Pipeline管道和HyperOpt问题

Question

目前，我正在尝试使用SMOTE进行过采样，然后在管道中运行我的XGBClassifier。由于某些原因，我不能让HyperOpt很好地使用管道。

下面两个示例都可以正常运行：

smote = SMOTE(random_state = 42)
model = XGBClassifier(random_state = 42)
pipe = Pipeline([('smote', smote),
('model',model)])

cv = StratifiedKFold(n_splits = 5)

score = cross_val_score(pipe, X_train, y_train, cv=cv, scoring='roc_auc', n_jobs=-1).mean()

print(score)

model = XGBClassifier(random_state = 42)

def objective_pipe(params):
  model.set_params(**params)

  cv = StratifiedKFold(n_splits = 5)

  score = cross_val_score(model, X_train, y_train, cv=cv, scoring='roc_auc', n_jobs=-1).mean()

  return {'loss': -score, 'params':params, 'status':STATUS_OK}

trials = Trials()
best = fmin(fn=objective_pipe, space = params, algo=tpe.suggest, max_evals = 10, trials = trials, rstate=np.random.RandomState(42))

然而，当我将管道放入目标函数中的时候，我最终得到了分数的NaN值。

smote = SMOTE(random_state = 42)
model = XGBClassifier(random_state = 42)
pipe = Pipeline([('smote', smote),
('model',model)])

def objective_pipe(params):
  pipe.set_params(**params)

  cv = StratifiedKFold(n_splits = 5)

  score = cross_val_score(pipe, X_train, y_train, cv=cv, scoring='roc_auc', n_jobs=-1).mean()

  return {'loss': -score, 'params':params, 'status':STATUS_OK}

trials = Trials()
best = fmin(fn=objective_pipe, space = params, algo=tpe.suggest, max_evals = 10, trials = trials, rstate=np.random.RandomState(42))

也许我只是错过了一些非常简单的东西，但并不是真的确定如何解决这个问题。欢迎任何建议/帮助/资源。

Answer 1

我不知道为什么，但我也遇到了类似的问题，通过设置njobs=1解决了这个问题，我想这和SMOTE不能并行运行有关。