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Diffstat (limited to 'examples/src/main/python/ml/generalized_linear_regression_example.py')
-rw-r--r-- | examples/src/main/python/ml/generalized_linear_regression_example.py | 66 |
1 files changed, 66 insertions, 0 deletions
diff --git a/examples/src/main/python/ml/generalized_linear_regression_example.py b/examples/src/main/python/ml/generalized_linear_regression_example.py new file mode 100644 index 0000000000..796752a60f --- /dev/null +++ b/examples/src/main/python/ml/generalized_linear_regression_example.py @@ -0,0 +1,66 @@ +# +# 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. +# + +from __future__ import print_function + +from pyspark.sql import SparkSession +# $example on$ +from pyspark.ml.regression import GeneralizedLinearRegression +# $example off$ + +""" +An example demonstrating generalized linear regression. +Run with: + bin/spark-submit examples/src/main/python/ml/generalized_linear_regression_example.py +""" + +if __name__ == "__main__": + spark = SparkSession\ + .builder\ + .appName("GeneralizedLinearRegressionExample")\ + .getOrCreate() + + # $example on$ + # Load training data + dataset = spark.read.format("libsvm")\ + .load("data/mllib/sample_linear_regression_data.txt") + + glr = GeneralizedLinearRegression(family="gaussian", link="identity", maxIter=10, regParam=0.3) + + # Fit the model + model = glr.fit(dataset) + + # Print the coefficients and intercept for generalized linear regression model + print("Coefficients: " + str(model.coefficients)) + print("Intercept: " + str(model.intercept)) + + # Summarize the model over the training set and print out some metrics + summary = model.summary + print("Coefficient Standard Errors: " + str(summary.coefficientStandardErrors)) + print("T Values: " + str(summary.tValues)) + print("P Values: " + str(summary.pValues)) + print("Dispersion: " + str(summary.dispersion)) + print("Null Deviance: " + str(summary.nullDeviance)) + print("Residual Degree Of Freedom Null: " + str(summary.residualDegreeOfFreedomNull)) + print("Deviance: " + str(summary.deviance)) + print("Residual Degree Of Freedom: " + str(summary.residualDegreeOfFreedom)) + print("AIC: " + str(summary.aic)) + print("Deviance Residuals: ") + summary.residuals().show() + # $example off$ + + spark.stop() |