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0001 # 0002 # Licensed to the Apache Software Foundation (ASF) under one or more 0003 # contributor license agreements. See the NOTICE file distributed with 0004 # this work for additional information regarding copyright ownership. 0005 # The ASF licenses this file to You under the Apache License, Version 2.0 0006 # (the "License"); you may not use this file except in compliance with 0007 # the License. You may obtain a copy of the License at 0008 # 0009 # http://www.apache.org/licenses/LICENSE-2.0 0010 # 0011 # Unless required by applicable law or agreed to in writing, software 0012 # distributed under the License is distributed on an "AS IS" BASIS, 0013 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 0014 # See the License for the specific language governing permissions and 0015 # limitations under the License. 0016 # 0017 0018 from __future__ import print_function 0019 0020 # $example on$ 0021 from pyspark.ml.classification import LogisticRegression 0022 # $example off$ 0023 from pyspark.sql import SparkSession 0024 0025 if __name__ == "__main__": 0026 spark = SparkSession\ 0027 .builder\ 0028 .appName("LogisticRegressionWithElasticNet")\ 0029 .getOrCreate() 0030 0031 # $example on$ 0032 # Load training data 0033 training = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") 0034 0035 lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8) 0036 0037 # Fit the model 0038 lrModel = lr.fit(training) 0039 0040 # Print the coefficients and intercept for logistic regression 0041 print("Coefficients: " + str(lrModel.coefficients)) 0042 print("Intercept: " + str(lrModel.intercept)) 0043 0044 # We can also use the multinomial family for binary classification 0045 mlr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8, family="multinomial") 0046 0047 # Fit the model 0048 mlrModel = mlr.fit(training) 0049 0050 # Print the coefficients and intercepts for logistic regression with multinomial family 0051 print("Multinomial coefficients: " + str(mlrModel.coefficientMatrix)) 0052 print("Multinomial intercepts: " + str(mlrModel.interceptVector)) 0053 # $example off$ 0054 0055 spark.stop()
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