| # |
| # 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() |