使用scikit-learn在随机森林上进行递归特征消除

Question

我正在尝试使用 scikit-learn 和随机森林分类器进行递归特征消除，并且希望用 OOB ROC 来评分每个在递归过程中创建的特征子集。

但是，当我尝试使用 RFECV 方法时，出现了一个错误，提示 AttributeError: 'RandomForestClassifier' object has no attribute 'coef_'。

随机森林本身没有系数，但它们有基于基尼分数的排名。所以，我在想如何解决这个问题。

请注意，我希望使用一种方法，明确告诉我在我的 pandas 数据框中哪些特征被选入了最佳组合，因为我使用递归特征选择是为了尽量减少输入到最终分类器中的数据量。

以下是一些示例代码：

from sklearn import datasets
import pandas as pd
from pandas import Series
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import RFECV

iris = datasets.load_iris()
x=pd.DataFrame(iris.data, columns=['var1','var2','var3', 'var4'])
y=pd.Series(iris.target, name='target')
rf = RandomForestClassifier(n_estimators=500, min_samples_leaf=5, n_jobs=-1)
rfecv = RFECV(estimator=rf, step=1, cv=10, scoring='ROC', verbose=2)
selector=rfecv.fit(x, y)

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/bbalin/anaconda/lib/python2.7/site-packages/sklearn/feature_selection/rfe.py", line 336, in fit
    ranking_ = rfe.fit(X_train, y_train).ranking_
  File "/Users/bbalin/anaconda/lib/python2.7/site-packages/sklearn/feature_selection/rfe.py", line 148, in fit
    if estimator.coef_.ndim > 1:
AttributeError: 'RandomForestClassifier' object has no attribute 'coef_'

Answer 1

我提交了一个请求，希望能添加一个叫 coef_ 的东西，这样 RandomForestClassifier 就可以和 RFECV 一起使用了。不过这个改动已经完成了。这个改动会在版本 0.17 中发布。

https://github.com/scikit-learn/scikit-learn/issues/4945

如果你想现在就使用这个功能，可以下载最新的开发版本。

Answer 2

这是我的代码，我稍微整理了一下，让它更符合你的任务：

features_to_use = fea_cols #  this is a list of features
# empty dataframe
trim_5_df = DataFrame(columns=features_to_use)
run=1
# this will remove the 5 worst features determined by their feature importance computed by the RF classifier
while len(features_to_use)>6:
    print('number of features:%d' % (len(features_to_use)))
    # build the classifier
    clf = RandomForestClassifier(n_estimators=1000, random_state=0, n_jobs=-1)
    # train the classifier
    clf.fit(train[features_to_use], train['OpenStatusMod'].values)
    print('classifier score: %f\n' % clf.score(train[features_to_use], df['OpenStatusMod'].values))
    # predict the class and print the classification report, f1 micro, f1 macro score
    pred = clf.predict(test[features_to_use])
    print(classification_report(test['OpenStatusMod'].values, pred, target_names=status_labels))
    print('micro score: ')
    print(metrics.precision_recall_fscore_support(test['OpenStatusMod'].values, pred, average='micro'))
    print('macro score:\n')
    print(metrics.precision_recall_fscore_support(test['OpenStatusMod'].values, pred, average='macro'))
    # predict the class probabilities
    probs = clf.predict_proba(test[features_to_use])
    # rescale the priors
    new_probs = kf.cap_and_update_priors(priors, probs, private_priors, 0.001)
    # calculate logloss with the rescaled probabilities
    print('log loss: %f\n' % log_loss(test['OpenStatusMod'].values, new_probs))
    row={}
    if hasattr(clf, "feature_importances_"):
        # sort the features by importance
        sorted_idx = np.argsort(clf.feature_importances_)
        # reverse the order so it is descending
        sorted_idx = sorted_idx[::-1]
        # add to dataframe
        row['num_features'] = len(features_to_use)
        row['features_used'] = ','.join(features_to_use)
        # trim the worst 5
        sorted_idx = sorted_idx[: -5]
        # swap the features list with the trimmed features
        temp = features_to_use
        features_to_use=[]
        for feat in sorted_idx:
            features_to_use.append(temp[feat])
        # add the logloss performance
        row['logloss']=[log_loss(test['OpenStatusMod'].values, new_probs)]
    print('')
    # add the row to the dataframe
    trim_5_df = trim_5_df.append(DataFrame(row))
run +=1

我在这里做的事情是，我有一个特征列表，想用这些特征来进行训练和预测。然后我根据特征的重要性，去掉表现最差的5个特征，并重复这个过程。在每次运行中，我会添加一行来记录预测的表现，这样我可以在后面进行一些分析。

原来的代码要复杂得多，我分析了不同的分类器和数据集，但我希望你能从上面的内容中明白我的意思。我注意到，对于随机森林算法来说，每次去掉的特征数量会影响性能，所以每次去掉1个、3个或5个特征，得到的最佳特征集合会有所不同。

我发现使用梯度提升分类器（GradientBoostingClassifier）更稳定，结果更一致，无论我每次去掉1个、3个还是5个特征，最终得到的最佳特征集合都是一样的。

希望我不是在教你一些你已经知道的东西，你可能比我更懂，但我进行特征分析的方法是先用一个快速的分类器大致了解最佳特征集合，然后再用一个表现更好的分类器，接着开始调整超参数，先进行粗略的比较，然后在我对最佳参数有了感觉后，再进行更细致的调整。

Answer 3

这是我为让RandomForestClassifier与RFECV配合使用所做的调整：

class RandomForestClassifierWithCoef(RandomForestClassifier):
    def fit(self, *args, **kwargs):
        super(RandomForestClassifierWithCoef, self).fit(*args, **kwargs)
        self.coef_ = self.feature_importances_

如果你使用'准确率'或'f1'分数，直接使用这个类就可以了。但如果用'roc_auc'，RFECV会抱怨说不支持多分类格式。通过下面的代码把它改成二分类，这样'roc_auc'评分就可以正常工作了。（我使用的是Python 3.4.1和scikit-learn 0.15.1）

y=(pd.Series(iris.target, name='target')==2).astype(int)

把它放到你的代码里：

from sklearn import datasets
import pandas as pd
from pandas import Series
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import RFECV

class RandomForestClassifierWithCoef(RandomForestClassifier):
    def fit(self, *args, **kwargs):
        super(RandomForestClassifierWithCoef, self).fit(*args, **kwargs)
        self.coef_ = self.feature_importances_

iris = datasets.load_iris()
x=pd.DataFrame(iris.data, columns=['var1','var2','var3', 'var4'])
y=(pd.Series(iris.target, name='target')==2).astype(int)
rf = RandomForestClassifierWithCoef(n_estimators=500, min_samples_leaf=5, n_jobs=-1)
rfecv = RFECV(estimator=rf, step=1, cv=2, scoring='roc_auc', verbose=2)
selector=rfecv.fit(x, y)

Answer 4

这是我想出来的方案。其实很简单，主要是用了一种自定义的准确率指标（叫做加权准确率），因为我处理的数据集不平衡。不过，如果需要的话，这个方案应该也能很容易地扩展。

from sklearn import datasets
import pandas
from sklearn.ensemble import RandomForestClassifier
from sklearn import cross_validation
from sklearn.metrics import confusion_matrix


def get_enhanced_confusion_matrix(actuals, predictions, labels):
    """"enhances confusion_matrix by adding sensivity and specificity metrics"""
    cm = confusion_matrix(actuals, predictions, labels = labels)
    sensitivity = float(cm[1][1]) / float(cm[1][0]+cm[1][1])
    specificity = float(cm[0][0]) / float(cm[0][0]+cm[0][1])
    weightedAccuracy = (sensitivity * 0.9) + (specificity * 0.1)
    return cm, sensitivity, specificity, weightedAccuracy

iris = datasets.load_iris()
x=pandas.DataFrame(iris.data, columns=['var1','var2','var3', 'var4'])
y=pandas.Series(iris.target, name='target')

response, _  = pandas.factorize(y)

xTrain, xTest, yTrain, yTest = cross_validation.train_test_split(x, response, test_size = .25, random_state = 36583)
print "building the first forest"
rf = RandomForestClassifier(n_estimators = 500, min_samples_split = 2, n_jobs = -1, verbose = 1)
rf.fit(xTrain, yTrain)
importances = pandas.DataFrame({'name':x.columns,'imp':rf.feature_importances_
                                }).sort(['imp'], ascending = False).reset_index(drop = True)

cm, sensitivity, specificity, weightedAccuracy = get_enhanced_confusion_matrix(yTest, rf.predict(xTest), [0,1])
numFeatures = len(x.columns)

rfeMatrix = pandas.DataFrame({'numFeatures':[numFeatures], 
                              'weightedAccuracy':[weightedAccuracy], 
                              'sensitivity':[sensitivity], 
                              'specificity':[specificity]})

print "running RFE on  %d features"%numFeatures

for i in range(1,numFeatures,1):
    varsUsed = importances['name'][0:i]
    print "now using %d of %s features"%(len(varsUsed), numFeatures)
    xTrain, xTest, yTrain, yTest = cross_validation.train_test_split(x[varsUsed], response, test_size = .25)
    rf = RandomForestClassifier(n_estimators = 500, min_samples_split = 2,
                                n_jobs = -1, verbose = 1)
    rf.fit(xTrain, yTrain)
    cm, sensitivity, specificity, weightedAccuracy = get_enhanced_confusion_matrix(yTest, rf.predict(xTest), [0,1])
    print("\n"+str(cm))
    print('the sensitivity is %d percent'%(sensitivity * 100))
    print('the specificity is %d percent'%(specificity * 100))
    print('the weighted accuracy is %d percent'%(weightedAccuracy * 100))
    rfeMatrix = rfeMatrix.append(
                                pandas.DataFrame({'numFeatures':[len(varsUsed)], 
                                'weightedAccuracy':[weightedAccuracy], 
                                'sensitivity':[sensitivity], 
                                'specificity':[specificity]}), ignore_index = True)    
print("\n"+str(rfeMatrix))    
maxAccuracy = rfeMatrix.weightedAccuracy.max()
maxAccuracyFeatures = min(rfeMatrix.numFeatures[rfeMatrix.weightedAccuracy == maxAccuracy])
featuresUsed = importances['name'][0:maxAccuracyFeatures].tolist()

print "the final features used are %s"%featuresUsed