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())
v, z, cv, _ = predict()
state.save_model(v, z, cv)
if public_score == None:
# если есть public score - перезаписывать отправленное уже не стоит
state.save_predicts(z)
else:
import os
if os.path.exists('../model_scores.csv'):
mdf = pd.read_csv('../model_scores.csv')
else:
mdf = pd.DataFrame(columns=['timestamp', 'model', 'cv', 'cv std', 'public score'])
idx = mdf.model == state.base_name_
if np.sum(idx) == 0:
mdf.loc[len(mdf), 'model'] = state.base_name_
idx = mdf.model == state.base_name_
if (mdf.ix[idx, 'public score'] != public_score).bool():
mdf.ix[idx, 'public score'] = public_score
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())
v, z, cv, _ = predict()
if not debug_mode:
state.save_model(v, z, cv)
if public_score == None:
# если есть public score - перезаписывать отправленное уже не стоит
state.save_predicts(z)
else:
import os
if os.path.exists('../model_scores.csv'):
mdf = pd.read_csv('../model_scores.csv')
else:
mdf = pd.DataFrame(
columns=['timestamp', 'model', 'cv', 'cv std', 'public score'])
idx = mdf.model == state.base_name_
if np.sum(idx) == 0:
mdf.loc[len(mdf), 'model'] = state.base_name_
idx = mdf.model == state.base_name_
if (mdf.ix[idx, 'public score'] != public_score).bool():