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26 Jul 2019

1# Gradient-Boosted Trees (GBTs) learning algorithm for regression
2
3from numpy import allclose
4from pyspark.ml.linalg import Vectors
5df = spark.createDataFrame([
6  (1.0, Vectors.dense(1.0)),
7  (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
8gbt = GBTRegressor(maxIter=5, maxDepth=2, seed=42)
9print(gbt.getImpurity())
10# variance
11model = gbt.fit(df)
12model.featureImportances
13# SparseVector(1, {0: 1.0})
14model.numFeatures
15# 1
16allclose(model.treeWeights, [1.0, 0.1, 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
24gbtr_path = temp_path + "gbtr"
25gbt.save(gbtr_path)
26gbt2 = GBTRegressor.load(gbtr_path)
27gbt2.getMaxDepth()
28# 2
29model_path = temp_path + "gbtr_model"
30model.save(model_path)
31model2 = GBTRegressionModel.load(model_path)
32model.featureImportances == model2.featureImportances
33# True
34model.treeWeights == model2.treeWeights
35# True
36model.trees
37# [DecisionTreeRegressionModel (uid=...) of depth..., DecisionTreeRegressionModel...]

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gradient boosted trees 28gbts 29 learning algorithm for regression

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