decision tree learning algorithm for classification

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1# Decision tree learning algorithm for classification
2
3from pyspark.ml.linalg import Vectors
4from pyspark.ml.feature import StringIndexer
5df = spark.createDataFrame([
6  (1.0, Vectors.dense(1.0)),
7  (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
8stringIndexer = StringIndexer(inputCol="label", outputCol="indexed")
9si_model = stringIndexer.fit(df)
10td = si_model.transform(df)
11dt = DecisionTreeClassifier(maxDepth=2, labelCol="indexed")
12model = dt.fit(td)
13model.numNodes
14# 3
15model.depth
16# 1
17model.featuresImportances
18# SparseVector(1, {0: 1.0})
19model.numFeatures
20# 1
21model.numClasses
22# 2
23print(model.toDebugString)
24# DecisionTreeClassificationModel (uid=...) of depth 1 with 3 nodes...
25test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
26result = model.transform(test0).head()
27result.prediction
28# 0.0
29result.probability
30# DenseVectors([1.0, 0.0])
31result.rawPrediction
32# DenseVector([1.0, 0.0])
33test1 = spark.createDataFrame([Vectors.sparse(1, [0], [1.0]),)], ["features"])
34model.transform(test1).head().prediction
35# 1.0
36
37dtc_path = temp_path + "/dtc"
38dt.save(dtc_path)
39dt2 = DecisionTreeClassifier.load(dtc_path)
40dt2.getMaxDepth()
41# 2
42model_path = temp_path + "/dtc_model"
43model.save(model_path)
44model2 = DecisionTreeClassificationModel.load(model_path)
45model.featureImportances == model2.featureImportances
46# True

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