gradient boosted trees 28gbts 29 learning algorithm for classification

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17 Aug 2016

1# Gradient-Boosted Trees (GBTs) learning algorithm for classification
2
3from numpy import allclose
4from pyspark.ml.linalg import Vectors
5from pyspark.ml.feature import StringIndexer
6df = spark.createDataFrame([
7  (1.0, Vectors.dense(1.0)),
8  (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
9stringIndexer = StringIndexer(inputCol="label", outputCol="indexed")
10si_model = stringIndexer.fit(df)
11td = si_model.transform(df)
12gbt = GBTClassifier(maxIter=5, maxDepth=2, labelCol="indexed", seed=42)
13model = gbt.fit(td)
14model.featuresImportances
15SparseVector(1, {0: 1.0})
16allclose(model.treeWeights, [1.0, 0.1, 0.1, 0.1])
17# True
18test0 = spark.createDataFrame([Vectors.dense(-1.0),)], ["features"])
19model.transform(test0).head().prediction
20# 0.0
21test1 = spark.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
22model.transform(test1).head().prediction
23# 1.0
24model.totalNumNodes
25# 15
26print(model.toDebugString)
27# GBTClassificationModel (uid=...)...with 5 trees...
28gbtc_path = temp_path + "gbtc"
29gbt.save(gbtc_path)
30gbt2 = GBTClassifier.load(gbtc_path)
31gbt2.getMaxDepth()
32# 2
33model_path = temp_path + "gbtc_model"
34model.save(model_path)
35model2 = GBTClassificationModel.load(model_path)
36model.featureImportances == model2.featureImportances
37# True
38model.treeWeights == model2.treeWeights
39# True
40model.trees
41# [DecisionTreeRegressionModel (uid=...) of depth..., DecisionTreeRegressionModel...]

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