Gradient-Boosted Trees (GBTs) learning algorithm for regression
# Gradient-Boosted Trees (GBTs) learning algorithm for regression
from numpy import allclose
from pyspark.ml.linalg import Vectors
df = spark.createDataFrame([
(1.0, Vectors.dense(1.0)),
(0.0, Vectors.sparse(1, [], []))], ["label", "features"])
gbt = GBTRegressor(maxIter=5, maxDepth=2, seed=42)
print(gbt.getImpurity())
# variance
model = gbt.fit(df)
model.featureImportances
# SparseVector(1, {0: 1.0})
model.numFeatures
# 1
allclose(model.treeWeights, [1.0, 0.1, 0.1, 0.1, 0.1])
# True
test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
model.transform(test0).head().prediction
# 0.0
test1 = spark.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
model.transform(test1).head().prediction
# 1.0
gbtr_path = temp_path + "gbtr"
gbt.save(gbtr_path)
gbt2 = GBTRegressor.load(gbtr_path)
gbt2.getMaxDepth()
# 2
model_path = temp_path + "gbtr_model"
model.save(model_path)
model2 = GBTRegressionModel.load(model_path)
model.featureImportances == model2.featureImportances
# True
model.treeWeights == model2.treeWeights
# True
model.trees
# [DecisionTreeRegressionModel (uid=...) of depth..., DecisionTreeRegressionModel...]Are there any code examples left?
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Gradient-Boosted Trees (GBTs) learning algorithm for regression
