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."