本文整理汇总了Python中pyspark.ml.evaluation.MulticlassClassificationEvaluator.getMetricName方法的典型用法代码示例。如果您正苦于以下问题：Python MulticlassClassificationEvaluator.getMetricName方法的具体用法？Python MulticlassClassificationEvaluator.getMetricName怎么用？Python MulticlassClassificationEvaluator.getMetricName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.evaluation.MulticlassClassificationEvaluator的用法示例。
在下文中一共展示了MulticlassClassificationEvaluator.getMetricName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: BinaryClassificationEvaluator
# 需要导入模块: from pyspark.ml.evaluation import MulticlassClassificationEvaluator [as 别名]
# 或者: from pyspark.ml.evaluation.MulticlassClassificationEvaluator import getMetricName [as 别名]
# check predictions
predictions.take(10)
from pyspark.ml.evaluation import BinaryClassificationEvaluator
# Print intercept and coefficients
print 'Model Intercept: ', lrModel.intercept
print 'Model weights: ', lrModel.coefficients
# Evaluate model
evaluator = BinaryClassificationEvaluator(rawPredictionCol="rawPrediction")
evaluator.evaluate(predictions)
# AUC
evaluator.getMetricName()
evaluator.evaluate(predictions)
# Other metrics
trainingSummary = lrModel.summary
trainingSummary.roc.show()
print("areaUnderROC: " + str(trainingSummary.areaUnderROC))
#### Bonus: Visualisation example
# Simple visualisations
$ sudo yum install gnuplot
$ sudo /usr/local/bin/pip install gnuplotlib
$ sudo /usr/local/bin/pip install pandas
示例2: print
# 需要导入模块: from pyspark.ml.evaluation import MulticlassClassificationEvaluator [as 别名]
# 或者: from pyspark.ml.evaluation.MulticlassClassificationEvaluator import getMetricName [as 别名]
print("Processing crossvalidation with 3-fold & 200/500 hidden layer units")
crossval = CrossValidator(estimator=pipeline,
estimatorParamMaps=paramGrid_MLP,
evaluator=evaluator,
numFolds=3)
starttime = datetime.datetime.now()
CV_model = crossval.fit(vectorizedData)
print CV_model.bestModel.stages[2]
print('Done on fitting model:%s'%(datetime.datetime.now()-starttime))
print("Transforming testing data...")
vectorized_test_data = testing_data.toDF()
#transformed_data1 = CV_model.transform(vectorizedData)
#print evaluator.getMetricName(), 'accuracy:', evaluator.evaluate(transformed_data1)
transformed_data = CV_model.transform(vectorized_test_data)
#print transformed_data.first()
print("Fitting testing data into model...")
print evaluator.getMetricName(), 'accuracy:', evaluator.evaluate(transformed_data)
predictions = transformed_data.select('indexedLabel', 'prediction')
print predictions.describe().show()
print predictions.take(10)
print predictions.where(predictions.prediction != predictions.indexedLabel)
#predictAndLabel=valid.map(lambda p: (model.predict(p.features),p.label))
#accuracy = 1.0*predictAndLabel.filter(lambda (x, v): x == v).count()/valid.count()
#accuracy
开发者ID:kyoyachuan，项目名称:datascienceProject，代码行数:32，代码来源:imgtrain_parrellal_load_final_version_multilayer.py