From 9da7ceed81b0afce7deb8f39f3a6d565d401a391 Mon Sep 17 00:00:00 2001 From: Yanbo Liang Date: Thu, 5 Nov 2015 09:56:18 -0800 Subject: [SPARK-11473][ML] R-like summary statistics with intercept for OLS via normal equation solver Follow up [SPARK-9836](https://issues.apache.org/jira/browse/SPARK-9836), we should also support summary statistics for ```intercept```. Author: Yanbo Liang Closes #9485 from yanboliang/spark-11473. --- python/pyspark/ml/regression.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) (limited to 'python') diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py index d7b4fd92c3..7648bf1326 100644 --- a/python/pyspark/ml/regression.py +++ b/python/pyspark/ml/regression.py @@ -55,15 +55,15 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction >>> lr = LinearRegression(maxIter=5, regParam=0.0, solver="normal") >>> model = lr.fit(df) >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"]) - >>> model.transform(test0).head().prediction - -1.0 - >>> model.weights - DenseVector([1.0]) - >>> model.intercept - 0.0 + >>> abs(model.transform(test0).head().prediction - (-1.0)) < 0.001 + True + >>> abs(model.coefficients[0] - 1.0) < 0.001 + True + >>> abs(model.intercept - 0.0) < 0.001 + True >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"]) - >>> model.transform(test1).head().prediction - 1.0 + >>> abs(model.transform(test1).head().prediction - 1.0) < 0.001 + True >>> lr.setParams("vector") Traceback (most recent call last): ... -- cgit v1.2.3