使用GridSearchCV进行随机森林回归器的参数调优

Question

我正在尝试使用GridSearchCV来进行RandomForestRegressor，但总是会得到ValueError: Found array with dim 100. Expected 500。请看这个玩具例子：

import numpy as np

from sklearn import ensemble
from sklearn.cross_validation import train_test_split
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import r2_score

if __name__ == '__main__':

    X = np.random.rand(1000, 2)
    y = np.random.rand(1000)

    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.5, random_state=1)

    # Set the parameters by cross-validation
    tuned_parameters = {'n_estimators': [500, 700, 1000], 'max_depth': [None, 1, 2, 3], 'min_samples_split': [1, 2, 3]}

    # clf = ensemble.RandomForestRegressor(n_estimators=500, n_jobs=1, verbose=1)
    clf = GridSearchCV(ensemble.RandomForestRegressor(), tuned_parameters, cv=5, scoring=r2_score, n_jobs=-1, verbose=1)
    clf.fit(X_train, y_train)
    print clf.best_estimator_

这是我得到的内容：

Fitting 5 folds for each of 36 candidates, totalling 180 fits
Traceback (most recent call last):
  File "C:\Users\abudis\Dropbox\machine_learning\toy_example.py", line 21, in <module>
    clf.fit(X_train, y_train)
  File "C:\Users\abudis\AppData\Local\Enthought\Canopy\User\lib\site-packages\sklearn\grid_search.py", line 596, in fit
    return self._fit(X, y, ParameterGrid(self.param_grid))
  File "C:\Users\abudis\AppData\Local\Enthought\Canopy\User\lib\site-packages\sklearn\grid_search.py", line 378, in _fit
    for parameters in parameter_iterable
  File "C:\Users\abudis\AppData\Local\Enthought\Canopy\User\lib\site-packages\sklearn\externals\joblib\parallel.py", line 653, in __call__
    self.dispatch(function, args, kwargs)
  File "C:\Users\abudis\AppData\Local\Enthought\Canopy\User\lib\site-packages\sklearn\externals\joblib\parallel.py", line 400, in dispatch
    job = ImmediateApply(func, args, kwargs)
  File "C:\Users\abudis\AppData\Local\Enthought\Canopy\User\lib\site-packages\sklearn\externals\joblib\parallel.py", line 138, in __init__
    self.results = func(*args, **kwargs)
  File "C:\Users\abudis\AppData\Local\Enthought\Canopy\User\lib\site-packages\sklearn\cross_validation.py", line 1240, in _fit_and_score
    test_score = _score(estimator, X_test, y_test, scorer)
  File "C:\Users\abudis\AppData\Local\Enthought\Canopy\User\lib\site-packages\sklearn\cross_validation.py", line 1296, in _score
    score = scorer(estimator, X_test, y_test)
  File "C:\Users\abudis\AppData\Local\Enthought\Canopy\User\lib\site-packages\sklearn\metrics\metrics.py", line 2324, in r2_score
    y_type, y_true, y_pred = _check_reg_targets(y_true, y_pred)
  File "C:\Users\abudis\AppData\Local\Enthought\Canopy\User\lib\site-packages\sklearn\metrics\metrics.py", line 65, in _check_reg_targets
    y_true, y_pred = check_arrays(y_true, y_pred)
  File "C:\Users\abudis\AppData\Local\Enthought\Canopy\User\lib\site-packages\sklearn\utils\validation.py", line 254, in check_arrays
    % (size, n_samples))
ValueError: Found array with dim 100. Expected 500

由于某些原因，GridSearchCV认为n_estimators参数应该等于每个折叠的大小。如果我更改tuned_parameters列表中n_estimators的第一个值，我会得到一个ValueError并且期望值会变成另一个值。

仅使用clf = ensemble.RandomForestRegressor(n_estimators=500, n_jobs=1, verbose=1)训练一个模型可以正常工作，所以我不确定我是否做错了什么或者scikit-learn中是否存在错误。

- abudis

Answer 1

看起来像是一个bug，但在你的情况下，如果你使用RandomForestRegressor自己的评分器（恰好是R ^ 2得分），而不指定GridSearchCV中的任何评分函数，则它应该可以工作。

clf = GridSearchCV(ensemble.RandomForestRegressor(), tuned_parameters, cv=5, 
                   n_jobs=-1, verbose=1)

编辑：如@jnothman在#4081中提到的那样，这是真正的问题：

评分不接受度量函数。它接受签名为(estimator, X, y_true=None) -> float score的函数。您可以使用scoring ='r2'或scoring = make_scorer(r2_score)。

- elyase

---

我已经创建了两个问题#4080和#4081。 - elyase

啊，好的。是的，我指定了评分参数，因为我不知道回归器的默认值是mse还是r2。完全删除它就解决了问题，谢谢！ - abudis

Answer 2

您可以在“[https://scikit-learn.org/stable/modules/model_evaluation.html]”中使用所有回归得分。

以下是均方误差的示例代码：

cv=RepeatedKFold(n_splits=10,n_repeats=3, random_state=100)



pipeRF = Pipeline([('classifier', [RandomForestRegressor()])]) 

param_grid = [{'classifier' : [RandomForestRegressor()],'classifier__n_estimators': [100, 200],'classifier__min_samples_split': [8, 10],'classifier__min_samples_leaf': [3, 4, 5],'classifier__max_depth': [80, 90]}]




clf = GridSearchCV(pipeRF, param_grid = param_grid, cv = cv, n_jobs=-1, scoring='neg_mean_squared_error')

使用r2：

clf = GridSearchCV(pipeRF, param_grid = param_grid, cv = cv, n_jobs=-1, scoring='r2')

- Rosa Aghdam