pypark lr

from pyspark.ml.linalg import Vectors
from pyspark.ml.classification import LogisticRegression
from pyspark.sql import SparkSession

spark = SparkSession\
    .builder\
    .appName("lr")\
    .getOrCreate()

training = spark.createDataFrame([
    (1.0, Vectors.dense([0.0, 1.1, 0.1])),
    (0.0, Vectors.dense([2.0, 1.0, -1.0])),
    (0.0, Vectors.dense([2.0, 1.3, 1.0])),
    (1.0, Vectors.dense([0.0, 1.2, -0.5]))], ["label", "features"])
# threshold：推断所用阈值。2分类，默认0.5；thresholds：多分类
# family：描述label分布，默认atuo，可选binomial和multinomial
lr = LogisticRegression(featuresCol='features',labelCol='label',predictionCol='prediction',
                        probabilityCol='probability',maxIter=10, regParam=0.01,elasticNetParam=0,tol=1e-6,
                       standardization=True,fitIntercept=True)
# 解释模型参数：这个方法很有用，因为上面LogisticRegression没有参数文档
print("LogisticRegression parameters:\n" + lr.explainParams() + "\n")

model1 = lr.fit(training)
print(model1.extractParamMap())# 提取参数map

# 自定义参数map
paramMap = {lr.maxIter: 20}
paramMap[lr.maxIter] = 30  # Specify 1 Param, overwriting the original maxIter.
paramMap.update({lr.regParam: 0.1, lr.threshold: 0.55})  # Specify multiple Params.
# 合并参数map
paramMap2 = {lr.probabilityCol: "myProbability"}  # Change output column name
paramMapCombined = paramMap.copy()
paramMapCombined.update(paramMap2)

model2 = lr.fit(training, paramMapCombined)# 使用自定义参数

# Prepare test data
test = spark.createDataFrame([
    (1.0, Vectors.dense([-1.0, 1.5, 1.3])),
    (0.0, Vectors.dense([3.0, 2.0, -0.1])),
    (1.0, Vectors.dense([0.0, 2.2, -1.5]))], ["label", "features"])

prediction = model2.transform(test)# 预测
result = prediction.select("features", "label", "myProbability", "prediction") \
    .collect()

for row in result:
    print("features=%s, label=%s -> prob=%s, prediction=%s"
          % (row.features, row.label, row.myProbability, row.prediction))