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From yanboliang <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-14979] [ML] [PySpark] Add examples for ...
Date Tue, 10 May 2016 02:31:06 GMT
Github user yanboliang commented on a diff in the pull request:

    https://github.com/apache/spark/pull/12754#discussion_r62604290
  
    --- Diff: examples/src/main/scala/org/apache/spark/examples/ml/GeneralizedLinearRegressionExample.scala
---
    @@ -0,0 +1,69 @@
    +/*
    + * 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.
    + */
    +
    +// scalastyle:off println
    +package org.apache.spark.examples.ml
    +
    +// $example on$
    +import org.apache.spark.ml.regression.GeneralizedLinearRegression
    +// $example off$
    +import org.apache.spark.sql.SparkSession
    +
    +object GeneralizedLinearRegressionExample {
    +
    +  def main(args: Array[String]): Unit = {
    +    val spark = SparkSession
    +      .builder
    +      .appName("GeneralizedLinearRegressionExample")
    +      .getOrCreate()
    +
    +    // $example on$
    +    // Load training data
    +    val training = spark.read.format("libsvm")
    +      .load("data/mllib/sample_linear_regression_data.txt")
    +
    +    val glr = new GeneralizedLinearRegression()
    +      .setFamily("gaussian")
    +      .setLink("identity")
    +      .setMaxIter(10)
    +      .setRegParam(0.3)
    +
    +    // Fit the model
    +    val model = glr.fit(training)
    +
    +    // Print the coefficients and intercept for generalized linear regression model
    +    println(s"Coefficients: ${model.coefficients} Intercept: ${model.intercept}")
    +
    +    // Summarize the model over the training set and print out some metrics
    +    val summary = model.summary
    --- End diff --
    
    We should keep the example succinct, so I think it does not necessary to illustrate a
complete ML pipeline in the algorithm example. 


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