#
# 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.regression import LinearRegression
# $example off$
from pyspark.sql import SparkSession

if __name__ == "__main__":
    spark = SparkSession\
        .builder\
        .appName("LinearRegressionWithElasticNet")\
        .getOrCreate()

    # $example on$
    # Load training data
    training = spark.read.format("libsvm")\
        .load("data/mllib/sample_linear_regression_data.txt")

    lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)

    # Fit the model
    lrModel = lr.fit(training)

    # Print the coefficients and intercept for linear regression
    print("Coefficients: %s" % str(lrModel.coefficients))
    print("Intercept: %s" % str(lrModel.intercept))

    # Summarize the model over the training set and print out some metrics
    trainingSummary = lrModel.summary
    print("numIterations: %d" % trainingSummary.totalIterations)
    print("objectiveHistory: %s" % str(trainingSummary.objectiveHistory))
    trainingSummary.residuals.show()
    print("RMSE: %f" % trainingSummary.rootMeanSquaredError)
    print("r2: %f" % trainingSummary.r2)
    # $example off$

    spark.stop()
