本文整理汇总了Python中pyspark.ml.regression.LinearRegression.save方法的典型用法代码示例。如果您正苦于以下问题：Python LinearRegression.save方法的具体用法？Python LinearRegression.save怎么用？Python LinearRegression.save使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pyspark.ml.regression.LinearRegression的用法示例。
在下文中一共展示了LinearRegression.save方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_linear_regression
# 需要导入模块: from pyspark.ml.regression import LinearRegression [as 别名]
# 或者: from pyspark.ml.regression.LinearRegression import save [as 别名]
def test_linear_regression(self):
lr = LinearRegression(maxIter=1)
path = tempfile.mkdtemp()
lr_path = path + "/lr"
lr.save(lr_path)
lr2 = LinearRegression.load(lr_path)
self.assertEqual(lr2.uid, lr2.maxIter.parent,
"Loaded LinearRegression instance uid (%s) did not match Param's uid (%s)"
% (lr2.uid, lr2.maxIter.parent))
self.assertEqual(lr._defaultParamMap[lr.maxIter], lr2._defaultParamMap[lr2.maxIter],
"Loaded LinearRegression instance default params did not match " +
"original defaults")
try:
rmtree(path)
except OSError:
pass示例2: print
# 需要导入模块: from pyspark.ml.regression import LinearRegression [as 别名]
# 或者: from pyspark.ml.regression.LinearRegression import save [as 别名]
lm_transform = lm_model_fit.transform(testDf)
results = lm_transform.select(lm_transform['prediction'], lm_transform['label'])
MSE = results.map(lambda (p,l):(p-l)**2).reduce(lambda x,y:x+y)/results.count()
print("Linear Regression testing Mean Squared Error = " + str(MSE))
res = results.collect()
predsAndLabels = sc.parallelize([i.asDict().values() for i in res])
metrics = RegressionMetrics(predsAndLabels)
print metrics.meanSquaredError
print metrics.rootMeanSquaredError
print metrics.r2
print metrics.explainedVariance
lm_model.save(sc, "LinerRegressionModel")
# LASSO
lasso_model = LinearRegression(featuresCol="features", predictionCol="prediction", maxIter=100, regParam=1.0, elasticNetParam=0.0, tol=1e-6)
lasso_model_fit = lasso_model.fit(trainDf)
lasso_transform = lasso_model_fit.transform(trainDf) #change to a test model
lasso_results = lasso_transform.select(lasso_transform['prediction'], lasso_transform['label'])
lasso_MSE = lasso_results.map(lambda (p,l):(p-l)**2).reduce(lambda x,y:x+y)/results.count()
print("LASSO training Mean Squared Error = " + str(lasso_MSE))
lasso_transform = lasso_model_fit.transform(testDf) #change to a test model
lasso_results = lasso_transform.select(lasso_transform['prediction'], lasso_transform['label'])
lasso_MSE = lasso_results.map(lambda (p,l):(p-l)**2).reduce(lambda x,y:x+y)/results.count()