params = {'task': 'train',
    'boosting_type': 'gbdt',
    'objective': 'multiclass',
    'num_class':3,
    'metric': 'multi_logloss',
    'learning_rate': 0.002296,
    'max_depth': 7,
    'num_leaves': 17,
    'feature_fraction': 0.4,
    'bagging_fraction': 0.6,
    'bagging_freq': 17}

lgb_cv = lgbm.cv(params, d_train, num_boost_round=10000, nfold=3, shuffle=True, stratified=True, verbose_eval=20, early_stopping_rounds=100)

nround = lgb_cv['multi_logloss-mean'].index(np.min(lgb_cv['multi_logloss-mean']))
print(nround)

model = lgbm.train(params, d_train, num_boost_round=nround)

preds = model.predict(test)
print(preds)

[[  7.93856847e-06   9.99989550e-01   2.51164967e-06]
 [  7.26332978e-01   1.65316511e-05   2.73650491e-01]
 [  7.28564308e-01   8.36756769e-06   2.71427325e-01]
 ...,
 [  7.26892634e-01   1.26915179e-05   2.73094674e-01]
 [  5.93217601e-01   2.07172044e-04   4.06575227e-01]
 [  5.91722491e-05   9.99883828e-01   5.69994435e-05]]

predictions = []

for x in preds:
    predictions.append(np.argmax(x))