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_]
import json
from os.path import join, dirname
from watson_developer_cloud import NaturalLanguageClassifierV1 as NaturalLanguageClassifier
natural_language_classifier = NaturalLanguageClassifier(
username='******', password='******')
classifiers = natural_language_classifier.list()
print(json.dumps(classifiers, indent=2))
# create a classifier
# with open('../resources/weather_data_train.csv', 'rb') as training_data:
# print(json.dumps(natural_language_classifier.create(training_data=training_data, name='weather2'), indent=2))
# replace 47C164-nlc-243 with your classifier id
status = natural_language_classifier.status('47C164-nlc-243')
print(json.dumps(status, indent=2))
classes = natural_language_classifier.classify('47C164-nlc-243',
'How hot will it be tomorrow?')
print(json.dumps(classes, indent=2))
import json
from os.path import join, dirname
from watson_developer_cloud import NaturalLanguageClassifierV1 as NaturalLanguageClassifier
natural_language_classifier = NaturalLanguageClassifier(username='******',
password='******')
print(json.dumps(natural_language_classifier.list(), indent=2))
# create a classifier
# with open('../resources/weather_data_train.csv', 'rb') as training_data:
# print(json.dumps(natural_language_classifier.create(training_data=training_data, name='weather2'), indent=2))
# replace 47C164-nlc-243 with your classifier id
status = natural_language_classifier.status('47C164-nlc-243')
print (json.dumps(status, indent=2))
classes = natural_language_classifier.classify('47C164-nlc-243', 'How hot will it be tomorrow?')
print(json.dumps(classes, indent=2))
def classifier_list(url, username, password):
connection = NaturalLanguageClassifier(url=url, username=username, password=password)
return connection.list()["classifiers"]
def classifier_list(url, username, password):
connection = NaturalLanguageClassifier(url=url, username=username, password=password)
return connection.list()["classifiers"]
import json from watson_developer_cloud import NaturalLanguageClassifierV1 as NaturalLanguageClassifier natural_language_classifier = NaturalLanguageClassifier( username='******', password='******') classifiers = natural_language_classifier.list() print(json.dumps(classifiers, indent=2))
class NLClassifier(object):
def __init__(self, username, password, classifier):
# Setup Watson SDK
self.natural_language_classifier = NLC(username=username,
password=password)
# Classifier information
self.classifier = {}
self.classifier['name'] = classifier['name']
self.classifier['training_file'] = classifier['training_file']
c = self.natural_language_classifier.list()
if any(d['name'] == self.classifier['name'] for d in c['classifiers']):
self.classifier['id'] = [
d['classifier_id'] for d in c['classifiers']
if d['name'] == self.classifier['name']
][0]
print 'Found classifier id %s ' % self.classifier['id']
self.classifier[
'status'] = self.natural_language_classifier.status(
self.classifier['id'])['status']
else:
print 'No classifier found, creating new from training set'
self.classifier['id'] = self.create_classifier()
print 'New classifier id: %s ' % self.classifier['id']
### Method to train the Watson Natural Language Classifier
# The training set is delivered as a CSV file as specified in the Developer Guide
# https://www.ibm.com/watson/developercloud/doc/nl-classifier/data_format.shtml
def create_classifier(self):
training_data = open(self.classifier['training_file'], 'rb')
training_result = self.natural_language_classifier.create(
training_data=training_data, name=self.classifier['name'])
if training_result['status'] == "Training":
self.classifier['status'] = "Training"
return training_result['classifier_id']
else:
print training_result
return "Error"
def classify(self, text):
# Typically in a production system Watson NLC will be fully trained and verified by a data scientist before the system is ever
# exposed in production. However because this is a demo application where Watson NLC is trained at application deployment time,
# we will need to have a check to verify that the training is completed.
if self.classifier['status'] == "Training":
r = self.natural_language_classifier.status(self.classifier['id'])
if r['status'] == "Training":
return {
"error":
"Classifier still in training. Please try again in a few minutes."
}
elif r['status'] == "Available":
self.classifier['status'] = 'Available'
else:
return {
"error": "Unknown status for classifier",
"message": r['status']
}
return self.natural_language_classifier.classify(
self.classifier['id'], text)
import json
from os.path import join, dirname
from watson_developer_cloud import NaturalLanguageClassifierV1 as NaturalLanguageClassifier
natural_language_classifier = NaturalLanguageClassifier(
username='******', password='******')
print(json.dumps(natural_language_classifier.list(), indent=2))
# create a classifier
# with open('../resources/weather_data_train.csv', 'rb') as training_data:
# print(json.dumps(natural_language_classifier.create(training_data=training_data, name='weather2'), indent=2))
# replace 47C164-nlc-243 with your classifier id
status = natural_language_classifier.status('47C164-nlc-243')
print(json.dumps(status, indent=2))
classes = natural_language_classifier.classify('47C164-nlc-243',
'How hot will it be tomorrow?')
print(json.dumps(classes, indent=2))
