def get(self, request, *arg, **kwargs):
gp = GenderPredictor()
gp.train_and_test()
context = {
"form": NameForm,
}
return render(request, 'gender_app/home.html',context)
def search():
gp = GenderPredictor()
accuracy = gp.train_and_test(training_percent=0.80) * 100
features = gp.get_most_informative_features(n=10)
name = request.form['query'].lower()
finalgender = gp.classify_name(name)
gender_dict = find_gender(name)
if finalgender[0] == 'Male':
return render_template("male.html")
else:
return render_template("female.html")
def post(self, request, **kwargs):
if 'submit' in request.POST:
name=request.POST['name']
gp = GenderPredictor()
gp.train_and_test()
detected_gender = gp.classify(name)
context = {
"name" : name,
"gender": detected_gender
}
return render(request, 'gender_app/predict_gender.html',context)
from gender_predictor import GenderNames, GenderPredictor, get_predictor_from_file, get_predictor_from_pickle
if __name__ == '__main__':
# Example 1 how use it
print("Example 1 Sami:")
gn = GenderNames()
gn = gn.load_from_files(path_female='data/female.txt',
path_male='data/male.txt')
gp = GenderPredictor(gn, female_label='Female', male_label='Male')
print(gp.get_gender('Sami'))
print()
print("Example 2 Pekka:")
gn = GenderNames()
gn = gn.load_from_files(path_female='data/female.txt',
path_male='data/male.txt')
gp = GenderPredictor(gn, female_label='Female', male_label='Male')
print(gp.get_gender('Pekka'))
print()
col3.append(10)
col3.append(10)
col3.append(12)
col3.append(10)
col3.append(11)
col3.append(12)
col3.append(10)
col3.append(10)
col3.append(9)
col3.append(9)
col3.append(9)
print(len(col3))
data_dict['Grade'] = col3
print(len(data_dict['Grade']))
# Getting the gender
gen = GenderPredictor()
gen.train_and_test()
col4 = [gen.classify(i.split(" ")[0]) for i in col1]
print(col4)
data_dict['Gender'] = col4
df = pd.DataFrame.from_dict(data_dict)
df.head()
df.replace('M', 'male', inplace=True)
df.replace('F', 'female', inplace=True)
df.to_csv("data/m132-student-data.csv")
import pandas as pd
from os import getcwd, listdir
from gender_predictor import GenderPredictor
# Get the Student Data
data_path = getcwd() + "/data"
listdir(data_path)
df = pd.read_csv(f"{data_path}/{listdir(data_path)[0]}")
# Instantiate the Class
gp = GenderPredictor()
# Train the Model
gp.train_and_test()
# Get List of Students' First Names
student_names = [name.split(" ")[0] for name in df['Student Name'].tolist()]
# Get Predictions
gender_preds = [gp.classify(name) for name in student_names]
# Add Gender Column to the Data Frame
df['Gender'] = gender_preds
# Save New Data
df.to_csv(f"{data_path}/{listdir(data_path)[0]}", index=False)
df['Gender']
'ethnicity', 'race'
]
candidate = []
age_attribute = []
name = gender = age = country = race = ethnicity = False
for i in zip(columns, predicted_labels):
if i[0].lower() in sensitive or i[1].lower() in sensitive:
candidate.append(i[0])
if i[0].lower() in [
'name', 'first_name', 'first name'
] or i[1].lower() in ['name', 'first_name', 'first name']:
name = i[0]
if i[0].lower() in ['sex', 'gender'
] or i[1].lower() in ['sex', 'gender']:
gender = i[0]
if i[0].lower() in ['age'] or i[1].lower() in ['age']:
age = i[0]
age_attribute.append(i[0])
if i[0].lower() in ['country'] or i[1].lower() in ['country']:
country = i[0]
if i[0].lower() in ['race'] or i[1].lower() in ['race']:
race = i[0]
if i[0].lower() in ['ethnicity'] or i[1].lower() in ['ethnicity']:
ethnicity = i[0]
return header, predicted_labels, candidate, name, gender, age, country, race, ethnicity, age_attribute
if __name__ == "__main__":
gp = GenderPredictor()
gp.train_and_test()