| # |
| # Licensed to the Apache Software Foundation (ASF) under one or more |
| # contributor license agreements. See the NOTICE file distributed with |
| # this work for additional information regarding copyright ownership. |
| # The ASF licenses this file to You under the Apache License, Version 2.0 |
| # (the "License"); you may not use this file except in compliance with |
| # the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # |
| |
| # $example on$ |
| from pyspark.ml.classification import LogisticRegression |
| # $example off$ |
| from pyspark.sql import SparkSession |
| |
| if __name__ == "__main__": |
| spark = SparkSession\ |
| .builder\ |
| .appName("LogisticRegressionWithElasticNet")\ |
| .getOrCreate() |
| |
| # $example on$ |
| # Load training data |
| training = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") |
| |
| lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8) |
| |
| # Fit the model |
| lrModel = lr.fit(training) |
| |
| # Print the coefficients and intercept for logistic regression |
| print("Coefficients: " + str(lrModel.coefficients)) |
| print("Intercept: " + str(lrModel.intercept)) |
| |
| # We can also use the multinomial family for binary classification |
| mlr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8, family="multinomial") |
| |
| # Fit the model |
| mlrModel = mlr.fit(training) |
| |
| # Print the coefficients and intercepts for logistic regression with multinomial family |
| print("Multinomial coefficients: " + str(mlrModel.coefficientMatrix)) |
| print("Multinomial intercepts: " + str(mlrModel.interceptVector)) |
| # $example off$ |
| |
| spark.stop() |