我试图使用scikit-learn和随机森林分类器来预先形成递归特征，使用OOB ROC作为对递归过程中创建的每个子集进行评分的方法。
但是，当我尝试使用RFECV方法时，出现错误AttributeError: 'RandomForestClassifier' object has no attribute 'coef_'
随机森林本身没有系数，但他们确实按基尼评分排名。所以，我想知道如何解决这个问题。
请注意，我想使用一种方法，明确告诉我在最佳分组中选择了pandas DataFrame中的哪些功能，因为我使用递归功能选择来尝试最小化数据量输入最终的分类器。
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_'
答案 0 :(得分：21)
以下是我为使RandomForestClassifier与RFECV合作而采取的措施：
class RandomForestClassifierWithCoef(RandomForestClassifier):
def fit(self, *args, **kwargs):
super(RandomForestClassifierWithCoef, self).fit(*args, **kwargs)
self.coef_ = self.feature_importances_
如果您使用＆＃39;准确度＆＃39;只需使用此课程即可。或者＆＃39; f1＆＃39;得分了。对于＆＃39; roc_auc＆＃39;，RFECV抱怨不支持多类格式。使用下面的代码将其更改为两级分类，＆＃39; roc_auc＆＃39;得分工作。 （使用Python 3.4.1和scikit-learn 0.15.1）
y=(pd.Series(iris.target, name='target')==2).astype(int)
插入代码：
from sklearn import datasets
import pandas as pd
from pandas import Series
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import RFECV
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)
答案 1 :(得分：6)
这是我的代码，我已经整理了一下以使其与您的任务相关：
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
所以我在这里做的是我有一个我要训练然后预测的功能列表，使用功能重要性我然后修剪最差的5并重复。在每次运行期间，我都会添加一行来记录预测性能，以便稍后进行分析。
原始代码要大得多我有不同的分类器和数据集我正在分析，但我希望你从上面得到的图片。我注意到的是，对于随机森林，我在每次运行时删除的功能数量会影响性能，因此一次修剪1,3和5个功能会产生一组不同的最佳功能。
我发现使用GradientBoostingClassifer更具可预测性和可重复性，因为最终的一组最佳功能同意我是一次修剪1个特征还是3或5。
我希望我不是教你在这里吮吸鸡蛋，你可能比我更了解，但我对消融法的处理方法是使用快速分类器来大致了解最佳功能集，然后使用性能更好的分类器，然后开始超参数调整，再次做粗粒子comaprisons然后细细纹理一旦我感觉到最好的params是什么。
答案 2 :(得分：6)
我提交了添加coef_的请求，因此RandomForestClassifier可能与RFECV一起使用。但是，已经做出了改变。此更改将在版本0.17中。
https://github.com/scikit-learn/scikit-learn/issues/4945
如果您想立即使用它，可以提取最新的开发版本。
答案 3 :(得分：3)
这是我开胃的东西。这是一个非常简单的解决方案，并依赖于自定义精度指标（称为weightedAccuracy），因为我对高度不平衡的数据集进行了分类。但是，如果需要，它应该更容易扩展。
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