对应内容所在位置，而且有些内容没有出现在本笔记中）
Classification 分类
逻辑回归
逻辑回归是预测分类问题的流行算法。它是 广义线性模型的一个特例来预测结果的可能性。 在spark.ml逻辑回归中可以使用二项式Logistic回归来预测二分类问题，也可以通过使用多项Logistic回归来预测多分类问题。 使用family参数在这两种算法之间进行选择，或者不设置它，让Spark自己推断出正确的值。
通过将family参数设置为“多项式”，也可以将多项Logistic回归用于二分类问题。它将产生两个系数的集合和两个intercept。
当在没有intercept的常量非零列的数据集上对LogisticRegressionModel进行拟合时，Spark MLlib为常数非零列输出零系数。此行为与R glmnet相同，但与LIBSVM不同。
二分类逻辑回归
有关二项式逻辑回归实现的更多背景和更多细节，请参阅spark.mllib中逻辑回归的文档。
代码示例：
以下示例显示了如何用elastic net regularization来训练的二项式和多项Logistic的回归模型用于二分类问题。 elasticNetParam对应于α，regParam对应于λ。(这两个参数的定义参见Linear methods)
Java版代码
public class JavaLogisticRegressionWithElasticNetExample {
public static void main(String[] args) {
SparkSession spark = SparkSession
.builder()
.appName("JavaLogisticRegressionWithElasticNetExample")
.getOrCreate();
// $example on$
// Load training data
Dataset<Row> training = spark.read().format("libsvm")
.load("/home/paul/spark/spark-2.1.0-bin-hadoop2.7/data/mllib/sample_libsvm_data.txt");
LogisticRegression lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(0.3)
.setElasticNetParam(0.8);
// Fit the model
LogisticRegressionModel lrModel = lr.fit(training);
// Print the coefficients and intercept for logistic regression
System.out.println("\n---------- Binomial logistic regression's Coefficients: "
+ lrModel.coefficients() + "\nBinomial Intercept: " + lrModel.intercept());
// We can also use the multinomial family for binary classification
LogisticRegression mlr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(0.3)
.setElasticNetParam(0.8)
.setFamily("multinomial");
// Fit the model
LogisticRegressionModel mlrModel = mlr.fit(training);
// Print the coefficients and intercepts for logistic regression with multinomial family
System.out.println("\n+++++++++ Multinomial coefficients: " + mlrModel.coefficientMatrix()
+ "\nMultinomial intercepts: " + mlrModel.interceptVector());
// $example off$
spark.stop();
}
}
上面代码运行结果，二项式的系数是按照稀疏矩阵格式打印的，多项式的是按照矩阵的格式打印
spark.ml实现的逻辑回归算法也支持提取出训练集上训练后模型的摘要（这有助于分析模型在训练集上的性能）。 需要注意的是预测结果和权值在BinaryLogisticRegressionSummary中被存储为DataFrame类型并且被标注为@transient，所以只能在driver上可用。
LogisticRegressionTrainingSummary
是提供给LogisticRegressionModel
的摘要。目前只有二分类模型有这个功能，而且必须被显式的强转成类型BinaryLogisticRegressionTrainingSummary
。对于多分类模型的摘要的支持将在后续版本中实现。
Java版代码：
public class JavaLogisticRegressionSummaryExample {
public static void main(String[] args) {
SparkSession spark = SparkSession
.builder()
.appName("JavaLogisticRegressionSummaryExample")
.getOrCreate();
// Load training data
Dataset<Row> training = spark.read().format("libsvm")
.load("/home/paul/spark/spark-2.1.0-bin-hadoop2.7/data/mllib/sample_libsvm_data.txt");
LogisticRegression lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(0.3)
.setElasticNetParam(0.8);
// Fit the model
LogisticRegressionModel lrModel = lr.fit(training);
// $example on$
// Extract the summary from the returned LogisticRegressionModel instance trained in the earlier
// example
LogisticRegressionTrainingSummary trainingSummary = lrModel.summary();
// Obtain the loss per iteration.
double[] objectiveHistory = trainingSummary.objectiveHistory();
for (double lossPerIteration : objectiveHistory) {
System.out.println(lossPerIteration);
}
// Obtain the metrics useful to judge performance on test data.
// We cast the summary to a BinaryLogisticRegressionSummary since the problem is a binary
// classification problem.
BinaryLogisticRegressionSummary binarySummary =
(BinaryLogisticRegressionSummary) trainingSummary;
// Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.
Dataset<Row> roc = binarySummary.roc();
roc.show();
roc.select("FPR").show();
System.out.println(binarySummary.areaUnderROC());
// Get the threshold corresponding to the maximum F-Measure and rerun LogisticRegression with
// this selected threshold.
Dataset<Row> fMeasure = binarySummary.fMeasureByThreshold();
double maxFMeasure = fMeasure.select(functions.max("F-Measure")).head().getDouble(0);
double bestThreshold = fMeasure.where(fMeasure.col("F-Measure").equalTo(maxFMeasure))
.select("threshold").head().getDouble(0);
lrModel.setThreshold(bestThreshold);
// $example off$
spark.stop();
}
}
运行结果为：
0.6833149135741672
0.6662875751473734
0.6217068546034618
0.6127265245887887
0.6060347986802873
0.6031750687571562
0.5969621534836274
0.5940743031983118
0.5906089243339022
0.5894724576491042
0.5882187775729587
17/05/02 22:46:21 WARN Executor: 1 block locks were not released by TID = 25:
[rdd_39_0]
+---+--------------------+
|FPR| TPR|
+---+--------------------+
|0.0| 0.0|
|0.0|0.017543859649122806|
|0.0| 0.03508771929824561|
|0.0| 0.05263157894736842|
|0.0| 0.07017543859649122|
|0.0| 0.08771929824561403|
|0.0| 0.10526315789473684|
|0.0| 0.12280701754385964|
|0.0| 0.14035087719298245|
|0.0| 0.15789473684210525|
|0.0| 0.17543859649122806|
|0.0| 0.19298245614035087|
|0.0| 0.21052631578947367|
|0.0| 0.22807017543859648|
|0.0| 0.24561403508771928|
|0.0| 0.2631578947368421|
|0.0| 0.2807017543859649|
|0.0| 0.2982456140350877|
|0.0| 0.3157894736842105|
|0.0| 0.3333333333333333|
+---+--------------------+
only showing top 20 rows
17/05/02 22:46:22 WARN Executor: 1 block locks were not released by TID = 27:
[rdd_39_0]
+---+
|FPR|
+---+
|0.0|
|0.0|
|0.0|
|0.0|
|0.0|
|0.0|
|0.0|
|0.0|
|0.0|
|0.0|
|0.0|
|0.0|
|0.0|
|0.0|
|0.0|
|0.0|
|0.0|
|0.0|
|0.0|
|0.0|
+---+
only showing top 20 rows
1.0
