girdsearchcv用于多输出randomforest回归剂

Question

我已经使用 sklearn.ensemble.randomforestregressor 创建了一个MultiOutput RandomForestRegressor。我现在想执行 GridSearchCV 以找到良好的超参数并为每个单独的目标功能输出R^2分数。代码使用如下：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
param_grid = {
    'model__bootstrap': [True],
    'model__max_depth': [8,10,12],
    'model__max_features': [3,4,5],
    'model__min_samples_leaf': [3,4,5],
    'model__min_samples_split': [3, 5, 7],
    'model__n_estimators': [100, 200, 300]
}
model = RandomForestRegressor()
pipe = Pipeline(steps=[
    ('scaler', StandardScaler()),
    ('model', model)])

scorer = make_scorer(r2_score, multioutput='raw_values')
search = GridSearchCV(pipe, param_grid, scoring=scorer)
search.fit(X_train, y_train)
print(f'Best parameter score {ship_type} {target}: {search.best_score_}')

运行此代码时，我明确会收到以下错误。

  File "run_xgb_rf_regressor.py", line 75, in <module>
    model, X = run_regression(ship_types[2], targets)
  File "run_xgb_rf_regressor.py", line 50, in run_regression
    search.fit(X_train, y_train)
  File "/home/lucas/.local/lib/python3.8/site-packages/sklearn/utils/validation.py", line 63, in inner_f
    return f(*args, **kwargs)
  File "/home/lucas/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py", line 841, in fit
    self._run_search(evaluate_candidates)
  File "/home/lucas/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py", line 1296, in _run_search
    evaluate_candidates(ParameterGrid(self.param_grid))
  File "/home/lucas/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py", line 795, in evaluate_candidates
    out = parallel(delayed(_fit_and_score)(clone(base_estimator),
  File "/home/lucas/.local/lib/python3.8/site-packages/joblib/parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "/home/lucas/.local/lib/python3.8/site-packages/joblib/parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "/home/lucas/.local/lib/python3.8/site-packages/joblib/parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "/home/lucas/.local/lib/python3.8/site-packages/joblib/_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "/home/lucas/.local/lib/python3.8/site-packages/joblib/_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "/home/lucas/.local/lib/python3.8/site-packages/joblib/parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "/home/lucas/.local/lib/python3.8/site-packages/joblib/parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "/home/lucas/.local/lib/python3.8/site-packages/sklearn/utils/fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "/home/lucas/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 625, in _fit_and_score
    test_scores = _score(estimator, X_test, y_test, scorer, error_score)
  File "/home/lucas/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 721, in _score
    raise ValueError(error_msg % (scores, type(scores), scorer))
ValueError: scoring must return a number, got [0.57359176 0.54407165 0.40313057 0.32515033 0.346224   0.39513717
 0.34375699] (<class 'numpy.ndarray'>) instead. (scorer=make_scorer(r2_score, multioutput=raw_values))

错误表明我只能使用单个数字值，在我的情况下，这将是所有目标功能的平均R^2分数。是否有人知道我如何使用GridSearchCV，以便我可以输出单个R^2分数？

非常感谢。

Answer 1

我认为我将使用以下选项进行评分参数（来自文档）：

可呼叫的返回字典，其中键是公制名称，值为度量分数；

因此，

def my_scorer(estimator, X, y):
    preds = estimator.predict(X)
    scores = r2_score(y, preds, multioutput='raw_values')
    return {f'r2_y{i}': score for i, score in enumerate(scores)}

在文档中， revit 的文档中，需要通过多项搜索进行更仔细的设置。也许决定“最佳”参数应通过某种平均值来完成，在这种情况下，您可以在自定义得分手中添加另一个条目。

用户指南的其他有用部分：