本文整理汇总了Python中Classifier.Classifier.trainModel方法的典型用法代码示例。如果您正苦于以下问题：Python Classifier.trainModel方法的具体用法？Python Classifier.trainModel怎么用？Python Classifier.trainModel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Classifier.Classifier的用法示例。
在下文中一共展示了Classifier.trainModel方法的1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: main
# 需要导入模块: from Classifier import Classifier [as 别名]
# 或者: from Classifier.Classifier import trainModel [as 别名]
def main(sc):
start = timer()
#### 1) Recuperando os produtos da base de dados
#categs = ["Computers & Tablets", "Video Games", "TV & Home Theater"]# , ]
stpwrds = stopwords.words('portuguese')
products = findProductsByCategory([])
print '####### Creating product rdd with {} product'.format(len(products))
productRDD = sc.parallelize(products)
#productRDD, discardedProductRDD = entiryProductRDD.randomSplit([2, 8], seed=0L)
#### 2) Criadno o corpus de documento utilizando
corpusRDD = productRDD.map(lambda s: (s[0], word_tokenize(s[1].lower()), s[2], s[3])).map(lambda s: (s[0], [PorterStemmer().stem(x) for x in s[1] if x not in stpwrds], s[2], s[3] )).map(lambda s: (s[0], [x[0] for x in pos_tag(s[1]) if x[1] == 'NN' or x[1] == 'NNP'], s[2], s[3])).cache()
idfsRDD = idfs(corpusRDD)
idfsRDDBroadcast = sc.broadcast(idfsRDD.collectAsMap())
tfidfRDD = corpusRDD.map(lambda x: (x[0], tfidf(x[1], idfsRDDBroadcast.value), x[2], x[3]))
category = productRDD.map(lambda x: x[2]).distinct().collect()
categoryAndSubcategory = productRDD.map(lambda x: (x[2], x[3])).distinct().collect()
tokens = corpusRDD.flatMap(lambda x: x[1]).distinct().collect()
insertTokensAndCategories(tokens, category, categoryAndSubcategory)
classifier = Classifier(sc, 'NaiveBayes')
trainingVectSpaceCategoryRDD, testVectSpaceCategoryRDD = classifier.createVectSpaceCategory(tfidfRDD, category, tokens).randomSplit([8, 2], seed=0L)
modelNaiveBayesCategory = classifier.trainModel(trainingVectSpaceCategoryRDD, '/dados/models/naivebayes/category_new')
predictionAndLabelCategoryRDD = testVectSpaceCategoryRDD.map(lambda p : (category[int(modelNaiveBayesCategory.predict(p.features))], category[int(p.label)]))
acuraccyCategory = float(predictionAndLabelCategoryRDD.filter(lambda (x, v): x[0] == v[0]).count())/float(predictionAndLabelCategoryRDD.count())
print 'the accuracy of the Category Naive Bayes model is %f' % acuraccyCategory
trainingVectSpaceSubcategory, testVectSpaceSubcategory = classifier.createVectSpaceSubcategory(tfidfRDD, categoryAndSubcategory, tokens).randomSplit([8, 2], seed=0L)
modelNaiveBayesSubcategory = classifier.trainModel(trainingVectSpaceSubcategory, '/dados/models/naivebayes/subcategory_new')
predictionAndLabelSubcategory = testVectSpaceSubcategory.map(lambda p : (categoryAndSubcategory[int(modelNaiveBayesSubcategory.predict(p.features))], categoryAndSubcategory[int(p.label)]))
acuraccySubcategory = float(predictionAndLabelSubcategory.filter(lambda (x, v): x[0] == v[0]).count())/float(predictionAndLabelSubcategory.count())
print 'the accuracy of the Subcategory Naive Bayes model is %f' % acuraccySubcategory
#test with DecisionTree Model
classifierDT = Classifier(sc, 'DecisionTree')
trainingVectSpaceCategory, testVectSpaceCategory = classifierDT.createVectSpaceCategory(tfidfRDD, category, tokens).randomSplit([8, 2], seed=0L)
modelDecisionTreeCategory = classifierDT.trainModel(trainingVectSpaceCategory, '/dados/models/dt/category_new')
predictions = modelDecisionTreeCategory.predict(testVectSpaceCategory.map(lambda x: x.features))
predictionAndLabelCategory = testVectSpaceCategory.map(lambda lp: lp.label).zip(predictions)
acuraccyDecisionTree = float(predictionAndLabelCategory.filter(lambda (x, v): x == v).count())/float(predictionAndLabelCategory.count())
print 'the accuracy of the Decision Tree model is %f' % acuraccyDecisionTree
elap = timer()-start
print 'it tooks %d seconds' % elap