本文整理汇总了Python中textblob.classifiers.NaiveBayesClassifier方法的典型用法代码示例。如果您正苦于以下问题：Python classifiers.NaiveBayesClassifier方法的具体用法？Python classifiers.NaiveBayesClassifier怎么用？Python classifiers.NaiveBayesClassifier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textblob.classifiers的用法示例。
在下文中一共展示了classifiers.NaiveBayesClassifier方法的5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: create_classifier
# 需要导入模块: from textblob import classifiers [as 别名]
# 或者: from textblob.classifiers import NaiveBayesClassifier [as 别名]
def create_classifier(doc,class_freq):
## iterates through all classes in dataset
for i in class_freq:
## create a copy of dataframe
temp_doc = doc.copy()
## assign class as '-1' to all other classes.
temp_doc.category[temp_doc['category'] != class_freq[i]] = '-1'
temp_doc = temp_doc.values.tolist()
## training classifier on the temp_doc
classifier = NaiveBayesClassifier(temp_doc)
# save the classifier on disk
with open('classifier_'+i+'.pkl', 'wb') as fid:
cPickle.dump(classifier, fid)
## reassign doc with the new reduced dataset
doc = doc[doc['category'] != class_freq[i]].copy()
## to track time taken in creating classifiers 示例2: __init__
# 需要导入模块: from textblob import classifiers [as 别名]
# 或者: from textblob.classifiers import NaiveBayesClassifier [as 别名]
def __init__(self, **kwargs):
super(TimeLogicAdapter, self).__init__(**kwargs)
training_data = [
("what time is it", 1),
("do you know the time", 1),
("do you know what time it is", 1),
("what is the time", 1),
("do you know the time", 0),
("it is time to go to sleep", 0),
("what is your favorite color", 0),
("i had a great time", 0),
("what is", 0)
]
self.classifier = NaiveBayesClassifier(training_data) 示例3: __init__
# 需要导入模块: from textblob import classifiers [as 别名]
# 或者: from textblob.classifiers import NaiveBayesClassifier [as 别名]
def __init__(self, **kwargs):
super(QueryAdapter, self).__init__(**kwargs)
training_file = '%s/../database/%s.json' % (
os.path.dirname(os.path.realpath(inspect.getfile(self.__class__))),
kwargs.get('training_file_for_query')
)
training_database = json.load(open(training_file))['data']
training_data = [(data,int(classe)) for classe in ["0", "1"] for data in training_database[classe]]
self.classifier = NaiveBayesClassifier(training_data)
self.partners = Database('partners_fake', parse_db=True) #default fixed to partners
self.fields = Database('fields') #lexical fields of different features in db
self.clf = self.train_feature_finder(self.fields.db, RandomForestClassifier(n_estimators=20)) 示例4: train
# 需要导入模块: from textblob import classifiers [as 别名]
# 或者: from textblob.classifiers import NaiveBayesClassifier [as 别名]
def train(self):
rows = []
with open('test_data.csv', 'r') as fp:
data = csv.reader(fp)
for row in data:
rows.append((row[0], row[1], ))
self.cl = NaiveBayesClassifier(rows) 示例5: __init__
# 需要导入模块: from textblob import classifiers [as 别名]
# 或者: from textblob.classifiers import NaiveBayesClassifier [as 别名]
def __init__(self):
self.features_set = []
self.application_tag = "application"
self.timezone_tag = "timezone"
self.location_tag = "location"
self.weather_tomorrow_tag = "weather tomorrow"
self.weather_tag = "weather"
self.distance_tag = "distance"
self.current_distance_from_tag = "distance from current loc"
self.elevation_tag = "elevation"
self.system_tag = "system"
self.name_tag = "name"
self.train_hal()
random.shuffle(self.features_set)
split = int(len(self.features_set)) - int(len(self.features_set) / 4)
train_set, test_set = self.features_set[:split], self.features_set[split:]
if os.path.isfile('request_classifier.pickle'):
self.classifier = self.load_classifier() # Load classifier so training does not have to occur on every run
else:
print(len(self.features_set))
print("Training...")
self.classifier = NaiveBayesClassifier(train_set)
print(self.classifier.accuracy(test_set))
print("Done")
self.save_classifier(self.classifier)