所用数据源,请参考本人博客http://www.cnblogs.com/wwxbi/p/6063613.html
1.导入包
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import
org.apache.spark.sql.SparkSession
import
org.apache.spark.sql.Dataset
import
org.apache.spark.sql.Row
import
org.apache.spark.sql.DataFrame
import
org.apache.spark.sql.Column
import
org.apache.spark.sql.DataFrameReader
import
org.apache.spark.rdd.RDD
import
org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
import
org.apache.spark.sql.Encoder
import
org.apache.spark.sql.DataFrameStatFunctions
import
org.apache.spark.sql.functions.
_
import
org.apache.spark.ml.Pipeline
import
org.apache.spark.ml.classification.DecisionTreeClassificationModel
import
org.apache.spark.ml.classification.DecisionTreeClassifier
import
org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import
org.apache.spark.ml.feature.VectorAssembler
import
org.apache.spark.ml.feature.StringIndexer
import
org.apache.spark.ml.feature.IndexToString
import
org.apache.spark.ml.feature.VectorIndexer
import
org.apache.spark.ml.feature.VectorSlicer
|
2.加载数据源
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val
spark
=
SparkSession.builder().appName(
"Spark decision tree classifier"
).config(
"spark.some.config.option"
,
"some-value"
).getOrCreate()
// For implicit conversions like converting RDDs to DataFrames
import
spark.implicits.
_
// 这里仅仅是示例数据,数据源,请参考本人博客http://www.cnblogs.com/wwxbi/p/6063613.html
val
dataList
:
List[(Double, String, Double, Double, String, Double, Double, Double, Double)]
=
List(
(
0
,
"male"
,
37
,
10
,
"no"
,
3
,
18
,
7
,
4
),
(
0
,
"female"
,
27
,
4
,
"no"
,
4
,
14
,
6
,
4
),
(
0
,
"female"
,
32
,
15
,
"yes"
,
1
,
12
,
1
,
4
),
(
0
,
"male"
,
57
,
15
,
"yes"
,
5
,
18
,
6
,
5
),
(
0
,
"male"
,
22
,
0.75
,
"no"
,
2
,
17
,
6
,
3
),
(
0
,
"female"
,
32
,
1.5
,
"no"
,
2
,
17
,
5
,
5
))
val
data
=
dataList.toDF(
"affairs"
,
"gender"
,
"age"
,
"yearsmarried"
,
"children"
,
"religiousness"
,
"education"
,
"occupation"
,
"rating"
)
data.createOrReplaceTempView(
"data"
)
// 字符类型转换成数值
val
labelWhere
=
"case when affairs=0 then 0 else cast(1 as double) end as label"
val
genderWhere
=
"case when gender='female' then 0 else cast(1 as double) end as gender"
val
childrenWhere
=
"case when children='no' then 0 else cast(1 as double) end as children"
val
dataLabelDF
=
spark.sql(s
"select $labelWhere, $genderWhere,age,yearsmarried,$childrenWhere,religiousness,education,occupation,rating from data"
)
|
3.创建决策树模型
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val
featuresArray
=
Array(
"gender"
,
"age"
,
"yearsmarried"
,
"children"
,
"religiousness"
,
"education"
,
"occupation"
,
"rating"
)
// 字段转换成特征向量
val
assembler
=
new
VectorAssembler().setInputCols(featuresArray).setOutputCol(
"features"
)
val
vecDF
:
DataFrame
=
assembler.transform(dataLabelDF)
vecDF.show(
10
, truncate
=
false
)
// 索引标签,将元数据添加到标签列中
val
labelIndexer
=
new
StringIndexer().setInputCol(
"label"
).setOutputCol(
"indexedLabel"
).fit(vecDF)
labelIndexer.transform(vecDF).show(
10
, truncate
=
false
)
// 自动识别分类的特征,并对它们进行索引
// 具有大于5个不同的值的特征被视为连续。
val
featureIndexer
=
new
VectorIndexer().setInputCol(
"features"
).setOutputCol(
"indexedFeatures"
).setMaxCategories(
5
).fit(vecDF)
featureIndexer.transform(vecDF).show(
10
, truncate
=
false
)
// 将数据分为训练和测试集(30%进行测试)
val
Array(trainingData, testData)
=
vecDF.randomSplit(Array(
0.7
,
0.3
))
// 训练决策树模型
val
dt
=
new
DecisionTreeClassifier()
.setLabelCol(
"indexedLabel"
)
.setFeaturesCol(
"indexedFeatures"
)
.setImpurity(
"entropy"
)
// 不纯度
.setMaxBins(
100
)
// 离散化"连续特征"的最大划分数
.setMaxDepth(
5
)
// 树的最大深度
.setMinInfoGain(
0.01
)
//一个节点分裂的最小信息增益,值为[0,1]
.setMinInstancesPerNode(
10
)
//每个节点包含的最小样本数
.setSeed(
123456
)
// 将索引标签转换回原始标签
val
labelConverter
=
new
IndexToString().setInputCol(
"prediction"
).setOutputCol(
"predictedLabel"
).setLabels(labelIndexer.labels)
// Chain indexers and tree in a Pipeline.
val
pipeline
=
new
Pipeline().setStages(Array(labelIndexer, featureIndexer, dt, labelConverter))
// Train model. This also runs the indexers.
val
model
=
pipeline.fit(trainingData)
// 作出预测
val
predictions
=
model.transform(testData)
// 选择几个示例行展示
predictions.select(
"predictedLabel"
,
"label"
,
"features"
).show(
10
, truncate
=
false
)
// 选择(预测标签,实际标签),并计算测试误差。
val
evaluator
=
new
MulticlassClassificationEvaluator().setLabelCol(
"indexedLabel"
).setPredictionCol(
"prediction"
).setMetricName(
"accuracy"
)
val
accuracy
=
evaluator.evaluate(predictions)
println(
"Test Error = "
+ (
1.0
- accuracy))
// 这里的stages(2)中的“2”对应pipeline中的“dt”,将model强制转换为DecisionTreeClassificationModel类型
val
treeModel
=
model.stages(
2
).asInstanceOf[DecisionTreeClassificationModel]
treeModel.getLabelCol
treeModel.getFeaturesCol
treeModel.featureImportances
treeModel.getPredictionCol
treeModel.getProbabilityCol
treeModel.numClasses
treeModel.numFeatures
treeModel.depth
treeModel.numNodes
treeModel.getImpurity
treeModel.getMaxBins
treeModel.getMaxDepth
treeModel.getMaxMemoryInMB
treeModel.getMinInfoGain
treeModel.getMinInstancesPerNode
println(
"Learned classification tree model:\n"
+ treeModel.toDebugString)
|
4.代码执行结果
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val
data
=
dataList.toDF(
"affairs"
,
"gender"
,
"age"
,
"yearsmarried"
,
"children"
,
"religiousness"
,
"education"
,
"occupation"
,
"rating"
)
data.show(
10
, truncate
=
false
)
+-------+------+----+------------+--------+-------------+---------+----------+------+
|affairs|gender|age |yearsmarried|children|religiousness|education|occupation|rating|
+-------+------+----+------------+--------+-------------+---------+----------+------+
|
0.0
|male |
37.0
|
10.0
|no |
3.0
|
18.0
|
7.0
|
4.0
|
|
0.0
|female|
27.0
|
4.0
|no |
4.0
|
14.0
|
6.0
|
4.0
|
|
0.0
|female|
32.0
|
15.0
|yes |
1.0
|
12.0
|
1.0
|
4.0
|
|
0.0
|male |
57.0
|
15.0
|yes |
5.0
|
18.0
|
6.0
|
5.0
|
|
0.0
|male |
22.0
|
0.75
|no |
2.0
|
17.0
|
6.0
|
3.0
|
|
0.0
|female|
32.0
|
1.5
|no |
2.0
|
17.0
|
5.0
|
5.0
|
|
0.0
|female|
22.0
|
0.75
|no |
2.0
|
12.0
|
1.0
|
3.0
|
|
0.0
|male |
57.0
|
15.0
|yes |
2.0
|
14.0
|
4.0
|
4.0
|
|
0.0
|female|
32.0
|
15.0
|yes |
4.0
|
16.0
|
1.0
|
2.0
|
|
0.0
|male |
22.0
|
1.5
|no |
4.0
|
14.0
|
4.0
|
5.0
|
+-------+------+----+------------+--------+-------------+---------+----------+------+
only showing top
10
rows
data.createOrReplaceTempView(
"data"
)
// 字符类型转换成数值
val
labelWhere
=
"case when affairs=0 then 0 else cast(1 as double) end as label"
val
genderWhere
=
"case when gender='female' then 0 else cast(1 as double) end as gender"
val
childrenWhere
=
"case when children='no' then 0 else cast(1 as double) end as children"
val
dataLabelDF
=
spark.sql(s
"select $labelWhere, $genderWhere,age,yearsmarried,$childrenWhere,religiousness,education,occupation,rating from data"
)
dataLabelDF.show(
10
, truncate
=
false
)
+-----+------+----+------------+--------+-------------+---------+----------+------+
|label|gender|age |yearsmarried|children|religiousness|education|occupation|rating|
+-----+------+----+------------+--------+-------------+---------+----------+------+
|
0.0
|
1.0
|
37.0
|
10.0
|
0.0
|
3.0
|
18.0
|
7.0
|
4.0
|
|
0.0
|
0.0
|
27.0
|
4.0
|
0.0
|
4.0
|
14.0
|
6.0
|
4.0
|
|
0.0
|
0.0
|
32.0
|
15.0
|
1.0
|
1.0
|
12.0
|
1.0
|
4.0
|
|
0.0
|
1.0
|
57.0
|
15.0
|
1.0
|
5.0
|
18.0
|
6.0
|
5.0
|
|
0.0
|
1.0
|
22.0
|
0.75
|
0.0
|
2.0
|
17.0
|
6.0
|
3.0
|
|
0.0
|
0.0
|
32.0
|
1.5
|
0.0
|
2.0
|
17.0
|
5.0
|
5.0
|
|
0.0
|
0.0
|
22.0
|
0.75
|
0.0
|
2.0
|
12.0
|
1.0
|
3.0
|
|
0.0
|
1.0
|
57.0
|
15.0
|
1.0
|
2.0
|
14.0
|
4.0
|
4.0
|
|
0.0
|
0.0
|
32.0
|
15.0
|
1.0
|
4.0
|
16.0
|
1.0
|
2.0
|
|
0.0
|
1.0
|
22.0
|
1.5
|
0.0
|
4.0
|
14.0
|
4.0
|
5.0
|
+-----+------+----+------------+--------+-------------+---------+----------+------+
only showing top
10
rows
val
featuresArray
=
Array(
"gender"
,
"age"
,
"yearsmarried"
,
"children"
,
"religiousness"
,
"education"
,
"occupation"
,
"rating"
)
// 字段转换成特征向量
val
assembler
=
new
VectorAssembler().setInputCols(featuresArray).setOutputCol(
"features"
)
val
vecDF
:
DataFrame
=
assembler.transform(dataLabelDF)
vecDF.show(
10
, truncate
=
false
)
+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+
|label|gender|age |yearsmarried|children|religiousness|education|occupation|rating|features |
+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+
|
0.0
|
1.0
|
37.0
|
10.0
|
0.0
|
3.0
|
18.0
|
7.0
|
4.0
|[
1.0
,
37.0
,
10.0
,
0.0
,
3.0
,
18.0
,
7.0
,
4.0
]|
|
0.0
|
0.0
|
27.0
|
4.0
|
0.0
|
4.0
|
14.0
|
6.0
|
4.0
|[
0.0
,
27.0
,
4.0
,
0.0
,
4.0
,
14.0
,
6.0
,
4.0
] |
|
0.0
|
0.0
|
32.0
|
15.0
|
1.0
|
1.0
|
12.0
|
1.0
|
4.0
|[
0.0
,
32.0
,
15.0
,
1.0
,
1.0
,
12.0
,
1.0
,
4.0
]|
|
0.0
|
1.0
|
57.0
|
15.0
|
1.0
|
5.0
|
18.0
|
6.0
|
5.0
|[
1.0
,
57.0
,
15.0
,
1.0
,
5.0
,
18.0
,
6.0
,
5.0
]|
|
0.0
|
1.0
|
22.0
|
0.75
|
0.0
|
2.0
|
17.0
|
6.0
|
3.0
|[
1.0
,
22.0
,
0.75
,
0.0
,
2.0
,
17.0
,
6.0
,
3.0
]|
|
0.0
|
0.0
|
32.0
|
1.5
|
0.0
|
2.0
|
17.0
|
5.0
|
5.0
|[
0.0
,
32.0
,
1.5
,
0.0
,
2.0
,
17.0
,
5.0
,
5.0
] |
|
0.0
|
0.0
|
22.0
|
0.75
|
0.0
|
2.0
|
12.0
|
1.0
|
3.0
|[
0.0
,
22.0
,
0.75
,
0.0
,
2.0
,
12.0
,
1.0
,
3.0
]|
|
0.0
|
1.0
|
57.0
|
15.0
|
1.0
|
2.0
|
14.0
|
4.0
|
4.0
|[
1.0
,
57.0
,
15.0
,
1.0
,
2.0
,
14.0
,
4.0
,
4.0
]|
|
0.0
|
0.0
|
32.0
|
15.0
|
1.0
|
4.0
|
16.0
|
1.0
|
2.0
|[
0.0
,
32.0
,
15.0
,
1.0
,
4.0
,
16.0
,
1.0
,
2.0
]|
|
0.0
|
1.0
|
22.0
|
1.5
|
0.0
|
4.0
|
14.0
|
4.0
|
5.0
|[
1.0
,
22.0
,
1.5
,
0.0
,
4.0
,
14.0
,
4.0
,
5.0
] |
+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+
only showing top
10
rows
// 索引标签,将元数据添加到标签列中
val
labelIndexer
=
new
StringIndexer().setInputCol(
"label"
).setOutputCol(
"indexedLabel"
).fit(vecDF)
labelIndexer.transform(vecDF).show(
10
, truncate
=
false
)
+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+------------+
|label|gender|age |yearsmarried|children|religiousness|education|occupation|rating|features |indexedLabel|
+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+------------+
|
0.0
|
1.0
|
37.0
|
10.0
|
0.0
|
3.0
|
18.0
|
7.0
|
4.0
|[
1.0
,
37.0
,
10.0
,
0.0
,
3.0
,
18.0
,
7.0
,
4.0
]|
0.0
|
|
0.0
|
0.0
|
27.0
|
4.0
|
0.0
|
4.0
|
14.0
|
6.0
|
4.0
|[
0.0
,
27.0
,
4.0
,
0.0
,
4.0
,
14.0
,
6.0
,
4.0
] |
0.0
|
|
0.0
|
0.0
|
32.0
|
15.0
|
1.0
|
1.0
|
12.0
|
1.0
|
4.0
|[
0.0
,
32.0
,
15.0
,
1.0
,
1.0
,
12.0
,
1.0
,
4.0
]|
0.0
|
|
0.0
|
1.0
|
57.0
|
15.0
|
1.0
|
5.0
|
18.0
|
6.0
|
5.0
|[
1.0
,
57.0
,
15.0
,
1.0
,
5.0
,
18.0
,
6.0
,
5.0
]|
0.0
|
|
0.0
|
1.0
|
22.0
|
0.75
|
0.0
|
2.0
|
17.0
|
6.0
|
3.0
|[
1.0
,
22.0
,
0.75
,
0.0
,
2.0
,
17.0
,
6.0
,
3.0
]|
0.0
|
|
0.0
|
0.0
|
32.0
|
1.5
|
0.0
|
2.0
|
17.0
|
5.0
|
5.0
|[
0.0
,
32.0
,
1.5
,
0.0
,
2.0
,
17.0
,
5.0
,
5.0
] |
0.0
|
|
0.0
|
0.0
|
22.0
|
0.75
|
0.0
|
2.0
|
12.0
|
1.0
|
3.0
|[
0.0
,
22.0
,
0.75
,
0.0
,
2.0
,
12.0
,
1.0
,
3.0
]|
0.0
|
|
0.0
|
1.0
|
57.0
|
15.0
|
1.0
|
2.0
|
14.0
|
4.0
|
4.0
|[
1.0
,
57.0
,
15.0
,
1.0
,
2.0
,
14.0
,
4.0
,
4.0
]|
0.0
|
|
0.0
|
0.0
|
32.0
|
15.0
|
1.0
|
4.0
|
16.0
|
1.0
|
2.0
|[
0.0
,
32.0
,
15.0
,
1.0
,
4.0
,
16.0
,
1.0
,
2.0
]|
0.0
|
|
0.0
|
1.0
|
22.0
|
1.5
|
0.0
|
4.0
|
14.0
|
4.0
|
5.0
|[
1.0
,
22.0
,
1.5
,
0.0
,
4.0
,
14.0
,
4.0
,
5.0
] |
0.0
|
+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+------------+
only showing top
10
rows
// 自动识别分类的特征,并对它们进行索引
// 具有大于5个不同的值的特征被视为连续。
val
featureIndexer
=
new
VectorIndexer().setInputCol(
"features"
).setOutputCol(
"indexedFeatures"
).setMaxCategories(
5
).fit(vecDF)
featureIndexer.transform(vecDF).show(
10
, truncate
=
false
)
featureIndexer.transform(vecDF).show(
10
, truncate
=
false
)
+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+------------------------------------+
|label|gender|age |yearsmarried|children|religiousness|education|occupation|rating|features |indexedFeatures |
+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+------------------------------------+
|
0.0
|
1.0
|
37.0
|
10.0
|
0.0
|
3.0
|
18.0
|
7.0
|
4.0
|[
1.0
,
37.0
,
10.0
,
0.0
,
3.0
,
18.0
,
7.0
,
4.0
]|[
1.0
,
37.0
,
10.0
,
0.0
,
2.0
,
18.0
,
7.0
,
3.0
]|
|
0.0
|
0.0
|
27.0
|
4.0
|
0.0
|
4.0
|
14.0
|
6.0
|
4.0
|[
0.0
,
27.0
,
4.0
,
0.0
,
4.0
,
14.0
,
6.0
,
4.0
] |[
0.0
,
27.0
,
4.0
,
0.0
,
3.0
,
14.0
,
6.0
,
3.0
] |
|
0.0
|
0.0
|
32.0
|
15.0
|
1.0
|
1.0
|
12.0
|
1.0
|
4.0
|[
0.0
,
32.0
,
15.0
,
1.0
,
1.0
,
12.0
,
1.0
,
4.0
]|[
0.0
,
32.0
,
15.0
,
1.0
,
0.0
,
12.0
,
1.0
,
3.0
]|
|
0.0
|
1.0
|
57.0
|
15.0
|
1.0
|
5.0
|
18.0
|
6.0
|
5.0
|[
1.0
,
57.0
,
15.0
,
1.0
,
5.0
,
18.0
,
6.0
,
5.0
]|[
1.0
,
57.0
,
15.0
,
1.0
,
4.0
,
18.0
,
6.0
,
4.0
]|
|
0.0
|
1.0
|
22.0
|
0.75
|
0.0
|
2.0
|
17.0
|
6.0
|
3.0
|[
1.0
,
22.0
,
0.75
,
0.0
,
2.0
,
17.0
,
6.0
,
3.0
]|[
1.0
,
22.0
,
0.75
,
0.0
,
1.0
,
17.0
,
6.0
,
2.0
]|
|
0.0
|
0.0
|
32.0
|
1.5
|
0.0
|
2.0
|
17.0
|
5.0
|
5.0
|[
0.0
,
32.0
,
1.5
,
0.0
,
2.0
,
17.0
,
5.0
,
5.0
] |[
0.0
,
32.0
,
1.5
,
0.0
,
1.0
,
17.0
,
5.0
,
4.0
] |
|
0.0
|
0.0
|
22.0
|
0.75
|
0.0
|
2.0
|
12.0
|
1.0
|
3.0
|[
0.0
,
22.0
,
0.75
,
0.0
,
2.0
,
12.0
,
1.0
,
3.0
]|[
0.0
,
22.0
,
0.75
,
0.0
,
1.0
,
12.0
,
1.0
,
2.0
]|
|
0.0
|
1.0
|
57.0
|
15.0
|
1.0
|
2.0
|
14.0
|
4.0
|
4.0
|[
1.0
,
57.0
,
15.0
,
1.0
,
2.0
,
14.0
,
4.0
,
4.0
]|[
1.0
,
57.0
,
15.0
,
1.0
,
1.0
,
14.0
,
4.0
,
3.0
]|
|
0.0
|
0.0
|
32.0
|
15.0
|
1.0
|
4.0
|
16.0
|
1.0
|
2.0
|[
0.0
,
32.0
,
15.0
,
1.0
,
4.0
,
16.0
,
1.0
,
2.0
]|[
0.0
,
32.0
,
15.0
,
1.0
,
3.0
,
16.0
,
1.0
,
1.0
]|
|
0.0
|
1.0
|
22.0
|
1.5
|
0.0
|
4.0
|
14.0
|
4.0
|
5.0
|[
1.0
,
22.0
,
1.5
,
0.0
,
4.0
,
14.0
,
4.0
,
5.0
] |[
1.0
,
22.0
,
1.5
,
0.0
,
3.0
,
14.0
,
4.0
,
4.0
] |
+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+------------------------------------+
only showing top
10
rows
// 将数据分为训练和测试集(30%进行测试)
val
Array(trainingData, testData)
=
vecDF.randomSplit(Array(
0.7
,
0.3
))
// 训练决策树模型
val
dt
=
new
DecisionTreeClassifier().setLabelCol(
"indexedLabel"
).setFeaturesCol(
"indexedFeatures"
).setImpurity(
"entropy"
).setMaxBins(
100
).setMaxDepth(
5
).setMinInfoGain(
0.01
).setMinInstancesPerNode(
10
).setSeed(
123456
)
//.setLabelCol("indexedLabel")
//.setFeaturesCol("indexedFeatures")
//.setImpurity("entropy") // 不纯度
//.setMaxBins(100) // 离散化"连续特征"的最大划分数
//.setMaxDepth(5) // 树的最大深度
//.setMinInfoGain(0.01) //一个节点分裂的最小信息增益,值为[0,1]
//.setMinInstancesPerNode(10) //每个节点包含的最小样本数
//.setSeed(123456)
// 将索引标签转换回原始标签
val
labelConverter
=
new
IndexToString().setInputCol(
"prediction"
).setOutputCol(
"predictedLabel"
).setLabels(labelIndexer.labels)
// Chain indexers and tree in a Pipeline.
val
pipeline
=
new
Pipeline().setStages(Array(labelIndexer, featureIndexer, dt, labelConverter))
// Train model. This also runs the indexers.
val
model
=
pipeline.fit(trainingData)
// 作出预测
val
predictions
=
model.transform(testData)
// 选择几个示例行展示
predictions.select(
"predictedLabel"
,
"label"
,
"features"
).show(
10
, truncate
=
false
)
+--------------+-----+-------------------------------------+
|predictedLabel|label|features |
+--------------+-----+-------------------------------------+
|
0.0
|
0.0
|[
0.0
,
22.0
,
0.125
,
0.0
,
2.0
,
14.0
,
4.0
,
5.0
]|
|
0.0
|
0.0
|[
0.0
,
22.0
,
0.417
,
0.0
,
1.0
,
17.0
,
6.0
,
4.0
]|
|
0.0
|
0.0
|[
0.0
,
22.0
,
0.75
,
0.0
,
2.0
,
18.0
,
6.0
,
5.0
] |
|
0.0
|
0.0
|[
0.0
,
22.0
,
0.75
,
0.0
,
3.0
,
16.0
,
1.0
,
5.0
] |
|
0.0
|
0.0
|[
0.0
,
22.0
,
0.75
,
0.0
,
4.0
,
16.0
,
1.0
,
5.0
] |
|
0.0
|
0.0
|[
0.0
,
22.0
,
1.5
,
0.0
,
1.0
,
14.0
,
1.0
,
5.0
] |
|
0.0
|
0.0
|[
0.0
,
22.0
,
1.5
,
0.0
,
2.0
,
14.0
,
1.0
,
5.0
] |
|
0.0
|
0.0
|[
0.0
,
22.0
,
1.5
,
0.0
,
2.0
,
16.0
,
5.0
,
5.0
] |
|
0.0
|
0.0
|[
0.0
,
22.0
,
1.5
,
0.0
,
2.0
,
16.0
,
5.0
,
5.0
] |
|
0.0
|
0.0
|[
0.0
,
22.0
,
1.5
,
0.0
,
2.0
,
17.0
,
5.0
,
4.0
] |
+--------------+-----+-------------------------------------+
// 选择(预测标签,实际标签),并计算测试误差。
val
evaluator
=
new
MulticlassClassificationEvaluator().setLabelCol(
"indexedLabel"
).setPredictionCol(
"prediction"
).setMetricName(
"accuracy"
)
val
accuracy
=
evaluator.evaluate(predictions)
accuracy
:
Double
=
0.6972972972972973
println(
"Test Error = "
+ (
1.0
- accuracy))
Test Error
=
0.3027027027027027
// 这里的stages(2)中的“2”对应pipeline中的“dt”,将model强制转换为DecisionTreeClassificationModel类型
val
treeModel
=
model.stages(
2
).asInstanceOf[DecisionTreeClassificationModel]
DecisionTreeClassificationModel (uid
=
dtc
_
b
950
f
91
d
35
f
8
) of depth
5
with
43
nodes
treeModel.getLabelCol
String
=
indexedLabel
treeModel.getFeaturesCol
String
=
indexedFeatures
treeModel.featureImportances
Vector
=
(
8
,[
0
,
1
,
2
,
4
,
5
,
6
,
7
],[
0.012972759843658999
,
0.1075317063921102
,
0.11654682273543511
,
0.17869552275855793
,
0.07532637852021348
,
0.27109893303920024
,
0.237827
876710824
])
treeModel.getPredictionCol
String
=
prediction
treeModel.getProbabilityCol
String
=
probability
treeModel.numClasses
Int
=
2
treeModel.numFeatures
Int
=
8
treeModel.depth
Int
=
5
treeModel.numNodes
Int
=
43
treeModel.getImpurity
String
=
entropy
treeModel.getMaxBins
Int
=
100
treeModel.getMaxDepth
Int
=
5
treeModel.getMaxMemoryInMB
Int
=
256
treeModel.getMinInfoGain
Double
=
0.01
treeModel.getMinInstancesPerNode
Int
=
10
// 查看决策树
println(
"Learned classification tree model:\n"
+ treeModel.toDebugString)
Learned classification tree model
:
DecisionTreeClassificationModel (uid
=
dtc
_
b
950
f
91
d
35
f
8
) of depth
5
with
43
nodes
// 例如“feature 7 in {0.0,1.0,2.0}”中的“{0.0,1.0,2.0}”
// 具体解释请参考本人博客http://www.cnblogs.com/wwxbi/p/6125493.html“VectorIndexer自动识别分类的特征,并对它们进行索引”
If (feature
7
in {
0.0
,
1.0
,
2.0
})
If (feature
7
in {
0.0
,
2.0
})
If (feature
4
in {
0.0
,
4.0
})
Predict
:
1.0
Else (feature
4
not in {
0.0
,
4.0
})
If (feature
1
<
=
32.0
)
If (feature
1
<
=
27.0
)
Predict
:
0.0
Else (feature
1
>
27.0
)
Predict
:
1.0
Else (feature
1
>
32.0
)
If (feature
5
<
=
16.0
)
Predict
:
0.0
Else (feature
5
>
16.0
)
Predict
:
0.0
Else (feature
7
not in {
0.0
,
2.0
})
If (feature
4
in {
0.0
,
1.0
,
3.0
,
4.0
})
If (feature
0
in {
0.0
})
If (feature
2
<
=
7.0
)
Predict
:
0.0
Else (feature
2
>
7.0
)
Predict
:
0.0
Else (feature
0
not in {
0.0
})
Predict
:
0.0
Else (feature
4
not in {
0.0
,
1.0
,
3.0
,
4.0
})
Predict
:
1.0
Else (feature
7
not in {
0.0
,
1.0
,
2.0
})
If (feature
2
<
=
4.0
)
If (feature
6
<
=
3.0
)
If (feature
6
<
=
1.0
)
Predict
:
0.0
Else (feature
6
>
1.0
)
Predict
:
0.0
Else (feature
6
>
3.0
)
If (feature
5
<
=
16.0
)
If (feature
2
<
=
0.75
)
Predict
:
0.0
Else (feature
2
>
0.75
)
Predict
:
0.0
Else (feature
5
>
16.0
)
If (feature
7
in {
4.0
})
Predict
:
0.0
Else (feature
7
not in {
4.0
})
Predict
:
0.0
Else (feature
2
>
4.0
)
If (feature
6
<
=
3.0
)
If (feature
4
in {
0.0
,
1.0
,
2.0
})
Predict
:
0.0
Else (feature
4
not in {
0.0
,
1.0
,
2.0
})
If (feature
7
in {
4.0
})
Predict
:
0.0
Else (feature
7
not in {
4.0
})
Predict
:
0.0
Else (feature
6
>
3.0
)
If (feature
4
in {
0.0
,
2.0
,
3.0
,
4.0
})
If (feature
6
<
=
4.0
)
Predict
:
0.0
Else (feature
6
>
4.0
)
Predict
:
0.0
Else (feature
4
not in {
0.0
,
2.0
,
3.0
,
4.0
})
If (feature
1
<
=
37.0
)
Predict
:
1.0
Else (feature
1
>
37.0
)
Predict
:
0.0
|