本文整理汇总了Python中sklearn.datasets.base.Bunch.bow方法的典型用法代码示例。如果您正苦于以下问题：Python Bunch.bow方法的具体用法？Python Bunch.bow怎么用？Python Bunch.bow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sklearn.datasets.base.Bunch的用法示例。
在下文中一共展示了Bunch.bow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 sklearn.datasets.base import Bunch [as 别名]
# 或者: from sklearn.datasets.base.Bunch import bow [as 别名]
def main():
print args
print
accuracies = defaultdict(lambda: [])
ora_accu = defaultdict(lambda: [])
oracle_accuracies =[]
ora_cm = defaultdict(lambda: [])
lbl_dit = defaultdict(lambda: [])
aucs = defaultdict(lambda: [])
x_axis = defaultdict(lambda: [])
vct = TfidfVectorizer(encoding='ISO-8859-1', min_df=5, max_df=1.0, binary=False, ngram_range=(1, 1),
token_pattern='\\b\\w+\\b', tokenizer=StemTokenizer())
print("Start loading ...")
# data fields: data, bow, file_names, target_names, target
########## NEWS GROUPS ###############
# easy to hard. see "Less is More" paper: http://axon.cs.byu.edu/~martinez/classes/678/Presentations/Clawson.pdf
categories = [['alt.atheism', 'talk.religion.misc'],
['comp.graphics', 'comp.windows.x'],
['comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware'],
['rec.sport.baseball', 'sci.crypt']]
min_size = 10
args.fixk = None
data, vct = load_from_file(args.train, [categories[3]], args.fixk, min_size, vct, raw=True)
print("Data %s" % args.train)
print("Data size %s" % len(data.train.data))
parameters = experiment_utils.parse_parameters_mat(args.cost_model)
print "Cost Parameters %s" % parameters
cost_model = experiment_utils.set_cost_model(args.cost_function, parameters=parameters)
print "\nCost Model: %s" % cost_model.__class__.__name__
### SENTENCE TRANSFORMATION
if args.train == "twitter":
sent_detector = TwitterSentenceTokenizer()
else:
sent_detector = nltk.data.load('tokenizers/punkt/english.pickle')
## delete <br> to "." to recognize as end of sentence
data.train.data = experiment_utils.clean_html(data.train.data)
data.test.data = experiment_utils.clean_html(data.test.data)
print("Train:{}, Test:{}, {}".format(len(data.train.data), len(data.test.data), data.test.target.shape[0]))
## Get the features of the sentence dataset
## create splits of data: pool, test, oracle, sentences
expert_data = Bunch()
if not args.fulloracle:
train_test_data = Bunch()
expert_data.sentence, train_test_data.pool = split_data(data.train)
expert_data.oracle, train_test_data.test = split_data(data.test)
data.train.data = train_test_data.pool.train.data
data.train.target = train_test_data.pool.train.target
data.test.data = train_test_data.test.train.data
data.test.target = train_test_data.test.train.target
## convert document to matrix
data.train.bow = vct.fit_transform(data.train.data)
data.test.bow = vct.transform(data.test.data)
#### EXPERT CLASSIFIER: ORACLE
print("Training Oracle expert")
exp_clf = experiment_utils.set_classifier(args.classifier, parameter=args.expert_penalty)
if not args.fulloracle:
print "Training expert documents:%s" % len(expert_data.oracle.train.data)
labels, sent_train = experiment_utils.split_data_sentences(expert_data.oracle.train, sent_detector, vct, limit=args.limit)
expert_data.oracle.train.data = sent_train
expert_data.oracle.train.target = np.array(labels)
expert_data.oracle.train.bow = vct.transform(expert_data.oracle.train.data)
exp_clf.fit(expert_data.oracle.train.bow, expert_data.oracle.train.target)
else:
# expert_data.data = np.concatenate((data.train.data, data.test.data))
# expert_data.target = np.concatenate((data.train.target, data.test.target))
expert_data.data =data.train.data
expert_data.target = data.train.target
expert_data.target_names = data.train.target_names
labels, sent_train = experiment_utils.split_data_sentences(expert_data, sent_detector, vct, limit=args.limit)
expert_data.bow = vct.transform(sent_train)
expert_data.target = labels
expert_data.data = sent_train
exp_clf.fit(expert_data.bow, expert_data.target)
#.........这里部分代码省略.........