IForestOutlierBatchOp进行异常检测时，输出结果顺序颠倒

[LXF0047]

`

    String[] FEATURE_NAMES = {"f1", "f2", "f3", "f4", "f5",
            "f6", "f7", "f8", "f9"};
    String[] ALL_NAMES = {"f1", "f2", "f3", "f4", "f5",
            "f6", "f7", "f8", "f9", "label"};
    BatchOperator <?> data = new MemSourceBatchOp(
            new Object[][] {
                    {10, 10, 10, 10, 10, 10, 10, 10, 10, 0},
                    {10, 10, 10, 10, 10, 10, 10, 10, 10, 0},
                    {10, 10, 10, 10, 10, 10, 10, 10, 10, 0},
                    {10, 10, 10, 10, 10, 10, 10, 10, 10, 0},
                    {10, 10, 10, 10, 10, 10, 10, 10, 10, 0},
                    {1, 1, 1, 1, 1, 1, 1, 1, 100, 1},
                    {1, 1, 1, 1, 1, 1, 1, 1, 200, 1},
                    {1, 1, 1, 1, 1, 1, 1, 1, 300, 1},
            },
            ALL_NAMES);
    BatchOperator <?> outlier = new IForestOutlierBatchOp()
            .setFeatureCols(FEATURE_NAMES)
            .setPredictionCol("pred")
            .setPredictionDetailCol("pred_detail");
    data.link(outlier).print();

`
的运行结果为：

f1	f2	f3	f4	f5	f6	f7	f8	f9	label	pred	pred_detail
10	10	10	10	10	10	10	10	10	0	true	{"outlier_score":"0.5907573613555553","is_outlier":"true"}
10	10	10	10	10	10	10	10	10	0	true	{"outlier_score":"0.5496377996962295","is_outlier":"true"}
10	10	10	10	10	10	10	10	10	0	true	{"outlier_score":"0.5988412960965512","is_outlier":"true"}
10	10	10	10	10	10	10	10	10	0	false	{"outlier_score":"0.48774601295119835","is_outlier":"false"}
10	10	10	10	10	10	10	10	10	0	false	{"outlier_score":"0.48774601295119835","is_outlier":"false"}
1	1	1	1	1	1	1	1	100	1	false	{"outlier_score":"0.48774601295119835","is_outlier":"false"}
1	1	1	1	1	1	1	1	200	1	false	{"outlier_score":"0.48774601295119835","is_outlier":"false"}
1	1	1	1	1	1	1	1	300	1	false	{"outlier_score":"0.48774601295119835","is_outlier":"false"}

后三行的结果跑到前三行中