本文整理匯總了Python中pyspark.ml.regression.LinearRegression.save方法的典型用法代碼示例。如果您正苦於以下問題：Python LinearRegression.save方法的具體用法？Python LinearRegression.save怎麽用？Python LinearRegression.save使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類pyspark.ml.regression.LinearRegression的用法示例。
在下文中一共展示了LinearRegression.save方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_linear_regression
# 需要導入模塊: from pyspark.ml.regression import LinearRegression [as 別名]
# 或者: from pyspark.ml.regression.LinearRegression import save [as 別名]
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
# 需要導入模塊: from pyspark.ml.regression import LinearRegression [as 別名]
# 或者: from pyspark.ml.regression.LinearRegression import save [as 別名]
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()