Using GridSearchCV for RandomForestRegressor

Question

我试图将GridSearchCV用于RandomForestRegressor，但总是得到ValueError: Found array with dim 100. Expected 500。考虑这个玩具示例：

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_

这就是我得到的：

Fitting 5 folds for each of 36 candidates, totalling 180 fits
Traceback (most recent call last):
  File "C:UsersabudisDropboxmachine_learningtoy_example.py", line 21, in <module>
    clf.fit(X_train, y_train)
  File "C:UsersabudisAppDataLocalEnthoughtCanopyUserlibsite-packagessklearngrid_search.py", line 596, in fit
    return self._fit(X, y, ParameterGrid(self.param_grid))
  File "C:UsersabudisAppDataLocalEnthoughtCanopyUserlibsite-packagessklearngrid_search.py", line 378, in _fit
    for parameters in parameter_iterable
  File "C:UsersabudisAppDataLocalEnthoughtCanopyUserlibsite-packagessklearnexternalsjoblibparallel.py", line 653, in __call__
    self.dispatch(function, args, kwargs)
  File "C:UsersabudisAppDataLocalEnthoughtCanopyUserlibsite-packagessklearnexternalsjoblibparallel.py", line 400, in dispatch
    job = ImmediateApply(func, args, kwargs)
  File "C:UsersabudisAppDataLocalEnthoughtCanopyUserlibsite-packagessklearnexternalsjoblibparallel.py", line 138, in __init__
    self.results = func(*args, **kwargs)
  File "C:UsersabudisAppDataLocalEnthoughtCanopyUserlibsite-packagessklearncross_validation.py", line 1240, in _fit_and_score
    test_score = _score(estimator, X_test, y_test, scorer)
  File "C:UsersabudisAppDataLocalEnthoughtCanopyUserlibsite-packagessklearncross_validation.py", line 1296, in _score
    score = scorer(estimator, X_test, y_test)
  File "C:UsersabudisAppDataLocalEnthoughtCanopyUserlibsite-packagessklearnmetricsmetrics.py", line 2324, in r2_score
    y_type, y_true, y_pred = _check_reg_targets(y_true, y_pred)
  File "C:UsersabudisAppDataLocalEnthoughtCanopyUserlibsite-packagessklearnmetricsmetrics.py", line 65, in _check_reg_targets
    y_true, y_pred = check_arrays(y_true, y_pred)
  File "C:UsersabudisAppDataLocalEnthoughtCanopyUserlibsite-packagessklearnutilsvalidation.py", line 254, in check_arrays
    % (size, n_samples))
ValueError: Found array with dim 100. Expected 500

出于某种原因，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，我会得到另一个预期值ValueError。

不过，仅使用clf = ensemble.RandomForestRegressor(n_estimators=500, n_jobs=1, verbose=1)训练一个模型效果很好，所以不确定我是否做错了什么，或者某处scikit-learn有错误。

Answer 1

看起来像

一个错误，但在您的情况下，如果您使用RandomForestRegressor自己的记分器（巧合的是 R^2 score），它应该可以工作，而不在GridSearchCV中指定任何评分函数：

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

编辑：正如@jnothman在#4081中提到的，这是真正的问题：

评分不接受指标函数。它接受签名函数（估计器，> X，y_true=无）->浮点分数。您可以使用 scorering='r2' 或 scorering=make_scorer（r2_score）。

Answer 2

您可以在"[https://scikit-learn.org/stable/modules/model_evaluation.html]"中使用所有回归评分

以下是 MSE 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')

对于 r2 使用：

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