def predict():
saved = state.load('model')
#saved = None
if debug_mode:
saved = None
if saved == None:
train, y, test, _ = data.get()
ftrain, ftest, _ = fea_1.get()
ftrain2, ftest2, _ = fea_2.get()
train = pd.concat([train, ftrain, ftrain2], axis=1)
test = pd.concat([test, ftest, ftest2], axis=1)
print(train.shape, test.shape)
z = pd.DataFrame()
z['id'] = test.id
z['y'] = 0
v = pd.DataFrame()
v['id'] = train.id
v['y'] = y
cv, _ = run(train, y, test, v, z)
state.save('model', (v, z, cv, None))
else:
v, z, cv, _ = saved
return v, z, cv, _
if '__main__' == __name__:
print('starting', state.now())
state.run_predict(predict, debug_mode, public_score)
print('done.', state.now())
saved = state.load('model')
#saved = None
if debug_mode:
saved = None
if saved == None:
train, y, test, _ = data.get()
ftrain, ftest, _ = fea_1.get()
ftrain2, ftest2, _ = fea_2.get()
train = pd.concat([train, ftrain, ftrain2], axis=1)
test = pd.concat([test, ftest, ftest2], axis=1)
print(train.shape, test.shape)
z = pd.DataFrame()
z['id'] = test.id
z['y'] = 0
v = pd.DataFrame()
v['id'] = train.id
v['y'] = y
cv, _ = run(train, y, test, v, z)
state.save('model', (v, z, cv, None))
else:
v, z, cv, _ = saved
return v, z, cv, _
if '__main__' == __name__:
print('starting', state.now())
state.run_predict(predict, debug_mode, public_score)
print('done.', state.now())