def gen():
sys_path = sys.path[:]
if len(sys.argv) < 3:
raise LookupError('Please specify model result to transfer.')
elif len(sys.argv) > 3:
raise UserWarning('Only one model result can be transfered.')
model_path = os.path.join(current_dir, sys.argv[2])
sys.path.append(model_path)
import pred
pred_result, _ = get_pred(pred.get_model(), all_data, prep.date(8, 15))
pred_stats = stats(pred_result, all_data)
print('| TOTAL VISITED BOUGHT FAVO CART NEW')
print('| Pred # {:<8}{:<8}{:<8}{:<8}{:<8}{:<8}'.format(*pred_stats))
print('| % {:<8.0%}{:<8.3%}{:<8.3%}{:<8.3%}{:<8.3%}{:<8.3%}'.format(*[i*1./pred_stats[0] for i in pred_stats]))
from datetime import date
target_path = os.path.join(current_dir, 'result%02d.txt'%date.today().day)
output(target_path, pred_result)
sys.path = sys_path
def gen():
sys_path = sys.path[:]
if len(sys.argv) < 3:
raise LookupError('Please specify model result to transfer.')
elif len(sys.argv) > 3:
raise UserWarning('Only one model result can be transfered.')
model_path = os.path.join(current_dir, sys.argv[2])
sys.path.append(model_path)
import pred
pred_result, _ = get_pred(pred.get_model(), all_data, prep.date(8, 15))
pred_stats = stats(pred_result, all_data)
print('| TOTAL VISITED BOUGHT FAVO CART NEW')
print('| Pred # {:<8}{:<8}{:<8}{:<8}{:<8}{:<8}'.format(*pred_stats))
print('| % {:<8.0%}{:<8.3%}{:<8.3%}{:<8.3%}{:<8.3%}{:<8.3%}'.format(
*[i * 1. / pred_stats[0] for i in pred_stats]))
from datetime import date
target_path = os.path.join(current_dir,
'result%02d.txt' % date.today().day)
output(target_path, pred_result)
sys.path = sys_path
def on_press_button(self, instance):
import pred
model = pred.get_model('mobclass.pkl')
data = pred.get_value(
self.bataryInput, self.blueInput, self.csInput, self.dsInput,
self.fcInput, self.four_gInput, self.int_memoryInput,
self.m_depInput, self.mobile_wtInput, self.n_coresInput,
self.pcInput, self.px_heightInput, self.px_widthInput,
self.ramInput, self.sc_hInput, self.sc_wInput, self.talk_timeInput,
self.three_gInput, self.touch_screenInput, self.wifiInput)
# predict model
predictVal = pred.get_predict(model, data)
if (predictVal == 0):
predictVal = "Düşük Fiyatlı"
if (predictVal == 1):
predictVal = "Orta Fiyatlı"
if (predictVal == 2):
predictVal = "Yüksek Fiyatlı"
if (predictVal == 3):
predictVal = "Çok Yüksek Fiyatlı"
# if you want follow value uncomment this rows
#print(predictVal)
phone.popUp(self=None, value=predictVal)
def val():
if len(sys.argv) < 3:
raise LookupError('Please specify model to val.')
val_cases = [
(prep.date(5, 18), prep.date(6, 17), 'predicts 5/18 to 6/17', '5/18'),
(prep.date(6, 17), prep.date(7, 17), 'predicts 6/17 to 7/17', '6/17'),
(prep.date(7, 17), prep.date(8, 16), 'predicts 7/17 to 8/16', '7/17'),
]
for model_name in sys.argv[2:]:
sys_path = sys.path[:]
model_path = os.path.join(current_dir, model_name)
sys.path.append(model_path)
import pred
reload(pred)
print_model_header(model_name)
Ps = []
Rs = []
Fs = []
for TRAIN_DATE, VAL_DATE, DESC, _ in val_cases:
train_data = all_data[all_data[:, 3] < TRAIN_DATE]
val_data = all_data[np.logical_and(all_data[:, 3] >= TRAIN_DATE,
all_data[:, 3] < VAL_DATE)]
all_userids = np.unique(train_data[:, 0])
pred_result, raw = get_pred(pred.get_model(), train_data,
TRAIN_DATE - 1)
val_result = get_val(val_data, all_userids)
p, r, f, pred_ratings, hit_ratings = f1(pred_result, val_result,
all_userids, raw)
Ps.append(p)
Rs.append(r)
Fs.append(f)
print_result_header(DESC)
print_result_stats(stats(pred_result, train_data),
stats(val_result, train_data), p)
print_basic_result(p, r, f, pred_ratings, hit_ratings)
plot_result(model_name, val_cases, Ps, Rs, Fs)
sys.path = sys_path
def val():
if len(sys.argv) < 3:
raise LookupError('Please specify model to val.')
val_cases = [
(prep.date(5, 18), prep.date(6, 17), 'predicts 5/18 to 6/17', '5/18'),
(prep.date(6, 17), prep.date(7, 17), 'predicts 6/17 to 7/17', '6/17'),
(prep.date(7, 17), prep.date(8, 16), 'predicts 7/17 to 8/16', '7/17'),
]
for model_name in sys.argv[2:]:
sys_path = sys.path[:]
model_path = os.path.join(current_dir, model_name)
sys.path.append(model_path)
import pred
reload(pred)
print_model_header(model_name)
Ps = []
Rs = []
Fs = []
for TRAIN_DATE, VAL_DATE, DESC, _ in val_cases:
train_data = all_data[all_data[:, 3] < TRAIN_DATE]
val_data = all_data[np.logical_and(
all_data[:, 3] >= TRAIN_DATE,
all_data[:, 3] < VAL_DATE
)]
all_userids = np.unique(train_data[:, 0])
pred_result, raw = get_pred(pred.get_model(), train_data, TRAIN_DATE-1)
val_result = get_val(val_data, all_userids)
p, r, f, pred_ratings, hit_ratings = f1(pred_result, val_result, all_userids, raw)
Ps.append(p)
Rs.append(r)
Fs.append(f)
print_result_header(DESC)
print_result_stats(stats(pred_result, train_data),
stats(val_result, train_data), p)
print_basic_result(p, r, f, pred_ratings, hit_ratings)
plot_result(model_name, val_cases, Ps, Rs, Fs)
sys.path = sys_path