the learning objective is to minimize the squared error 2c with regularization

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12 Sep 2018

1# The learning objective is to minimize the squared error, with regularization
2
3from pyspark.ml.linalg import Vectors
4df = spark.createDataFrame([
5  (1.0, 2.0, Vectors.dense(1.0)),
6  (0.0, 2.0, Vectors.sparse(1, [], []))], ["label", "weight", "features"])
7lr = LinearRegression(maxIter=5, regParam=0.0, solver="normal", weightCol="weight")
8model = lr.fit(df)
9test0 = spark.createDataFrame([Vectors.dense(-1.0),)], ["features"])
10abs(model.transform(test0).head().prediction - (-1.0)) < 0.001
11# True
12abs(model.coefficients[0] - 1.0) < 0.001
13# True
14abs(model.intercept - 0.0) < 0.001
15# True
16test1 = spark.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
17abs(model.transform(test1).head().prediction - 1.0) < 0.001
18# True
19lr.setParams("vector")
20# Traceback (most recent call last):
21#     ...
22# TypeError: Method setParams forces keyword arguments.
23lr_path = temp_path + "/lr"
24lr.save(lr_path)
25lr2 = LinearRegression.load(lr_path)
26lr2.getMaxIter()
27# 5
28model_path = temp_path + "/lr_model"
29model.save(model_path)
30model2 = LinearRegressionModel.load(model_path)
31model.coefficient[0] == model2.coefficients[0]
32# True
33model.intercept == model2.intercept
34# True
35model.numFeatures
36# 1

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