def test_relevance(self):
test_data = 'president'
test_query = ['president', "prime minister"]
response = relevance(test_data, test_query)
self.assertTrue(isinstance(response, list))
self.assertTrue(response[0] > 0.5)
self.assertTrue(response[1] > 0.3)
self.assertEqual(len(response), 2)
def test_relevance(self):
test_data = 'president'
test_query = ['president', "prime minister"]
response = relevance(test_data, test_query)
self.assertTrue(isinstance(response, np.ndarray))
self.assertTrue(response[0] > 0.5)
self.assertTrue(response[1] > 0.2)
self.assertEqual(len(response), 2)
def test_batch_relevance(self):
test_data = ['president', 'president']
test_query = ['president', "prime minister"]
response = relevance(test_data, test_query)
self.assertTrue(isinstance(response, list))
self.assertTrue(response[0][0] > 0.5)
self.assertTrue(response[0][1] > 0.3)
self.assertEqual(len(response), 2)
self.assertEqual(len(response[0]), 2)
self.assertEqual(len(response[1]), 2)
def parse(message, number):
store(message, number)
userProf = analyzeUser(number)
if comparePrev(message, number):
return "Message Sent"
else:
ent = entityMatch(message)
if ent == "None":
print "keywords"
print indicoio.keywords(message, version=2)
print "tags"
print indicoio.text_tags(message, threshold = .03)
print "relevance"
print indicoio.relevance("Renowned soccer legend Pele will be visiting...", ["Germany", "relocation", "Food", "Safety", "Family", "Transportation", "clothing"])
else:
"Found Entity, directing there"
ticketCreate(message, number, ent)
def test_batch_relevance(self):
test_data = ['president', 'president']
test_query = ['president', "prime minister"]
response = relevance(test_data, test_query)
self.assertTrue(isinstance(response, np.ndarray))
self.assertTrue(response[0][0] > 0.5)
self.assertTrue(response[0][1] > 0.2)
self.assertEqual(len(response), 2)
self.assertEqual(len(response[0]), 2)
self.assertEqual(len(response[1]), 2)
def job_matcher(job_desc, resume):
return indicoio.relevance(list(summarizer(resume)), analyzer(job_desc))
import indicoio
import operator
indicoio.config.api_key = 'b94312524aff44f47c4cc57b9e56c5e6'
# single example
#print indicoio.keywords("Where do I get food in Lesbos?", version=2)
#returns the words that are deemed most relevant
print indicoio.relevance("Where do I get food in Lesbos?", ["food", "general_food"])
#returns list of proportions representing how relevant the word is to the string
keyword_dictionary = indicoio.text_tags("Where do I get food in Lesbos?")
top_word = max(keyword_dictionary.iteritems(), key=operator.itemgetter(1))[0]
keyword_dictionary.pop(top_word)
second_top_word =
# batch example
#print indicoio.keywords([
# "How do I get water nearby?",
# "Where did my family go?"
#], version=2)
#returns the words that are deemed most relevant
#print indicoio.relevance(["How do I get water nearby?", "Where did my family go?"], ["family"])
#returns list of proportions representing how relevant the word is to the string
#print indicoio.text_tags([
# "The most common form of arrow consists of a shaft with an arrowhead attached to the front end and with fletchings and a nock attached to the other end.",
# "Yoga in Indian traditions, however, is more than physical exercise, it has a meditative and spiritual core."
