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_namsor_get_method_returns_correct_result(self):
g = DameNamsor()
self.assertEqual(['male', -1.0], g.get("David", "Arroyo",
binary=False))
self.assertEqual(['male', -1.0], g.get("David", "Arroyo", binary=True))
self.assertEqual(['female', 1.0], g.get("Karen", "Arroyo",
binary=True))
def test_dame_namsor_accuracy_score_dame(self):
dn = DameNamsor()
score1 = dn.accuracy_score_dame([1, 1], [1, 1])
self.assertEqual(score1, 1)
score2 = dn.accuracy_score_dame([1, 1, 1, 0], [1, 1, 2, 0])
self.assertEqual(score2, 0.75)
score3 = dn.accuracy_score_dame([1, 1, 1, 1, 2, 1], [1, 1, 1, 1, 2, 1])
self.assertEqual(score3, 1)
def test_dame_namsor_accuracy_score_dame_method_returns_correct_result(
self):
g = DameNamsor()
score1 = g.accuracy_score_dame([1, 1], [1, 1])
self.assertEqual(score1, 1)
score2 = g.accuracy_score_dame([1, 1, 1, 0], [1, 1, 2, 0])
self.assertEqual(score2, 0.75)
score3 = g.accuracy_score_dame([1, 1, 1, 1, 2, 1], [1, 1, 1, 1, 2, 1])
self.assertEqual(score3, 1)
def test_dame_namsor_download(self):
dn = DameNamsor()
du = DameUtils()
path1 = "files/names/min.csv"
if (dn.config['DEFAULT']['namsor'] == 'yes'):
g = dn.download(path1)
self.assertTrue(
os.path.isfile("files/names/namsor" + du.path2file(path1) +
".json"))
def test_dame_namsor_json2guess_list(self):
dn = DameNamsor()
j2gl = dn.json2guess_list(
jsonf="files/names/namsorfiles_names_min.csv.json", binary=False)
self.assertEqual(['male', 'male', 'male', 'male', 'male', 'female'],
j2gl)
j2gl = dn.json2guess_list(
jsonf="files/names/namsorfiles_names_min.csv.json", binary=True)
self.assertEqual([1, 1, 1, 1, 1, 0], j2gl)
def test_dame_namsor_get(self):
dn = DameNamsor()
if (dn.config['DEFAULT']['namsor'] == 'yes'):
l1 = dn.get("David", "Arroyo", binary=False)
self.assertEqual(['male', -1.0], [l1[0], round(l1[1])])
l2 = dn.get("David", "Arroyo", binary=True)
self.assertEqual(['male', -1.0], [l2[0], round(l2[1])])
l3 = dn.get("Karen", "Arroyo", binary=True)
self.assertEqual(['female', 1.0], [l3[0], round(l3[1])])
def test_dame_namsor_gender_list_method_returns_correct_result(self):
g = DameNamsor()
gl = g.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(g.females, 3)
self.assertEqual(g.males, 16)
self.assertEqual(g.unknown, 2)
def test_dame_namsor_gender_list(self):
dn = DameNamsor()
gl = dn.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(dn.females, 3)
self.assertEqual(dn.males, 16)
self.assertEqual(dn.unknown, 2)
def test_dame_namsor_guess_list_method_returns_correct_result(self):
g = DameNamsor()
self.assertEqual([
'male', 'male', 'male', 'male', 'male', 'male', 'female', 'female',
'male', 'male', 'male', 'male', 'male', 'male', 'male', 'male',
'male', 'male', 'female', 'male', 'male'
], g.guess_list(path="files/names/partial.csv", binary=False))
self.assertEqual(
[1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1],
g.guess_list(path="files/names/partial.csv", binary=True))
def test_dame_namsor_guess_list(self):
dn = DameNamsor()
if (dn.config['DEFAULT']['namsor'] == 'yes'):
self.assertEqual([
'male', 'male', 'male', 'male', 'male', 'male', 'female',
'female', 'male', 'male', 'male', 'male', 'male', 'male',
'male', 'male', 'male', 'male', 'female', 'male', 'male'
], dn.guess_list(path="files/names/partial.csv", binary=False))
self.assertEqual([
1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1
], dn.guess_list(path="files/names/partial.csv", binary=True))
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--csv', default="files/names/min.csv")
parser.add_argument('--api', default="all")
parser.add_argument('--ml', default="nltk", choices=['nltk', 'svc', 'sgd', 'gaussianNB', 'multinomialNB', 'bernoulliNB'])
parser.add_argument('--dimensions', default="3x2", choices=['3x2', '3x3'])
args = parser.parse_args()
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)
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)
# 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 == "namsor"):
d = DameNamsor()
print("Namsor 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)
def test_dame_namsor_features_list(self):
dn = DameNamsor()
fl = dn.features_list()
self.assertTrue(len(fl) > 20)
def test_dame_namsor_scale(self):
dn = DameNamsor()
if (dn.config['DEFAULT']['namsor'] == 'yes'):
self.assertEqual(-1.0, round(dn.scale("David", "Arroyo")))
def test_dame_namsor_gender_guess(self):
dn = DameNamsor()
if (dn.config['DEFAULT']['namsor'] == 'yes'):
self.assertEqual(1, dn.guess("David", "Arroyo", binary=True))
self.assertEqual(0, dn.guess("Andrea", "Arroyo", binary=True))
self.assertEqual(1, dn.guess("Asdf", "qwer", binary=True))
parser.add_argument('--csv', type=str, required=True, help="files/names/min.csv")
parser.add_argument('--jsondownloaded', default="", help="files/names/genderapifiles_names_min.csv.json")
parser.add_argument('--api', required=True, choices=['namsor', 'genderize', 'genderapi', 'genderguesser', 'damegender', 'nameapi', 'all'])
#parser.add_argument('--ml', required=True, choices=['nltk', 'svc', 'sgd', 'gaussianNB', 'multinomialNB', 'bernoulliNB', 'forest', 'tree', 'mlp', 'adaboost'])
parser.add_argument('--reverse', default=False, action="store_true")
parser.add_argument('--dimensions', default="2x3", choices=['1x1', '1x2', '1x3', '2x1', '2x2', '2x3', '3x1', '3x2', '3x3'])
args = parser.parse_args()
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"):
dg = Gender()
if (dg.config['DEFAULT']['namsor'] == 'yes'):
dn = DameNamsor()
dn.pretty_cm(path=args.csv, jsonf=args.jsondownloaded, reverse=args.reverse, dimensions=args.dimensions, api=args.api.title())
if (dg.config['DEFAULT']['genderize'] == 'yes'):
dg = DameGenderize()
dg.pretty_cm(path=args.csv, jsonf=args.jsondownloaded, reverse=args.reverse, dimensions=args.dimensions, api=args.api.title())
# dg.print_confusion_matrix_gender(path=args.csv, dimensions=args.dimensions)
if (dg.config['DEFAULT']['genderapi'] == 'yes'):
dga = DameGenderApi()
dga.pretty_cm(path=args.csv, jsonf=args.jsondownloaded, reverse=args.reverse, dimensions=args.dimensions, api=args.api.title())
dgg = DameGenderGuesser()
dgg.print_confusion_matrix_gender(path=args.csv, dimensions=args.dimensions)
ds = DameSexmachine()
def test_dame_namsor_getGeo(self):
dn = DameNamsor()
if (dn.config['DEFAULT']['namsor'] == 'yes'):
l1 = dn.get("David", "Arroyo", binary=False)
self.assertEqual(['male', -1.0], [l1[0], round(l1[1])])
def test_dame_namsor_json2names(self):
dn = DameNamsor()
l = dn.json2names(jsonf="files/names/namsorfiles_names_min.csv.json")
self.assertEqual(
['Pierre', 'Raul', 'Adriano', 'Ralf', 'Guillermo', 'Sabina'], l)
def test_dame_namsor_init(self):
g = DameNamsor()
self.assertEqual(g.males, 0)
self.assertEqual(g.females, 0)
self.assertEqual(g.unknown, 0)
def test_dame_namsor_init_method_returns_correct_result(self):
g = DameNamsor()
self.assertEqual(g.males, 0)
self.assertEqual(g.females, 0)
self.assertEqual(g.unknown, 0)
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'):
dn = DameNamsor()
if (dn.config['DEFAULT']['namsor'] == 'yes'):
text1 = dn.download(path=args.csv)
else:
print("You must enable namsor in config.cfg")
elif (args.api == 'nameapi'):
dna = DameNameapi()
if (dna.config['DEFAULT']['nameapi'] == 'yes'):
text1 = dna.download(path=args.csv)
else:
print("You must enable nameapi in config.cfg")
choices=['accuracy', 'precision', 'recall', 'f1score'])
parser.add_argument('--api',
required=True,
choices=[
'customsearch', 'namsor', 'genderize', 'genderguesser',
'damegender', 'genderapi', 'nameapi', 'all'
])
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,
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()
average = da.average(args.name, args.surname)
print("average: " + str(average))
def test_dame_namsor_scale_method_returns_correct_result(self):
g = DameNamsor()
self.assertEqual(-1.0, g.scale("David", "Arroyo"))
def test_dame_namsor_gender_guess_method_returns_correct_result(self):
g = DameNamsor()
self.assertEqual(1, g.guess("David", "Arroyo", binary=True))
self.assertEqual(0, g.guess("Andrea", "Arroyo", binary=True))
self.assertEqual(0, g.guess("Asdf", "qwer", binary=True))
def test_dame_namsor_features_list_method_returns_correct_result(self):
g = DameNamsor()
fl = g.features_list()
self.assertTrue(len(fl) > 20)
from app.dame_genderize import DameGenderize
from app.dame_genderguesser import DameGenderGuesser
from app.dame_genderapi import DameGenderApi
from app.dame_nameapi import DameNameapi
from app.dame_customsearch import DameCustomsearch
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--csv', default="files/names/min.csv")
parser.add_argument('--api', default="damegender", choices=['customsearch', 'namsor', 'genderize', 'genderguesser', 'damegender', 'genderapi', 'nameapi'])
parser.add_argument('--ml', default="nltk", choices=['nltk', 'svc', 'sgd', 'gaussianNB', 'multinomialNB', 'bernoulliNB'])
args = parser.parse_args()
if (args.api == "all"):
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!!")
naCoded = dn.na_coded(gl1, gl2)
print("+ The na coded: %s" % naCoded)
egb = dn.error_gender_bias(gl1, gl2)
print("+ The error gender bias: %s" % egb)
elif (args.api == "nameapi"):
dn = DameNameapi()
print("Nameapi with %s has: " % args.csv)
gl1 = dn.gender_list(path=args.csv)
gl2 = dn.guess_list(path=args.csv, binary=True)
ec = dn.error_coded(gl1, gl2)
print("+ The error code: %s" % ec)
ecwa = dn.error_coded_without_na(gl1, gl2)
print("+ The error code without na: %s" % ecwa)
naCoded = dn.na_coded(gl1, gl2)
print("+ The na coded: %s" % naCoded)
egb = dn.error_gender_bias(gl1, gl2)
print("+ The error gender bias: %s" % egb)
elif (args.api == "namsor"):
dn = DameNamsor()
print("Namsor with %s has: " % args.csv)
gl1 = dn.gender_list(path=args.csv)
gl2 = dn.guess_list(path=args.csv, binary=True)
ec = dn.error_coded(gl1, gl2)
print("+ The error code: %s" % ec)
ecwa = dn.error_coded_without_na(gl1, gl2)
print("+ The error code without na: %s" % ecwa)
naCoded = dn.na_coded(gl1, gl2)
print("+ The na coded: %s" % naCoded)
egb = dn.error_gender_bias(gl1, gl2)
print("+ The error gender bias: %s" % egb)