Question

这里有个有趣的问题-我有GridSearchCV个结果，从grid_search_cv.results_属性中挑选樱桃后，结果如下：

Input: pd.DataFrame(grid_clf_rf.cv_results_).iloc[4966]['params']

Output: {'rf__max_depth': 40, 'rf__max_features': 2, 'rf__n_estimators': 310}

现在，据我所知，Imbalanced Learn包的Pipeline对象是SciKit-Learn的Pipeline的包装，它应在**fit_params方法中接受.fit()参数，如下所示：

clf = BalancedRandomForestClassifier(random_state = random_state, 
                                 n_jobs = n_jobs)

pipeline = Pipeline([('nt', nt), ('rf', clf)])

pipeline.fit(X_train, y_train, **pd.DataFrame(grid_clf_rf.cv_results_).iloc[4966]['params'])

但是，当我执行上面的表达式时，我得到以下结果：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-64-a26424dc8038> in <module>
      4 pipeline = Pipeline([('nt', nt), ('rf', clf)])
      5 
----> 6 pipeline.fit(X_train, y_train, **pd.DataFrame(grid_clf_rf.cv_results_).iloc[4966]['params'])
      7 
      8 print_scores(pipeline, X_train, y_train, X_test, y_test)

/opt/conda/lib/python3.7/site-packages/imblearn/pipeline.py in fit(self, X, y, **fit_params)
    237         Xt, yt, fit_params = self._fit(X, y, **fit_params)
    238         if self._final_estimator is not None:
--> 239             self._final_estimator.fit(Xt, yt, **fit_params)
    240         return self
    241 

TypeError: fit() got an unexpected keyword argument 'max_features'

有什么主意我在做错什么吗？

Answer 1

为什么要向包含.fit()方法的模型构建参数的数据帧中输入数据，它只需要X和y两个参数即可。您需要将模型的参数传递给BalancedRandomForestClassifier构造函数。由于您的参数名称与BalancedRandomForestClassifier所用的参数名称不匹配，因此您需要像这样手动输入

clf = BalancedRandomForestClassifier(max_depth = 40, max_features = 2, n_estimators = 310, random_state = random_state, n_jobs = n_jobs)

希望这会有所帮助！

Answer 2

让我们假设您想到了一组如下所示的超参数

hyper_params=  {'rf__max_depth': 40, 'rf__max_features': 2, 'rf__n_estimators': 310}

如@ Parthasarathy Subburaj所述，这些不是fit_params。我们可以使用.set_params()选项为管道内的分类器设置这些参数

from imblearn.ensemble import BalancedRandomForestClassifier
from sklearn.datasets import make_classification
from imblearn.pipeline import Pipeline

X, y = make_classification(n_samples=1000, n_classes=3,
                           n_informative=4, weights=[0.2, 0.3, 0.5],
                           random_state=0)

clf = BalancedRandomForestClassifier(random_state=0)

pipeline = Pipeline([ ('rf', clf)])

hyper_params=  {'rf__max_depth': 40, 'rf__max_features': 2, 'rf__n_estimators': 310}
pipeline.set_params(**hyper_params)

pipeline.fit(X,y)

#
Pipeline(memory=None,
         steps=[('rf',
                 BalancedRandomForestClassifier(bootstrap=True,
                                                class_weight=None,
                                                criterion='gini', max_depth=40,
                                                max_features=2,
                                                max_leaf_nodes=None,
                                                min_impurity_decrease=0.0,
                                                min_samples_leaf=2,
                                                min_samples_split=2,
                                                min_weight_fraction_leaf=0.0,
                                                n_estimators=310, n_jobs=1,
                                                oob_score=False, random_state=0,
                                                replacement=False,
                                                sampling_strategy='auto',
                                                verbose=0, warm_start=False))],
         verbose=False)

将GridSearchCV结果传递给Imbalanced-Learn的Pipeline对象

2 个答案: