def average(self, name, surname):
r = 0
count = 0
avg = 0
dgg = DameGenderGuesser()
guess1 = int(dgg.guess(name, binary="True"))
if (guess1 != 2):
r = r + guess1
count = count + 1
if (self.config['DEFAULT']['genderapi'] == 'yes'):
dga = DameGenderApi()
guess2 = int(dga.guess(name, binary="True"))
if (guess2 != 2):
r = r + guess2
count = count + 1
if (self.config['DEFAULT']['genderize'] == 'yes'):
dg = DameGenderize()
guess3 = int(dg.guess(name, binary="True"))
if (guess3 != 2):
r = r + guess3
count = count + 1
if (self.config['DEFAULT']['namsor'] == 'yes'):
dn = DameNamsor()
guess4 = int(dn.guess(str(name), str(surname), binary="True"))
if (guess4 != 2):
r = r + guess4
count = count + 1
avg = r / count
return avg
def test_dame_genderize_get(self):
dg = DameGenderize()
if (dg.config['DEFAULT']['genderize'] == 'yes'):
string1 = dg.get("peter")
self.assertEqual(string1, {'probability': 0.99, 'count': 165452, 'name': 'peter', 'gender': 'male'})
string2 = dg.get(name="peter", surname="smith", country_id="US")
self.assertEqual(string2, {'count': 6521, 'country_id': 'US', 'gender': 'male', 'name': 'peter', 'probability': 0.99})
def test_dame_genderize_get2to10(self):
dg = DameGenderize()
if (dg.config['DEFAULT']['genderize'] == 'yes'):
string1 = dg.get2to10(["peter", "lois", "stevie"])
self.assertEqual(string1, [{'count': 165452, 'gender': 'male', 'name': 'peter', 'probability': 0.99}, {'count': 2510, 'gender': 'female', 'name': 'lois', 'probability': 0.58}, {'count': 2568, 'gender': 'male', 'name': 'stevie', 'probability': 0.87}])
string2 = dg.get2to10(["peter", "lois", "stevie", "john", "paul", "mike", "mary", "anna"])
self.assertEqual(string2, [{"name":"peter","gender":"male","probability":0.99,"count":165452},{"name":"lois","gender":"female","probability":0.58,"count":2510},{"name":"stevie","gender":"male","probability":0.87,"count":2568},{"name":"john","gender":"male","probability":0.99,"count":218952},{"name":"paul","gender":"male","probability":0.99,"count":148099},{"name":"mike","gender":"male","probability":0.99,"count":109844},{"name":"mary","gender":"female","probability":0.99,"count":142684},{"name":"anna","gender":"female","probability":0.98,"count":383713}])
def test_dame_genderize_guess_list(self):
dg = DameGenderize()
gl1 = dg.json2guess_list(jsonf="files/names/genderizefiles_names_min.csv.json", binary=True)
self.assertEqual(gl1, [1, 1, 1, 1, 1, 0])
gl2 = dg.json2guess_list(jsonf="files/names/genderizefiles_names_partialnoundefined.csv.json", binary=True)
self.assertEqual(gl2, [1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1])
gl3 = dg.json2guess_list(jsonf="files/names/genderizefiles_names_allnoundefined0.csv.json", binary=True)
self.assertEqual(gl3, [1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
def test_dame_genderize_gender_list(self):
dg = DameGenderize()
gl = dg.gender_list(path="files/names/partial.csv")
self.assertEqual(gl, [1, 1, 1, 1, 2, 1, 0, 0, 1, 1,
2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1])
self.assertEqual(len(gl), 21)
self.assertEqual(dg.females, 3)
self.assertEqual(dg.males, 16)
self.assertEqual(dg.unknown, 2)
def test_dame_genderize_guess_list(self):
dg = DameGenderize()
if (dg.config['DEFAULT']['genderize'] == 'yes'):
self.assertEqual(['male', 'male', 'male', 'male', 'male',
'male', 'female', 'female', 'male', 'male'],
dg.guess_list(path="files/names/partial.csv",
binary=False)[0:10])
self.assertEqual([1, 1, 1, 1, 1, 1, 0, 0, 1, 1],
dg.guess_list(path="files/names/partial.csv",
binary=True)[0:10])
def test_dame_genderize_gender_list_method_returns_correct_result(self):
dg = DameGenderize()
if (dg.config['DEFAULT']['genderize'] == 'yes'):
gl = dg.gender_list()
self.assertEqual(gl, [
1, 1, 1, 1, 2, 1, 0, 0, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1
])
self.assertEqual(len(gl), 21)
self.assertEqual(dg.females, 3)
self.assertEqual(dg.males, 16)
self.assertEqual(dg.unknown, 2)
def test_dame_genderize_guess_file_method_returns_correct_result(self):
dg = DameGenderize()
if (dg.config['DEFAULT']['genderize'] == 'yes'):
self.assertEqual(dg.guess("David", binary=True), 1)
args = parser.parse_args()
du = DameUtils()
if (args.api == "all"):
dg = Gender()
if (dg.config['DEFAULT']['namsor'] == 'yes'):
dn = DameNamsor()
dn.pretty_gg_list(path=args.csv,
jsonf=args.jsondownloaded,
measure=args.measure,
api='Namsor')
if (dg.config['DEFAULT']['genderize'] == 'yes'):
dg = DameGenderize()
dg.pretty_gg_list(path=args.csv,
jsonf=args.jsondownloaded,
measure=args.measure,
api='Genderize')
dgg = DameGenderGuesser()
dgg.pretty_gg_list(path=args.csv,
jsonf=args.jsondownloaded,
measure=args.measure,
api='Genderguesser')
ds = DameSexmachine()
ds.pretty_gg_list(path=args.csv,
jsonf=args.jsondownloaded,
measure=args.measure,
dn = DameNamsor()
print("################### Namsor!!")
gl = dn.gender_list(path=args.csv)
print("Gender list: " + str(gl))
sl = dn.guess_list(path=args.csv, binary=True)
print("Guess list: " +str(sl))
namsor_accuracy = dn.accuracy_score_dame(gl, sl)
#print(dn.accuracy_score_dame([1, 1, 1, 1, 2, 1], [1, 1, 1, 1, 1, 1]))
#namsor_accuracy = dn.accuracy_score_dame([1, 1, 1, 1, 2, 1], [1, 1, 1, 1, 2, 1])
print("Namsor accuracy: %s" % namsor_accuracy)
dg = DameGenderize()
print("################### Genderize!!")
gl = dg.gender_list(path=args.csv)
print("Gender list: " + str(gl))
sl = dg.guess_list(path=args.csv, binary=True)
print("Guess list: " +str(gl))
genderize_accuracy = dg.accuracy_score_dame(gl,sl)
print("Genderize accuracy: %s" % genderize_accuracy)
dgg = DameGenderGuesser()
print("################### GenderGuesser!!")
gl = dgg.gender_list(path=args.csv)
print("Gender list: " + str(gl))
sl = dgg.guess_list(path=args.csv, binary=True)
print("Guess list: " +str(sl))
print("A confusion matrix C is such that Ci,j is equal to the number of observations known to be in group i but predicted to be in group j.")
print("If the classifier is nice, the diagonal is high because there are true positives")
if (args.api == "all"):
dn = DameNamsor()
if (args.dimensions == "3x2"):
print("Namsor confusion matrix:\n")
dn.print_confusion_matrix_dame(path=args.csv)
elif (args.dimensions == "3x3"):
namsor_confusion_matrix = dn.confusion_matrix(path=args.csv)
print("Namsor confusion matrix:\n %s" % namsor_confusion_matrix)
dg = DameGenderize()
if (args.dimensions == "3x2"):
print("Genderize confusion matrix:\n")
dg.print_confusion_matrix_dame(path=args.csv)
elif (args.dimensions == "3x3"):
dg = DameGenderize()
genderize_confusion_matrix = dg.confusion_matrix(path=args.csv)
print("Genderize confusion matrix:\n %s" % genderize_confusion_matrix)
dgg = DameGenderGuesser()
if (args.dimensions == "3x2"):
print("Gender Guesser confusion matrix:\n")
dgg.print_confusion_matrix_dame(path=args.csv)
elif (args.dimensions == "3x3"):
genderguesser_confusion_matrix = dgg.confusion_matrix(path=args.csv)
print("Gender Guesser confusion matrix:\n %s" % genderguesser_confusion_matrix)
parser.add_argument('--csv',
type=str,
required=True,
default="files/names/min.csv",
help='input file for names')
parser.add_argument('--api',
required=True,
choices=['namsor', 'genderize', 'genderapi', 'nameapi'])
parser.add_argument("--surnames",
default=False,
action="store_true",
help="Flag to surnames")
args = parser.parse_args()
if (args.api == 'genderize'):
dg = DameGenderize()
text1 = dg.download(path=args.csv, surnames=args.surnames)
elif (args.api == 'genderapi'):
dga = DameGenderApi()
if (dga.config['DEFAULT']['genderapi'] == 'yes'):
if (dga.apikey_limit_exceeded_p() == False):
text1 = dga.download(path=args.csv)
elif (dga.apikey_count_requests() < len(dga.csv2names(args.csv))):
print("You don't have enough requests with this api key")
elif (dga.apikey_count_requests() >= len(dga.csv2names(args.csv))):
text1 = dga.download(path=args.csv)
else:
print("You have not money with this api key")
else:
print("You must enable genderapi in config.cfg")
elif (args.api == 'namsor'):
ec = d.error_coded(gl1, gl2)
print("+ The error code: %s" % ec)
ecwa = d.error_coded_without_na(gl1, gl2)
print("+ The error code without na: %s" % ecwa)
naCoded = d.na_coded(gl1, gl2)
print("+ The na coded: %s" % naCoded)
egb = d.error_gender_bias(gl1, gl2)
print("+ The error gender bias: %s" % egb)
elif ((args.api != "damegender") and (args.ml in [
'svc', 'sgd', 'gaussianNB', 'multinomialNB', 'bernoulliNB', 'forest',
'xgboost'
])):
print("The machine learning prediction is only for damegender")
elif (args.api == "genderize"):
d = DameGenderize()
print("Genderize with %s has: " % args.csv)
gl1 = d.gender_list(path=args.csv)
if (os.path.isfile(args.jsondownloaded)):
gl2 = d.json2guess_list(jsonf=args.jsondownloaded, binary=True)
else:
gl2 = d.guess_list(path=args.csv, binary=True)
ec = d.error_coded(gl1, gl2)
print("+ The error code: %s" % ec)
ecwa = d.error_coded_without_na(gl1, gl2)
print("+ The error code without na: %s" % ecwa)
naCoded = d.na_coded(gl1, gl2)
print("+ The na coded: %s" % naCoded)
egb = d.error_gender_bias(gl1, gl2)
print("+ The error gender bias: %s" % egb)
elif (args.api == "genderapi"):
parser.add_argument('--version', action='version', version='0.1')
args = parser.parse_args()
du = DameUtils()
if (len(sys.argv) > 1):
if (args.api == "genderguesser"):
dgg = DameGenderGuesser()
print(dgg.guess(args.name))
elif (args.api == "genderapi"):
dga = DameGenderApi()
print(dga.guess(args.name, binary=False))
print("accuracy: " + str(dga.accuracy(args.name)))
elif (args.api == "genderize"):
dg = DameGenderize()
print(dg.guess(args.name))
print("probability: " + str(dg.prob(args.name)))
elif (args.api == "namsor"):
dn = DameNamsor()
if (du.is_not_blank(args.surname)):
print(dn.guess(str(args.name), str(args.surname)))
print("scale: " + str(dn.scale(str(args.name), str(args.surname))))
else:
print("Surname is required in namsor api")
elif (args.api == "nameapi"):
dn = DameNameapi()
print(dn.guess(str(args.name), str(args.surname)))
print("confidence: " + str(dn.confidence(str(args.name), str(args.surname))))
elif (args.api == "average"):
da = DameAll()
def test_dame_genderize_json2names(self):
dg = DameGenderize()
l = dg.json2names(jsonf="files/names/genderizefiles_names_min.csv.json")
self.assertEqual(['Pierre', 'Raul', 'Adriano', 'Ralf',
'Guillermo', 'Sabina'], l)
def test_dame_genderize_prob(self):
dg = DameGenderize()
if (dg.config['DEFAULT']['genderize'] == 'yes'):
self.assertEqual(dg.prob("David"), 0.99)
def test_dame_genderize_guess(self):
dg = DameGenderize()
if (dg.config['DEFAULT']['genderize'] == 'yes'):
self.assertEqual(dg.guess("David"), "male")
self.assertEqual(dg.guess("David", binary=True), 1)