class NLC(object):
def __init__(self, credential_file_path=None):
self.__nlc = None
self.__initialize(credential_file_path)
def __initialize(self, credential_file_path):
if not credential_file_path:
credential_file_path = os.path.expanduser(DEFAULT_CREDENTIAL_PATH)
with open(credential_file_path, 'r') as credential_file:
credential = json.load(credential_file)
self.__nlc = NaturalLanguageClassifier(url=credential['url'], username=credential['username'], password=credential['password'])
def create(self, traning_data, name=None, language='en'):
"""
:param traning_data: A csv file or file path representing the traning data
:param name: The optional descriptive name for the classifier
:param language: The language og the input data
:return: A instance object with the classifier_id of the newly created classifier, still in traning
"""
create_result = None
if isinstance(traning_data, file) or isinstance(traning_data, IOBase): # traning_data is file discripter
create_result = self.__nlc.create(traning_data, name=name, language=language)
elif isinstance(traning_data, str): # traning_data is file path
with open(traning_data, newline=None, mode='r', encoding='utf-8') as csv_file:
if is_valid_recode_num(csv_file):
create_result = self.__nlc.create(csv_file, name=name, language=language)
return CreateResult(create_result)
def classifiers(self):
classifiers_raw = self.__nlc.list()
classifiers_ = [Classifier(c) for c in classifiers_raw['classifiers']]
return Classifiers(classifiers_)
def status(self, classifier_id):
return Status(self.__nlc.status(classifier_id))
def classify(self, classifier_id, text):
return ClassifyResult(self.__nlc.classify(classifier_id, text))
def remove(self, classifier_id):
"""
param: classifier_id: Unique identifier for the classifier
retrun: empty dict object
raise: watson_developer_cloud.watson_developer_cloud_service.WatsonException: Not found
"""
return self.__nlc.remove(classifier_id)
def remove_all(self):
classifiers_ = self.classifiers()
return [self.remove(c.classifier_id) for c in classifiers_]
print(str(err))
print(usage())
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
usage()
sys.exit()
elif opt in ("-c", "---classifier_id"):
classifier_id = arg
elif opt == '-d':
DEBUG = True
if not classifier_id:
print('Required argument missing.')
usage()
sys.exit(2)
try:
# create classifiers with the training data
natural_language_classifier = NaturalLanguageClassifier(url=nlcConstants.getUrl(), username=nlcConstants.getUsername(), password=nlcConstants.getPassword())
# Delete the classifier
sys.stdout.write('Deleting the classifier %s ...\n' % classifier_id)
res = natural_language_classifier.remove(classifier_id)
sys.stdout.write('Response: \n%s\n' % json.dumps(res, indent=2))
except Exception as e:
sys.stdout.write(str(e))
exit(1)
def remove_classifiers(url, username, password, classifier_ids):
n = NaturalLanguageClassifier(url=url, username=username, password=password)
for classifier_id in classifier_ids:
n.remove(classifier_id)
def remove_classifiers(url, username, password, classifier_ids):
n = NaturalLanguageClassifier(url=url, username=username, password=password)
for classifier_id in classifier_ids:
n.remove(classifier_id)
import sys
import operator
import requests
import json
import twitter
from watson_developer_cloud import NaturalLanguageClassifierV1 as NaturalLanguageClassifier
#The IBM Bluemix credentials
nlc_username = '******'
nlc_password = '******'
natural_language_classifier = NaturalLanguageClassifier(username=nlc_username,
password=nlc_password)
classifierId = 'e4be4cx148-nlc-16'
classes = natural_language_classifier.remove(classifierId)
print(json.dumps(classes, indent=2))
for opt, arg in opts:
if opt == '-h':
usage()
sys.exit()
elif opt in ("-c", "---classifier_id"):
classifier_id = arg
elif opt == '-d':
DEBUG = True
if not classifier_id:
print('Required argument missing.')
usage()
sys.exit(2)
try:
# create classifiers with the training data
natural_language_classifier = NaturalLanguageClassifier(
url=nlcConstants.getUrl(),
username=nlcConstants.getUsername(),
password=nlcConstants.getPassword())
# Delete the classifier
sys.stdout.write('Deleting the classifier %s ...\n' % classifier_id)
res = natural_language_classifier.remove(classifier_id)
sys.stdout.write('Response: \n%s\n' % json.dumps(res, indent=2))
except Exception as e:
sys.stdout.write(str(e))
exit(1)
