def gen_profile(vect=None):
'''
Input lifestyle vector return person obj with income, expenditure,
purchase amount by category.
'''
sav_rating = determine_saving_rating()
gender = random.choice(['male', 'female'])
username = gen.generate_name().lower()
if not vect:
if gender == 'male':
vect = gc.generate_test_cases(1)[0]
else:
vect = gc.generate_test_cases(1)[1]
trans_hist = create_transaction_history(vect)
income, saving = determine_inv_sav(trans_hist, sav_rating)
email = gen.generate_email(username)
age = str(random.choice(list(range(16, 81))))
rent = determine_rent()
category = str(predict(vect))
utilities = determine_utility(rent)
Location = random.choice(
['Toronto', 'Calgary', 'Vancover', 'Montreal', 'Edmonton'])
password = "******"
return Person(category, username, gender, password, email, age, income,
saving, rent, utilities, trans_hist)
def train(n):
training_data = gc.generate_test_cases(n)
training_data = np.asarray(training_data)
clusterer = mixture.GaussianMixture(n_components=15)
clusterer.fit(training_data)
joblib.dump(clusterer, "Classification/training_data.pkl")
predict()
def predict(toPredict=None):
"""
Predicting a vector of dimensions 12 to output a value from 0 to 15.
@param toPredict: vector of dimensions 12
"""
if not toPredict:
toPredict = gc.generate_test_cases(1)[0]
toPredict = np.asarray(toPredict)
toPredict = toPredict.reshape(1, -1)
clusterer = joblib.load("Classification/training_data.pkl")
class_ = clusterer.predict(toPredict)
return class_[0]